From c27bb8df05bf7df1e5bbbb338962ce594dde1f04 Mon Sep 17 00:00:00 2001
From: Chester Ismay <chester.ismay@gmail.com>
Date: Fri, 19 Jun 2026 07:09:46 -0700
Subject: [PATCH 1/9] docs: add example-rich getting-started, task guides,
 datasets, and R map

Expand the documentation from a single API page into an accessible, example-first
site (informed by top Python docs and the R moderndive pkgdown site):

- Rewritten landing page: value proposition, feature list, install (+ image
  extra), and a 30-second worked example.
- getting-started: an end-to-end first analysis.
- Task guides with runnable, verified examples (dual-engine plots throughout):
  sampling, bootstrapping & confidence intervals, hypothesis testing, regression
  (incl. per-facet coefficient shading and the model plots), theory-based
  inference, and a plotting/engines guide.
- datasets: a thematic tour of all 58 bundled datasets.
- coming-from-r: a function-by-function R -> Python map for moderndive/infer users.

Every code block was executed against the package to confirm it runs. Docs build
clean with -W (no warnings).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017CTL1QSTg1DmDUpqYuPEog
---
 doc/_build/html/.doctrees/api.doctree         | Bin 3757004 -> 3764428 bytes
 .../html/.doctrees/coming-from-r.doctree      | Bin 0 -> 32029 bytes
 doc/_build/html/.doctrees/datasets.doctree    | Bin 0 -> 19326 bytes
 doc/_build/html/.doctrees/environment.pickle  | Bin 3468520 -> 3498118 bytes
 .../html/.doctrees/getting-started.doctree    | Bin 0 -> 15230 bytes
 .../guides/confidence-intervals.doctree       | Bin 0 -> 10716 bytes
 .../guides/hypothesis-testing.doctree         | Bin 0 -> 15466 bytes
 .../html/.doctrees/guides/plotting.doctree    | Bin 0 -> 15736 bytes
 .../html/.doctrees/guides/regression.doctree  | Bin 0 -> 16985 bytes
 .../html/.doctrees/guides/sampling.doctree    | Bin 0 -> 11059 bytes
 .../.doctrees/guides/theory-based.doctree     | Bin 0 -> 11598 bytes
 doc/_build/html/.doctrees/index.doctree       | Bin 6785 -> 17546 bytes
 doc/_build/html/_modules/index.html           |  18 +-
 doc/_build/html/_sources/coming-from-r.md.txt |  75 +++
 doc/_build/html/_sources/datasets.md.txt      |  48 ++
 .../html/_sources/getting-started.md.txt      | 110 +++++
 .../guides/confidence-intervals.md.txt        |  88 ++++
 .../_sources/guides/hypothesis-testing.md.txt |  88 ++++
 .../html/_sources/guides/plotting.md.txt      | 113 +++++
 .../html/_sources/guides/regression.md.txt    | 111 +++++
 .../html/_sources/guides/sampling.md.txt      |  67 +++
 .../html/_sources/guides/theory-based.md.txt  |  79 +++
 doc/_build/html/_sources/index.md.txt         |  94 +++-
 doc/_build/html/api.html                      |  32 +-
 doc/_build/html/coming-from-r.html            | 464 ++++++++++++++++++
 doc/_build/html/datasets.html                 | 379 ++++++++++++++
 doc/_build/html/genindex.html                 |  18 +-
 doc/_build/html/getting-started.html          | 436 ++++++++++++++++
 .../html/guides/confidence-intervals.html     | 420 ++++++++++++++++
 .../html/guides/hypothesis-testing.html       | 417 ++++++++++++++++
 doc/_build/html/guides/plotting.html          | 446 +++++++++++++++++
 doc/_build/html/guides/regression.html        | 439 +++++++++++++++++
 doc/_build/html/guides/sampling.html          | 398 +++++++++++++++
 doc/_build/html/guides/theory-based.html      | 408 +++++++++++++++
 doc/_build/html/index.html                    | 143 ++++--
 doc/_build/html/objects.inv                   | Bin 655 -> 833 bytes
 doc/_build/html/py-modindex.html              |  18 +-
 doc/_build/html/search.html                   |  18 +-
 doc/_build/html/searchindex.js                |   2 +-
 doc/coming-from-r.md                          |  75 +++
 doc/datasets.md                               |  48 ++
 doc/getting-started.md                        | 110 +++++
 doc/guides/confidence-intervals.md            |  88 ++++
 doc/guides/hypothesis-testing.md              |  88 ++++
 doc/guides/plotting.md                        | 113 +++++
 doc/guides/regression.md                      | 111 +++++
 doc/guides/sampling.md                        |  67 +++
 doc/guides/theory-based.md                    |  79 +++
 doc/index.md                                  |  94 +++-
 49 files changed, 5716 insertions(+), 86 deletions(-)
 create mode 100644 doc/_build/html/.doctrees/coming-from-r.doctree
 create mode 100644 doc/_build/html/.doctrees/datasets.doctree
 create mode 100644 doc/_build/html/.doctrees/getting-started.doctree
 create mode 100644 doc/_build/html/.doctrees/guides/confidence-intervals.doctree
 create mode 100644 doc/_build/html/.doctrees/guides/hypothesis-testing.doctree
 create mode 100644 doc/_build/html/.doctrees/guides/plotting.doctree
 create mode 100644 doc/_build/html/.doctrees/guides/regression.doctree
 create mode 100644 doc/_build/html/.doctrees/guides/sampling.doctree
 create mode 100644 doc/_build/html/.doctrees/guides/theory-based.doctree
 create mode 100644 doc/_build/html/_sources/coming-from-r.md.txt
 create mode 100644 doc/_build/html/_sources/datasets.md.txt
 create mode 100644 doc/_build/html/_sources/getting-started.md.txt
 create mode 100644 doc/_build/html/_sources/guides/confidence-intervals.md.txt
 create mode 100644 doc/_build/html/_sources/guides/hypothesis-testing.md.txt
 create mode 100644 doc/_build/html/_sources/guides/plotting.md.txt
 create mode 100644 doc/_build/html/_sources/guides/regression.md.txt
 create mode 100644 doc/_build/html/_sources/guides/sampling.md.txt
 create mode 100644 doc/_build/html/_sources/guides/theory-based.md.txt
 create mode 100644 doc/_build/html/coming-from-r.html
 create mode 100644 doc/_build/html/datasets.html
 create mode 100644 doc/_build/html/getting-started.html
 create mode 100644 doc/_build/html/guides/confidence-intervals.html
 create mode 100644 doc/_build/html/guides/hypothesis-testing.html
 create mode 100644 doc/_build/html/guides/plotting.html
 create mode 100644 doc/_build/html/guides/regression.html
 create mode 100644 doc/_build/html/guides/sampling.html
 create mode 100644 doc/_build/html/guides/theory-based.html
 create mode 100644 doc/coming-from-r.md
 create mode 100644 doc/datasets.md
 create mode 100644 doc/getting-started.md
 create mode 100644 doc/guides/confidence-intervals.md
 create mode 100644 doc/guides/hypothesis-testing.md
 create mode 100644 doc/guides/plotting.md
 create mode 100644 doc/guides/regression.md
 create mode 100644 doc/guides/sampling.md
 create mode 100644 doc/guides/theory-based.md

diff --git a/doc/_build/html/.doctrees/api.doctree b/doc/_build/html/.doctrees/api.doctree
index 3ccb91913bf3a0869729cb2d49988dcba6a36c36..3419ad98beaf968dd4c7be7614da61f32240d3a9 100644
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diff --git a/doc/_build/html/_modules/index.html b/doc/_build/html/_modules/index.html
index a1ad52f..43f426b 100644
--- a/doc/_build/html/_modules/index.html
+++ b/doc/_build/html/_modules/index.html
@@ -194,9 +194,23 @@
   <input type="hidden" name="area" value="default">
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+  <p class="caption" role="heading"><span class="caption-text">Get started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../getting-started.html">Getting started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../coming-from-r.html">Coming from R</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../datasets.html">Datasets</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../guides/sampling.html">Sampling</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guides/confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guides/hypothesis-testing.html">Hypothesis testing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guides/regression.html">Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guides/theory-based.html">Theory-based inference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../guides/plotting.html">Plotting: plotly &amp; plotnine</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../index.html">moderndive (Python)</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../api.html">API reference</a></li>
 </ul>
 
diff --git a/doc/_build/html/_sources/coming-from-r.md.txt b/doc/_build/html/_sources/coming-from-r.md.txt
new file mode 100644
index 0000000..fca9f7f
--- /dev/null
+++ b/doc/_build/html/_sources/coming-from-r.md.txt
@@ -0,0 +1,75 @@
+# Coming from R
+
+If you know the R `moderndive` and `infer` packages, this page maps the API to
+Python. The grammar is the same; the main differences are Python method-chaining
+(`.hypothesize()` instead of the `|>`/`%>%` pipe) and polars DataFrames.
+
+## The infer pipeline
+
+```r
+# R
+mtcars %>%
+  specify(response = mpg) %>%
+  hypothesize(null = "point", mu = 20) %>%
+  generate(reps = 1000, type = "bootstrap") %>%
+  calculate(stat = "mean")
+```
+
+```python
+# Python — verbs are methods on the returned objects
+(
+    specify(data, response="mpg")
+    .hypothesize(null="point", mu=20)
+    .generate(reps=1000, type="bootstrap", seed=1)
+    .calculate(stat="mean")
+)
+```
+
+`specify(formula="y ~ x")` works just like R's formula interface; `success=` marks
+the success level for categorical responses.
+
+## Function map
+
+| R (`infer` / `moderndive`)        | Python (`moderndive`)                                  |
+| --------------------------------- | ------------------------------------------------------ |
+| `specify()`                       | `specify()`                                            |
+| `hypothesize()` / `hypothesise()` | `.hypothesize()` / `.hypothesise()`                    |
+| `generate()`                      | `.generate()`                                          |
+| `calculate()`                     | `.calculate()`                                         |
+| `fit()`                           | `.fit()`                                               |
+| `assume()`                        | `assume()`                                             |
+| `observe()`                       | `observe()`                                            |
+| `get_p_value()` / `get_pvalue()`  | `get_p_value()` / `get_pvalue()`                       |
+| `get_confidence_interval()` / `get_ci()` | `get_confidence_interval()` / `get_ci()`        |
+| `visualize()` / `visualise()`     | `visualize()` / `visualise()`                          |
+| `shade_p_value()` / `shade_pvalue()` | `shade_p_value()` / `shade_pvalue()`                |
+| `shade_confidence_interval()` / `shade_ci()` | `shade_confidence_interval()` / `shade_ci()` |
+| `t_test()`, `prop_test()`, `chisq_test()` | same names                                     |
+| `t_stat()`, `chisq_stat()`        | same names                                             |
+| `rep_sample_n()` / `rep_slice_sample()` | `rep_sample_n()` / `rep_slice_sample()`          |
+| `get_regression_table()`          | `get_regression_table(model)`                          |
+| `get_regression_points()`         | `get_regression_points(model)`                         |
+| `get_regression_summaries()`      | `get_regression_summaries(model)`                      |
+| `get_correlation()`               | `get_correlation(data, "y ~ x")`                       |
+| `pop_sd()`                        | `pop_sd()`                                             |
+| `geom_parallel_slopes()`          | `geom_parallel_slopes()` (plotnine) / `gg_parallel_slopes(engine=...)` |
+| `geom_categorical_model()`        | `gg_categorical_model(engine=...)`                     |
+| `tidy_summary()`                  | `tidy_summary()`                                       |
+
+## Key differences
+
+- **Pipe → methods.** `x %>% hypothesize(...)` becomes `x.hypothesize(...)`.
+- **Models.** Where R passes an `lm()` object, Python passes a fitted
+  [statsmodels](https://www.statsmodels.org) model:
+  `smf.ols("y ~ x", data=df.to_pandas()).fit()`.
+- **Plotting engine.** R returns ggplot2; Python defaults to **plotly**
+  (interactive) with `engine="plotnine"` available everywhere. Plots compose with
+  `+` in both engines.
+- **DataFrames.** Inputs/outputs are polars; pass `.to_pandas()` when a downstream
+  tool needs pandas.
+- **Reproducibility.** Pass `seed=` to `generate()` (R uses `set.seed()`).
+
+## Same datasets
+
+Most R `moderndive`/`infer` datasets are bundled here under the same name —
+`load_promotions()`, `load_pennies()`, `load_gss()`, etc. See {doc}`datasets`.
diff --git a/doc/_build/html/_sources/datasets.md.txt b/doc/_build/html/_sources/datasets.md.txt
new file mode 100644
index 0000000..5b8c693
--- /dev/null
+++ b/doc/_build/html/_sources/datasets.md.txt
@@ -0,0 +1,48 @@
+# Datasets
+
+`moderndive` bundles 58 datasets (the R `moderndive` + `infer` data, plus a few
+derived tables). Each loads with `load_<name>()` and returns a polars DataFrame.
+
+```python
+import moderndive as md
+
+md.available_datasets()          # sorted list of every dataset name
+md.load_dataset("promotions")    # load by name (string)
+md.load_promotions()             # or via the generated loader
+```
+
+## By topic
+
+**Sampling & simulation**
+: `bowl`, `bowl_sample_1`, `bowl_samples`, `tactile_prop_red`,
+  `almonds_bowl`, `almonds_sample`, `almonds_sample_100`,
+  `pennies`, `pennies_sample`, `pennies_resamples`, `orig_pennies_sample`
+
+**Hypothesis testing & CIs**
+: `promotions`, `promotions_shuffled`, `mythbusters_yawn`, `movies_sample`,
+  `spotify_by_genre`, `spotify_52_original`, `spotify_52_shuffled`,
+  `spotify_metal_deephouse`, `offshore`, `age_at_marriage`, `zinc_tidy`,
+  `cle_sac`, `gss`
+
+**Regression**
+: `evals`, `saratoga_houses`, `house_prices`, `coffee_quality`, `coffee_ratings`,
+  `credit`, `MA_schools`, `un_member_states_2024`, `mario_kart_auction`,
+  `amazon_books`, `old_faithful_2024`
+
+**Flights & weather (nycflights23 / 13)**
+: `flights`, `weather`, `airlines`, `airports`, `planes`, `envoy_flights`,
+  `alaska_flights`, `early_january_weather`, `early_january_2023_weather`
+
+**Other example data**
+: `gapminder`, `gapminder_2007`, `drinks`, `airline_safety`, `dem_score`,
+  `us_births_1994_2003`, `steves_episodes`, `avocados`, `babies`, `ipf_lifts`,
+  `ev_charging`, `DD_vs_SB`, `ma_traffic_2020_vs_2019`, `mass_traffic_2020`
+
+## Tips
+
+- Every loader returns a polars DataFrame; call `.to_pandas()` if a function
+  (e.g. statsmodels) expects pandas.
+- Datetime columns (e.g. `time_hour` in the weather/flights data) are stored in
+  UTC.
+- `load_dataset("<name>")` raises a helpful `ValueError` listing valid names if
+  the dataset doesn't exist.
diff --git a/doc/_build/html/_sources/getting-started.md.txt b/doc/_build/html/_sources/getting-started.md.txt
new file mode 100644
index 0000000..2453b27
--- /dev/null
+++ b/doc/_build/html/_sources/getting-started.md.txt
@@ -0,0 +1,110 @@
+# Getting started
+
+This page walks through a complete analysis end to end, then points you at the
+task guides for more depth.
+
+## Install
+
+```bash
+pip install moderndive
+```
+
+`moderndive` returns [polars](https://pola.rs) DataFrames, but every function also
+accepts pandas DataFrames as input.
+
+## Load a dataset
+
+All datasets ship with the package and load with `load_<name>()`:
+
+```python
+import moderndive as md
+
+promotions = md.load_promotions()
+promotions.head()
+```
+
+```text
+shape: (5, 3)
+┌─────┬──────────┬────────┐
+│ id  ┆ decision ┆ gender │
+│ i64 ┆ str      ┆ str    │
+╞═════╪══════════╪════════╡
+│ 1   ┆ promoted ┆ male   │
+│ 2   ┆ promoted ┆ male   │
+│ …   ┆ …        ┆ …      │
+└─────┴──────────┴────────┘
+```
+
+List everything that's available with `md.available_datasets()` (58 datasets), and
+see {doc}`datasets` for a thematic tour.
+
+## A first summary
+
+`tidy_summary` gives a per-variable five-number summary (numeric) or counts
+(categorical):
+
+```python
+from moderndive import tidy_summary
+
+tidy_summary(md.load_almonds_sample_100(), columns=["weight"])
+```
+
+## The inference pipeline
+
+The core grammar mirrors R `infer`. You build a pipeline and read it like a
+sentence:
+
+```python
+from moderndive import specify, observe, get_p_value
+
+# 1. The observed statistic
+obs = observe(
+    promotions, formula="decision ~ gender", success="promoted",
+    stat="diff in props", order=("male", "female"),
+)
+
+# 2. A null distribution: specify → hypothesize → generate → calculate
+null = (
+    specify(promotions, formula="decision ~ gender", success="promoted")
+    .hypothesize(null="independence")
+    .generate(reps=1000, type="permute", seed=42)
+    .calculate(stat="diff in props", order=("male", "female"))
+)
+
+# 3. Summarize
+get_p_value(null, obs_stat=obs, direction="right")   # ≈ 0.025
+```
+
+Each verb has a focused guide: {doc}`guides/sampling`,
+{doc}`guides/confidence-intervals`, and {doc}`guides/hypothesis-testing`.
+
+## Visualizing — choose your engine
+
+Plots default to **plotly** (interactive). Pass `engine="plotnine"` for
+grammar-of-graphics output. The composition syntax is identical:
+
+```python
+from moderndive import visualize, shade_p_value
+
+# Interactive plotly figure
+visualize(null) + shade_p_value(obs_stat=obs, direction="right")
+
+# Same plot, plotnine
+visualize(null, engine="plotnine") + shade_p_value(obs_stat=obs, direction="right")
+```
+
+See {doc}`guides/plotting` for shading, confidence-interval overlays, theoretical
+overlays, and the regression-model plots.
+
+## Regression
+
+```python
+import statsmodels.formula.api as smf
+from moderndive import get_regression_table
+
+houses = md.load_saratoga_houses()
+model = smf.ols("price ~ living_area + bedrooms", data=houses.to_pandas()).fit()
+get_regression_table(model)
+```
+
+Full details in {doc}`guides/regression`.
diff --git a/doc/_build/html/_sources/guides/confidence-intervals.md.txt b/doc/_build/html/_sources/guides/confidence-intervals.md.txt
new file mode 100644
index 0000000..e02676e
--- /dev/null
+++ b/doc/_build/html/_sources/guides/confidence-intervals.md.txt
@@ -0,0 +1,88 @@
+# Bootstrapping & confidence intervals
+
+Resample your data with replacement to build a **bootstrap distribution**, then
+read a confidence interval off it.
+
+## A bootstrap distribution for a mean
+
+```python
+import moderndive as md
+from moderndive import specify, get_confidence_interval, visualize, shade_confidence_interval
+
+almonds = md.load_almonds_sample_100()
+
+boot = (
+    specify(almonds, response="weight")
+    .generate(reps=1000, type="bootstrap", seed=1)
+    .calculate(stat="mean")
+)
+```
+
+## Confidence intervals — three methods
+
+```python
+# Percentile method (default)
+get_confidence_interval(boot, level=0.95, type="percentile")
+# shape: (1, 2) → lower_ci ≈ 3.61, upper_ci ≈ 3.75
+
+# Standard-error method (needs the point estimate)
+point = float(specify(almonds, response="weight").calculate(stat="mean"))
+get_confidence_interval(boot, level=0.95, type="se", point_estimate=point)
+
+# Bias-corrected
+get_confidence_interval(boot, level=0.95, type="bias-corrected", point_estimate=point)
+```
+
+Distribution objects also expose the getter as a method:
+
+```python
+boot.get_confidence_interval(level=0.95, type="percentile")
+```
+
+## Visualize the interval
+
+```python
+ci = get_confidence_interval(boot, level=0.95, type="percentile")
+
+# plotly (default) — interactive
+visualize(boot) + shade_confidence_interval(ci)
+
+# plotnine
+visualize(boot, engine="plotnine") + shade_confidence_interval(ci)
+
+# one-call keyword form
+visualize(boot, shade_ci=ci)
+```
+
+## A confidence interval for a proportion
+
+```python
+import polars as pl
+
+mythbusters = md.load_mythbusters_yawn()
+
+boot_prop = (
+    specify(mythbusters, response="yawn", success="yes")
+    .generate(reps=1000, type="bootstrap", seed=1)
+    .calculate(stat="prop")
+)
+get_confidence_interval(boot_prop, level=0.95, type="percentile")
+```
+
+## A confidence interval for a difference
+
+Add an explanatory variable and an `order=` to compare two groups:
+
+```python
+boot_diff = (
+    specify(mythbusters, formula="yawn ~ group", success="yes")
+    .generate(reps=1000, type="bootstrap", seed=1)
+    .calculate(stat="diff in props", order=("seed", "control"))
+)
+boot_diff.get_confidence_interval(level=0.95)
+```
+
+```{seealso}
+For *theory-based* intervals (the formula-based `t` interval) see
+{doc}`theory-based`.
+```
diff --git a/doc/_build/html/_sources/guides/hypothesis-testing.md.txt b/doc/_build/html/_sources/guides/hypothesis-testing.md.txt
new file mode 100644
index 0000000..3a0d8c2
--- /dev/null
+++ b/doc/_build/html/_sources/guides/hypothesis-testing.md.txt
@@ -0,0 +1,88 @@
+# Hypothesis testing
+
+Hypothesis tests follow the same grammar as confidence intervals, with an added
+`hypothesize()` step that defines the null world, and `generate()` that simulates
+from it.
+
+## Two groups: a permutation test
+
+The classic "promotions" experiment — does the (perceived) gender on a résumé
+change the promotion rate?
+
+```python
+import moderndive as md
+from moderndive import specify, observe, get_p_value, visualize, shade_p_value
+
+promotions = md.load_promotions()
+
+# Observed difference in promotion proportions, male − female
+obs = observe(
+    promotions, formula="decision ~ gender", success="promoted",
+    stat="diff in props", order=("male", "female"),
+)   # ≈ 0.292
+
+# Null: gender is independent of the decision → permute the labels
+null = (
+    specify(promotions, formula="decision ~ gender", success="promoted")
+    .hypothesize(null="independence")
+    .generate(reps=1000, type="permute", seed=42)
+    .calculate(stat="diff in props", order=("male", "female"))
+)
+
+get_p_value(null, obs_stat=obs, direction="right")   # ≈ 0.025
+```
+
+## Shade the p-value
+
+```python
+visualize(null) + shade_p_value(obs_stat=obs, direction="right")
+```
+
+`direction` is one of `"right"`/`"greater"`, `"left"`/`"less"`, or `"two-sided"`.
+The two-sided shading mirrors the observed statistic about 0.
+
+## One mean / one proportion (point null)
+
+Use a `"point"` null with bootstrap resampling, supplying the hypothesized value:
+
+```python
+age = md.load_age_at_marriage()
+
+obs_t = observe(age, response="age", stat="t", null="point", mu=23)
+
+null_t = (
+    specify(age, response="age")
+    .hypothesize(null="point", mu=23)
+    .generate(reps=1000, type="bootstrap", seed=1)
+    .calculate(stat="t")
+)
+get_p_value(null_t, obs_stat=obs_t, direction="two-sided")
+```
+
+For a one-proportion test you can also *simulate* draws directly:
+
+```python
+import polars as pl
+
+coins = pl.DataFrame({"flip": ["heads"] * 30 + ["tails"] * 70})
+
+null_p = (
+    specify(coins, response="flip", success="heads")
+    .hypothesize(null="point", p=0.5)
+    .generate(reps=1000, type="draw", seed=1)   # "simulate" is an alias
+    .calculate(stat="prop")
+)
+```
+
+## Available statistics
+
+`calculate(stat=...)` supports the full infer vocabulary: `"mean"`, `"median"`,
+`"sum"`, `"sd"`, `"prop"`, `"count"`, `"diff in means"`, `"diff in medians"`,
+`"diff in props"`, `"ratio of means"`, `"ratio of props"`, `"odds ratio"`,
+`"slope"`, `"correlation"`, `"t"`, `"z"`, `"F"`, `"Chisq"`, plus any custom
+callable `stat(response, explanatory) -> float`.
+
+```{seealso}
+Prefer a one-line classical test? See the tidy wrappers in {doc}`theory-based`
+(`t_test`, `prop_test`, `chisq_test`).
+```
diff --git a/doc/_build/html/_sources/guides/plotting.md.txt b/doc/_build/html/_sources/guides/plotting.md.txt
new file mode 100644
index 0000000..1d5cf94
--- /dev/null
+++ b/doc/_build/html/_sources/guides/plotting.md.txt
@@ -0,0 +1,113 @@
+# Plotting: plotly & plotnine
+
+Every plotting function in `moderndive` takes an `engine=` argument:
+
+- `engine="plotly"` (**default**) — interactive figures (`plotly.graph_objects.Figure`).
+- `engine="plotnine"` — grammar-of-graphics figures (`plotnine.ggplot`).
+
+The composition syntax is identical across engines, so you can switch a whole
+analysis by changing one argument.
+
+## visualize() returns an InferPlot
+
+`visualize()` returns a small `InferPlot` wrapper that you compose with shading via
+`+` in **both** engines:
+
+```python
+import moderndive as md
+from moderndive import specify, get_confidence_interval, visualize, shade_confidence_interval
+
+boot = (
+    specify(md.load_almonds_sample_100(), response="weight")
+    .generate(reps=1000, type="bootstrap", seed=1)
+    .calculate(stat="mean")
+)
+ci = get_confidence_interval(boot, level=0.95, type="percentile")
+
+p = visualize(boot) + shade_confidence_interval(ci)   # plotly InferPlot
+p.figure          # the underlying plotly Figure
+p.show()          # display it
+```
+
+For the plotnine engine, the raw `ggplot` is available via `.gg`:
+
+```python
+g = visualize(boot, engine="plotnine") + shade_confidence_interval(ci)
+g.gg              # the underlying plotnine ggplot
+```
+
+### Keyword form
+
+If you'd rather not use `+`, pass the shading inline (handy for plotly):
+
+```python
+visualize(boot, shade_ci=ci)
+visualize(null, shade_pvalue={"obs_stat": obs, "direction": "right"})
+```
+
+## Shading p-values
+
+```python
+from moderndive import observe, shade_p_value
+
+promotions = md.load_promotions()
+obs = observe(promotions, formula="decision ~ gender", success="promoted",
+              stat="diff in props", order=("male", "female"))
+null = (
+    specify(promotions, formula="decision ~ gender", success="promoted")
+    .hypothesize(null="independence")
+    .generate(reps=1000, type="permute", seed=42)
+    .calculate(stat="diff in props", order=("male", "female"))
+)
+
+visualize(null) + shade_p_value(obs_stat=obs, direction="right")
+```
+
+## Simulation vs. theory overlays
+
+```python
+visualize(boot, method="simulation")    # histogram (default)
+visualize(boot, method="theoretical")   # normal-approximation curve
+visualize(boot, method="both")          # histogram + curve overlaid
+```
+
+## Faceted regression-coefficient plots
+
+`visualize_fit` shows one panel per term, and shading is **per-facet**:
+
+```python
+from moderndive.infer.viz import visualize_fit
+
+f = "price ~ living_area + bedrooms"
+houses = md.load_saratoga_houses()
+boot_fit = specify(houses, formula=f).generate(reps=1000, type="bootstrap", seed=1).fit()
+
+visualize_fit(boot_fit) + shade_confidence_interval(boot_fit.get_confidence_interval())
+```
+
+## Model plots
+
+```python
+from moderndive import gg_parallel_slopes, gg_categorical_model, pairplot
+
+evals = md.load_evals()
+gg_parallel_slopes(evals, response="score", explanatory="age", by="gender")
+gg_categorical_model(evals, response="score", explanatory="rank")
+
+# Scatterplot matrix (~ GGally::ggpairs)
+pairplot(md.load_coffee_quality(), columns=["total_cup_points", "aroma", "flavor"])
+```
+
+## Saving figures
+
+```python
+p = visualize(boot)            # plotly
+p.save("dist.html")            # interactive HTML (no extra deps)
+p.save("dist.png")             # static image — needs: pip install "moderndive[image]"
+
+g = visualize(boot, engine="plotnine")
+g.save("dist.png", width=6, height=4, dpi=150)
+```
+
+`pairplot(..., engine="seaborn")` (alias `"plotnine"`) returns a Matplotlib figure
+if you prefer the seaborn-backed scatterplot matrix.
diff --git a/doc/_build/html/_sources/guides/regression.md.txt b/doc/_build/html/_sources/guides/regression.md.txt
new file mode 100644
index 0000000..b89f2b1
--- /dev/null
+++ b/doc/_build/html/_sources/guides/regression.md.txt
@@ -0,0 +1,111 @@
+# Regression
+
+The regression helpers turn a fitted [statsmodels](https://www.statsmodels.org)
+model into tidy polars tables — the analog of R `moderndive`'s
+`get_regression_table()` / `get_regression_points()` / `get_regression_summaries()`.
+
+Fit models with statsmodels' formula API, then tidy them:
+
+```python
+import statsmodels.formula.api as smf
+import moderndive as md
+from moderndive import (
+    get_regression_table, get_regression_points, get_regression_summaries, get_correlation,
+)
+
+houses = md.load_saratoga_houses()
+model = smf.ols("price ~ living_area + bedrooms", data=houses.to_pandas()).fit()
+```
+
+## Coefficient table (with confidence intervals)
+
+```python
+get_regression_table(model)
+```
+
+```text
+shape: (3, 7)
+┌─────────────┬───────────┬───────────┬───────────┬─────────┬────────────┬───────────┐
+│ term        ┆ estimate  ┆ std_error ┆ statistic ┆ p_value ┆ lower_ci   ┆ upper_ci  │
+╞═════════════╪═══════════╪═══════════╪═══════════╪═════════╪════════════╪═══════════╡
+│ intercept   ┆ 20986.094 ┆ 6816.251  ┆ 3.079     ┆ 0.002   ┆ 7611.128   ┆ 34361.06  │
+│ living_area ┆ 93.842    ┆ 3.109     ┆ 30.183    ┆ 0.0     ┆ 87.741     ┆ 99.943    │
+│ bedrooms    ┆ -7483.095 ┆ 2783.531  ┆ -2.688    ┆ 0.007   ┆ -12944.988 ┆ -2021.203 │
+└─────────────┴───────────┴───────────┴───────────┴─────────┴────────────┴───────────┘
+```
+
+Change the confidence level with `conf_level=` (e.g. `0.99`).
+
+## Fitted values & residuals
+
+```python
+get_regression_points(model).head()
+# columns: ID, price, living_area, bedrooms, price_hat, residual
+```
+
+## Model-fit summaries
+
+```python
+get_regression_summaries(model)
+# r_squared, adj_r_squared, mse, rmse, sigma, statistic (F), p_value, df, nobs
+```
+
+## Correlation
+
+```python
+get_correlation(houses, "price ~ living_area")   # ≈ 0.759
+# or: get_correlation(houses, x="living_area", y="price")
+```
+
+## Visualizing models
+
+Two ports of R `moderndive`'s ggplot helpers, both dual-engine:
+
+```python
+from moderndive import gg_parallel_slopes, gg_categorical_model
+
+evals = md.load_evals()
+
+# Parallel-slopes model: one common slope, a separate intercept per group
+gg_parallel_slopes(evals, response="score", explanatory="age", by="gender")            # plotly
+gg_parallel_slopes(evals, response="score", explanatory="age", by="gender",
+                   engine="plotnine")
+
+# Regression with a single categorical predictor
+gg_categorical_model(evals, response="score", explanatory="rank")
+```
+
+For the plotnine engine you can also drop the parallel-slopes lines onto your own
+`ggplot` with `geom_parallel_slopes()`.
+
+## Inference for regression coefficients
+
+`fit()` runs the regression on every bootstrap/permutation replicate, giving a
+distribution per coefficient. Pair it with `visualize_fit` and **per-facet**
+shading:
+
+```python
+from moderndive import specify
+from moderndive.infer.viz import visualize_fit
+from moderndive import shade_confidence_interval, shade_p_value
+
+f = "price ~ living_area + bedrooms"
+obs_fit = specify(houses, formula=f).fit()
+
+# Bootstrap distribution of each coefficient → per-term CIs
+boot_fit = specify(houses, formula=f).generate(reps=1000, type="bootstrap", seed=1).fit()
+boot_fit.get_confidence_interval(level=0.95)          # one row per term
+visualize_fit(boot_fit) + shade_confidence_interval(boot_fit.get_confidence_interval())
+
+# Null distribution → per-term p-values, each facet shaded at its own estimate
+null_fit = (
+    specify(houses, formula=f).hypothesize(null="independence")
+    .generate(reps=1000, type="permute", seed=1).fit()
+)
+null_fit.get_p_value(obs_stat=obs_fit, direction="two-sided")
+visualize_fit(null_fit) + shade_p_value(obs_stat=obs_fit, direction="two-sided")
+```
+
+Per-facet shading works in both engines: `shade_p_value` / `shade_confidence_interval`
+accept the observed `FitResult` or a term-keyed table, and each facet is shaded
+from its own value.
diff --git a/doc/_build/html/_sources/guides/sampling.md.txt b/doc/_build/html/_sources/guides/sampling.md.txt
new file mode 100644
index 0000000..cb7a7bc
--- /dev/null
+++ b/doc/_build/html/_sources/guides/sampling.md.txt
@@ -0,0 +1,67 @@
+# Sampling
+
+Sampling activities (the "bowl" of balls, tactile samples) are how ModernDive
+builds intuition for sampling variation. `rep_slice_sample` takes repeated samples
+and stacks them with a `replicate` column — the analog of R
+`moderndive::rep_slice_sample()` / `infer::rep_sample_n()`.
+
+## The bowl
+
+```python
+import moderndive as md
+import polars as pl
+
+bowl = md.load_bowl()      # 2400 red/white balls
+bowl.head()
+```
+
+## One virtual sample
+
+```python
+from moderndive import rep_slice_sample
+
+sample = rep_slice_sample(bowl, n=50, seed=1)
+# proportion red in this sample
+sample.select((pl.col("color") == "red").mean().alias("prop_red"))
+```
+
+## Many samples → a sampling distribution
+
+Take 1000 samples of size 50 and compute the proportion red in each:
+
+```python
+samples = rep_slice_sample(bowl, n=50, reps=1000, seed=1)
+
+prop_red = (
+    samples
+    .group_by("replicate")
+    .agg((pl.col("color") == "red").mean().alias("prop_red"))
+)
+prop_red.head()
+```
+
+That `prop_red` column is a **sampling distribution**. Visualize its spread the
+same way you would any distribution (see {doc}`confidence-intervals` for building
+one with the inference pipeline instead).
+
+## With vs. without replacement
+
+`rep_slice_sample` samples **without** replacement by default (like dealing from a
+deck). Pass `replace=True` for bootstrap-style resampling:
+
+```python
+rep_slice_sample(bowl, n=50, reps=1000, replace=True, seed=1)
+```
+
+## Tactile samples
+
+The hand-collected counterparts are bundled too:
+
+```python
+md.load_tactile_prop_red()   # 33 groups' samples of 50 balls
+```
+
+```{seealso}
+`rep_sample_n` is an alias of `rep_slice_sample` (the older infer name). Both are
+documented in the {doc}`../api`.
+```
diff --git a/doc/_build/html/_sources/guides/theory-based.md.txt b/doc/_build/html/_sources/guides/theory-based.md.txt
new file mode 100644
index 0000000..3bb59b0
--- /dev/null
+++ b/doc/_build/html/_sources/guides/theory-based.md.txt
@@ -0,0 +1,79 @@
+# Theory-based inference
+
+Alongside the simulation grammar, `moderndive` ships tidy wrappers for the
+classical (formula-based) tests and a way to draw the theoretical distributions —
+mirroring R `infer`'s `t_test`, `prop_test`, `chisq_test`, and `assume()`.
+
+## One-line tests
+
+```python
+import moderndive as md
+from moderndive import t_test, prop_test, chisq_test
+
+# One-sample t-test
+age = md.load_age_at_marriage()
+t_test(age, response="age", mu=23)
+# → statistic, t_df, p_value, alternative, estimate, lower_ci, upper_ci
+
+# Two-sample (Welch) t-test
+movies = md.load_movies_sample()
+t_test(movies, formula="rating ~ genre", order=("Action", "Romance"))
+
+# Two-proportion z-test
+yawn = md.load_mythbusters_yawn()
+prop_test(yawn, formula="yawn ~ group", success="yes", order=("seed", "control"))
+
+# Chi-squared test of independence
+chisq_test(yawn, formula="yawn ~ group")
+# → statistic, chisq_df, p_value
+```
+
+`t_stat` and `chisq_stat` return just the test statistic if that's all you need.
+
+## Theoretical distributions with `assume()`
+
+`assume()` defines a theoretical sampling distribution you can visualize and use
+for p-values without simulating:
+
+```python
+from moderndive import assume
+
+# t-distribution with 10 degrees of freedom
+t_dist = assume("t", df=10)
+t_dist.get_p_value(2.0, direction="right")     # one-sided p-value
+
+# plotly by default; engine="plotnine" for ggplot output
+t_dist.visualize()
+t_dist.visualize(engine="plotnine")
+```
+
+Supported distributions: `"t"`, `"z"`, `"F"` (pass `df=(df1, df2)`), and
+`"Chisq"`.
+
+## Overlaying theory on a simulation
+
+`visualize(..., method=...)` can show the simulation histogram, the theoretical
+curve, or both:
+
+```python
+from moderndive import specify, visualize
+
+boot = (
+    specify(age, response="age")
+    .generate(reps=1000, type="bootstrap", seed=1)
+    .calculate(stat="mean")
+)
+
+visualize(boot, method="both")          # histogram + normal-approximation curve
+visualize(boot, method="theoretical")   # just the curve
+```
+
+## Population standard deviation
+
+A small helper that divides by `n` (not `n − 1`):
+
+```python
+from moderndive import pop_sd
+
+pop_sd([1, 2, 3, 4, 5])   # → 1.414…
+```
diff --git a/doc/_build/html/_sources/index.md.txt b/doc/_build/html/_sources/index.md.txt
index 43ff464..516de35 100644
--- a/doc/_build/html/_sources/index.md.txt
+++ b/doc/_build/html/_sources/index.md.txt
@@ -1,16 +1,25 @@
 # moderndive (Python)
 
 The Python companion package for **ModernDive: Statistical Inference via Data
-Science** — a faithful port of the R `moderndive` and `infer` packages to a
-modern Python data-science stack (polars, plotnine, statsmodels).
+Science** — a faithful port of the R [`moderndive`](https://moderndive.github.io/moderndive/)
+and [`infer`](https://infer.tidymodels.org) packages to a modern Python
+data-science stack ([polars](https://pola.rs), [plotly](https://plotly.com/python/),
+[plotnine](https://plotnine.org), [statsmodels](https://www.statsmodels.org)).
 
-```{toctree}
-:maxdepth: 2
-:caption: Contents
+If you teach or learn statistical inference the *tidy* way — `specify` →
+`hypothesize` → `generate` → `calculate` → `visualize` — this package gives you
+the same grammar in Python, plus regression helpers and the book's datasets.
 
-self
-api
-```
+## Why moderndive?
+
+- **A tidy inference grammar.** Bootstrap confidence intervals and
+  permutation/​simulation hypothesis tests read like sentences, mirroring R `infer`.
+- **Regression helpers** that return tidy tables: `get_regression_table`,
+  `get_regression_points`, `get_regression_summaries`, `get_correlation`.
+- **Dual-engine plots.** Every plot takes `engine="plotly"` (default, interactive)
+  or `engine="plotnine"` (grammar-of-graphics) — same code, your choice of output.
+- **58 bundled datasets** via `load_*()` loaders returning polars DataFrames.
+- **polars-first**, works with pandas too.
 
 ## Installation
 
@@ -18,24 +27,73 @@ api
 pip install moderndive
 ```
 
-## Quick start
+Optional extra for saving plotly figures as static images (PNG/SVG):
+
+```bash
+pip install "moderndive[image]"   # adds kaleido
+```
+
+## 30-second example
+
+Did promotion decisions depend on the applicant's (perceived) gender? Shuffle the
+labels 1000 times to build a null distribution and read off a p-value:
 
 ```python
 import moderndive as md
-from moderndive import specify, get_p_value, visualize, shade_p_value
+from moderndive import specify, observe, get_p_value, visualize, shade_p_value
 
-spotify = md.load_spotify_metal_deephouse()
+promotions = md.load_promotions()
 
-obs = specify(spotify, formula="popular_or_not ~ track_genre", success="popular") \
-    .calculate(stat="diff in props", order=("metal", "deep-house"))
+# Observed difference in promotion rates (male − female)
+obs = observe(
+    promotions, formula="decision ~ gender", success="promoted",
+    stat="diff in props", order=("male", "female"),
+)
 
+# Null distribution under "gender doesn't matter" (permutation)
 null = (
-    specify(spotify, formula="popular_or_not ~ track_genre", success="popular")
+    specify(promotions, formula="decision ~ gender", success="promoted")
     .hypothesize(null="independence")
-    .generate(reps=1000, type="permute", seed=76)
-    .calculate(stat="diff in props", order=("metal", "deep-house"))
+    .generate(reps=1000, type="permute", seed=42)
+    .calculate(stat="diff in props", order=("male", "female"))
 )
-get_p_value(null, obs_stat=obs, direction="right")
+
+get_p_value(null, obs_stat=obs, direction="right")   # ≈ 0.025
+
+# Visualize it (interactive plotly by default; engine="plotnine" also works)
+visualize(null) + shade_p_value(obs_stat=obs, direction="right")
+```
+
+## Where to next
+
+- New here? Start with {doc}`getting-started`.
+- Coming from R? See {doc}`coming-from-r` for a function-by-function map.
+- Browse the task guides below, or jump to the {doc}`api`.
+
+```{toctree}
+:maxdepth: 1
+:caption: Get started
+
+getting-started
+coming-from-r
+datasets
+```
+
+```{toctree}
+:maxdepth: 1
+:caption: Guides
+
+guides/sampling
+guides/confidence-intervals
+guides/hypothesis-testing
+guides/regression
+guides/theory-based
+guides/plotting
 ```
 
-See the {doc}`api` for the full reference.
+```{toctree}
+:maxdepth: 2
+:caption: Reference
+
+api
+```
diff --git a/doc/_build/html/api.html b/doc/_build/html/api.html
index ceb0340..15e032d 100644
--- a/doc/_build/html/api.html
+++ b/doc/_build/html/api.html
@@ -3,7 +3,7 @@
   <head><meta charset="utf-8">
     <meta name="viewport" content="width=device-width,initial-scale=1">
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
-<link rel="index" title="Index" href="genindex.html"><link rel="search" title="Search" href="search.html"><link rel="prev" title="moderndive (Python)" href="index.html">
+<link rel="index" title="Index" href="genindex.html"><link rel="search" title="Search" href="search.html"><link rel="prev" title="Plotting: plotly &amp; plotnine" href="guides/plotting.html">
         <link rel="prefetch" href="_static/moderndive-logo.png" as="image">
 
     <link rel="shortcut icon" href="_static/moderndive-logo.png"><!-- Generated with Sphinx 9.1.0 and Furo 2025.12.19 -->
@@ -195,9 +195,23 @@
   <input type="hidden" name="area" value="default">
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+  <p class="caption" role="heading"><span class="caption-text">Get started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="getting-started.html">Getting started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="coming-from-r.html">Coming from R</a></li>
+<li class="toctree-l1"><a class="reference internal" href="datasets.html">Datasets</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guides/sampling.html">Sampling</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/hypothesis-testing.html">Hypothesis testing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/regression.html">Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/theory-based.html">Theory-based inference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/plotting.html">Plotting: plotly &amp; plotnine</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
 <ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="index.html">moderndive (Python)</a></li>
 <li class="toctree-l1 current current-page"><a class="current reference internal" href="#">API reference</a></li>
 </ul>
 
@@ -587,7 +601,9 @@ <h2>Getters and visualization<a class="headerlink" href="#getters-and-visualizat
 <dt class="sig sig-object py" id="moderndive.shade_p_value">
 <span class="sig-prename descclassname"><span class="pre">moderndive.</span></span><span class="sig-name descname"><span class="pre">shade_p_value</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obs_stat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">direction</span></span></em>, <em class="sig-param"><span class="keyword-only-separator o"><abbr title="Keyword-only parameters separator (PEP 3102)"><span class="pre">*</span></abbr></span></em>, <em class="sig-param"><span class="n"><span class="pre">color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/moderndive/infer/viz.html#shade_p_value"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moderndive.shade_p_value" title="Link to this definition">¶</a></dt>
 <dd><p>A p-value shading spec; add it to a <code class="docutils literal notranslate"><span class="pre">visualize()</span></code> plot with <code class="docutils literal notranslate"><span class="pre">+</span></code>.</p>
-<p><code class="docutils literal notranslate"><span class="pre">direction</span></code> ∈ {right/greater, left/less, two-sided}.</p>
+<p><code class="docutils literal notranslate"><span class="pre">direction</span></code> ∈ {right/greater, left/less, two-sided}. For a faceted
+<code class="xref py py-func docutils literal notranslate"><span class="pre">visualize_fit()</span></code> plot, pass a per-term <code class="docutils literal notranslate"><span class="pre">obs_stat</span></code> — an observed
+<code class="docutils literal notranslate"><span class="pre">FitResult</span></code>, a <code class="docutils literal notranslate"><span class="pre">term</span></code>-keyed frame, or a dict — to shade each facet.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">ShadeSpec</span></code></span></p>
@@ -605,7 +621,9 @@ <h2>Getters and visualization<a class="headerlink" href="#getters-and-visualizat
 <dt class="sig sig-object py" id="moderndive.shade_confidence_interval">
 <span class="sig-prename descclassname"><span class="pre">moderndive.</span></span><span class="sig-name descname"><span class="pre">shade_confidence_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">endpoints</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/moderndive/infer/viz.html#shade_confidence_interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#moderndive.shade_confidence_interval" title="Link to this definition">¶</a></dt>
 <dd><p>A confidence-interval shading spec; add it to a <code class="docutils literal notranslate"><span class="pre">visualize()</span></code> plot with <code class="docutils literal notranslate"><span class="pre">+</span></code>.</p>
-<p><code class="docutils literal notranslate"><span class="pre">endpoints</span></code> is a CI DataFrame (<code class="docutils literal notranslate"><span class="pre">lower_ci</span></code>/<code class="docutils literal notranslate"><span class="pre">upper_ci</span></code>) or a <code class="docutils literal notranslate"><span class="pre">(lower,</span> <span class="pre">upper)</span></code> tuple.</p>
+<p><code class="docutils literal notranslate"><span class="pre">endpoints</span></code> is a CI DataFrame (<code class="docutils literal notranslate"><span class="pre">lower_ci</span></code>/<code class="docutils literal notranslate"><span class="pre">upper_ci</span></code>) or a <code class="docutils literal notranslate"><span class="pre">(lower,</span> <span class="pre">upper)</span></code>
+tuple. For a faceted <code class="xref py py-func docutils literal notranslate"><span class="pre">visualize_fit()</span></code> plot, pass a per-term CI table (with a
+<code class="docutils literal notranslate"><span class="pre">term</span></code> column) to shade each facet from its own interval.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-class docutils literal notranslate"><span class="pre">ShadeSpec</span></code></span></p>
@@ -1058,14 +1076,14 @@ <h2>Datasets<a class="headerlink" href="#datasets" title="Link to this heading">
         
         <div class="related-pages">
           
-          <a class="prev-page" href="index.html">
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+<li class="toctree-l1"><a class="reference internal" href="datasets.html">Datasets</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="guides/sampling.html">Sampling</a></li>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
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+        <article role="main" id="furo-main-content">
+          <section id="coming-from-r">
+<h1>Coming from R<a class="headerlink" href="#coming-from-r" title="Link to this heading">¶</a></h1>
+<p>If you know the R <code class="docutils literal notranslate"><span class="pre">moderndive</span></code> and <code class="docutils literal notranslate"><span class="pre">infer</span></code> packages, this page maps the API to
+Python. The grammar is the same; the main differences are Python method-chaining
+(<code class="docutils literal notranslate"><span class="pre">.hypothesize()</span></code> instead of the <code class="docutils literal notranslate"><span class="pre">|&gt;</span></code>/<code class="docutils literal notranslate"><span class="pre">%&gt;%</span></code> pipe) and polars DataFrames.</p>
+<section id="the-infer-pipeline">
+<h2>The infer pipeline<a class="headerlink" href="#the-infer-pipeline" title="Link to this heading">¶</a></h2>
+<div class="highlight-r notranslate"><div class="highlight"><pre><span></span><span class="c1"># R</span>
+<span class="n">mtcars</span><span class="w"> </span><span class="o">%&gt;%</span>
+<span class="w">  </span><span class="nf">specify</span><span class="p">(</span><span class="n">response</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mpg</span><span class="p">)</span><span class="w"> </span><span class="o">%&gt;%</span>
+<span class="w">  </span><span class="nf">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s">&quot;point&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">mu</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">20</span><span class="p">)</span><span class="w"> </span><span class="o">%&gt;%</span>
+<span class="w">  </span><span class="nf">generate</span><span class="p">(</span><span class="n">reps</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">1000</span><span class="p">,</span><span class="w"> </span><span class="n">type</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s">&quot;bootstrap&quot;</span><span class="p">)</span><span class="w"> </span><span class="o">%&gt;%</span>
+<span class="w">  </span><span class="nf">calculate</span><span class="p">(</span><span class="n">stat</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s">&quot;mean&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Python — verbs are methods on the returned objects</span>
+<span class="p">(</span>
+    <span class="n">specify</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;mpg&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;point&quot;</span><span class="p">,</span> <span class="n">mu</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">specify(formula=&quot;y</span> <span class="pre">~</span> <span class="pre">x&quot;)</span></code> works just like R’s formula interface; <code class="docutils literal notranslate"><span class="pre">success=</span></code> marks
+the success level for categorical responses.</p>
+</section>
+<section id="function-map">
+<h2>Function map<a class="headerlink" href="#function-map" title="Link to this heading">¶</a></h2>
+<div class="table-wrapper colwidths-auto docutils container">
+<table class="docutils align-default">
+<thead>
+<tr class="row-odd"><th class="head"><p>R (<code class="docutils literal notranslate"><span class="pre">infer</span></code> / <code class="docutils literal notranslate"><span class="pre">moderndive</span></code>)</p></th>
+<th class="head"><p>Python (<code class="docutils literal notranslate"><span class="pre">moderndive</span></code>)</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">specify()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">specify()</span></code></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">hypothesize()</span></code> / <code class="docutils literal notranslate"><span class="pre">hypothesise()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">.hypothesize()</span></code> / <code class="docutils literal notranslate"><span class="pre">.hypothesise()</span></code></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">generate()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">.generate()</span></code></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">calculate()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">.calculate()</span></code></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">fit()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">.fit()</span></code></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">assume()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">assume()</span></code></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">observe()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">observe()</span></code></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">get_p_value()</span></code> / <code class="docutils literal notranslate"><span class="pre">get_pvalue()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">get_p_value()</span></code> / <code class="docutils literal notranslate"><span class="pre">get_pvalue()</span></code></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">get_confidence_interval()</span></code> / <code class="docutils literal notranslate"><span class="pre">get_ci()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">get_confidence_interval()</span></code> / <code class="docutils literal notranslate"><span class="pre">get_ci()</span></code></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">visualize()</span></code> / <code class="docutils literal notranslate"><span class="pre">visualise()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">visualize()</span></code> / <code class="docutils literal notranslate"><span class="pre">visualise()</span></code></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">shade_p_value()</span></code> / <code class="docutils literal notranslate"><span class="pre">shade_pvalue()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">shade_p_value()</span></code> / <code class="docutils literal notranslate"><span class="pre">shade_pvalue()</span></code></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">shade_confidence_interval()</span></code> / <code class="docutils literal notranslate"><span class="pre">shade_ci()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">shade_confidence_interval()</span></code> / <code class="docutils literal notranslate"><span class="pre">shade_ci()</span></code></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">t_test()</span></code>, <code class="docutils literal notranslate"><span class="pre">prop_test()</span></code>, <code class="docutils literal notranslate"><span class="pre">chisq_test()</span></code></p></td>
+<td><p>same names</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">t_stat()</span></code>, <code class="docutils literal notranslate"><span class="pre">chisq_stat()</span></code></p></td>
+<td><p>same names</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">rep_sample_n()</span></code> / <code class="docutils literal notranslate"><span class="pre">rep_slice_sample()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">rep_sample_n()</span></code> / <code class="docutils literal notranslate"><span class="pre">rep_slice_sample()</span></code></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">get_regression_table()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">get_regression_table(model)</span></code></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">get_regression_points()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">get_regression_points(model)</span></code></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">get_regression_summaries()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">get_regression_summaries(model)</span></code></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">get_correlation()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">get_correlation(data,</span> <span class="pre">&quot;y</span> <span class="pre">~</span> <span class="pre">x&quot;)</span></code></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">pop_sd()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">pop_sd()</span></code></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">geom_parallel_slopes()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">geom_parallel_slopes()</span></code> (plotnine) / <code class="docutils literal notranslate"><span class="pre">gg_parallel_slopes(engine=...)</span></code></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">geom_categorical_model()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">gg_categorical_model(engine=...)</span></code></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">tidy_summary()</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">tidy_summary()</span></code></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+<section id="key-differences">
+<h2>Key differences<a class="headerlink" href="#key-differences" title="Link to this heading">¶</a></h2>
+<ul class="simple">
+<li><p><strong>Pipe → methods.</strong> <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">%&gt;%</span> <span class="pre">hypothesize(...)</span></code> becomes <code class="docutils literal notranslate"><span class="pre">x.hypothesize(...)</span></code>.</p></li>
+<li><p><strong>Models.</strong> Where R passes an <code class="docutils literal notranslate"><span class="pre">lm()</span></code> object, Python passes a fitted
+<a class="reference external" href="https://www.statsmodels.org">statsmodels</a> model:
+<code class="docutils literal notranslate"><span class="pre">smf.ols(&quot;y</span> <span class="pre">~</span> <span class="pre">x&quot;,</span> <span class="pre">data=df.to_pandas()).fit()</span></code>.</p></li>
+<li><p><strong>Plotting engine.</strong> R returns ggplot2; Python defaults to <strong>plotly</strong>
+(interactive) with <code class="docutils literal notranslate"><span class="pre">engine=&quot;plotnine&quot;</span></code> available everywhere. Plots compose with
+<code class="docutils literal notranslate"><span class="pre">+</span></code> in both engines.</p></li>
+<li><p><strong>DataFrames.</strong> Inputs/outputs are polars; pass <code class="docutils literal notranslate"><span class="pre">.to_pandas()</span></code> when a downstream
+tool needs pandas.</p></li>
+<li><p><strong>Reproducibility.</strong> Pass <code class="docutils literal notranslate"><span class="pre">seed=</span></code> to <code class="docutils literal notranslate"><span class="pre">generate()</span></code> (R uses <code class="docutils literal notranslate"><span class="pre">set.seed()</span></code>).</p></li>
+</ul>
+</section>
+<section id="same-datasets">
+<h2>Same datasets<a class="headerlink" href="#same-datasets" title="Link to this heading">¶</a></h2>
+<p>Most R <code class="docutils literal notranslate"><span class="pre">moderndive</span></code>/<code class="docutils literal notranslate"><span class="pre">infer</span></code> datasets are bundled here under the same name —
+<code class="docutils literal notranslate"><span class="pre">load_promotions()</span></code>, <code class="docutils literal notranslate"><span class="pre">load_pennies()</span></code>, <code class="docutils literal notranslate"><span class="pre">load_gss()</span></code>, etc. See <a class="reference internal" href="datasets.html"><span class="doc">Datasets</span></a>.</p>
+</section>
+</section>
+
+        </article>
+      </div>
+      <footer>
+        
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+            On this page
+          </span>
+        </div>
+        <div class="toc-tree-container">
+          <div class="toc-tree">
+            <ul>
+<li><a class="reference internal" href="#">Coming from R</a><ul>
+<li><a class="reference internal" href="#the-infer-pipeline">The infer pipeline</a></li>
+<li><a class="reference internal" href="#function-map">Function map</a></li>
+<li><a class="reference internal" href="#key-differences">Key differences</a></li>
+<li><a class="reference internal" href="#same-datasets">Same datasets</a></li>
+</ul>
+</li>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guides/sampling.html">Sampling</a></li>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="api.html">API reference</a></li>
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+        <article role="main" id="furo-main-content">
+          <section id="datasets">
+<h1>Datasets<a class="headerlink" href="#datasets" title="Link to this heading">¶</a></h1>
+<p><code class="docutils literal notranslate"><span class="pre">moderndive</span></code> bundles 58 datasets (the R <code class="docutils literal notranslate"><span class="pre">moderndive</span></code> + <code class="docutils literal notranslate"><span class="pre">infer</span></code> data, plus a few
+derived tables). Each loads with <code class="docutils literal notranslate"><span class="pre">load_&lt;name&gt;()</span></code> and returns a polars DataFrame.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
+
+<span class="n">md</span><span class="o">.</span><span class="n">available_datasets</span><span class="p">()</span>          <span class="c1"># sorted list of every dataset name</span>
+<span class="n">md</span><span class="o">.</span><span class="n">load_dataset</span><span class="p">(</span><span class="s2">&quot;promotions&quot;</span><span class="p">)</span>    <span class="c1"># load by name (string)</span>
+<span class="n">md</span><span class="o">.</span><span class="n">load_promotions</span><span class="p">()</span>             <span class="c1"># or via the generated loader</span>
+</pre></div>
+</div>
+<section id="by-topic">
+<h2>By topic<a class="headerlink" href="#by-topic" title="Link to this heading">¶</a></h2>
+<dl class="simple myst">
+<dt><strong>Sampling &amp; simulation</strong></dt><dd><p><code class="docutils literal notranslate"><span class="pre">bowl</span></code>, <code class="docutils literal notranslate"><span class="pre">bowl_sample_1</span></code>, <code class="docutils literal notranslate"><span class="pre">bowl_samples</span></code>, <code class="docutils literal notranslate"><span class="pre">tactile_prop_red</span></code>,
+<code class="docutils literal notranslate"><span class="pre">almonds_bowl</span></code>, <code class="docutils literal notranslate"><span class="pre">almonds_sample</span></code>, <code class="docutils literal notranslate"><span class="pre">almonds_sample_100</span></code>,
+<code class="docutils literal notranslate"><span class="pre">pennies</span></code>, <code class="docutils literal notranslate"><span class="pre">pennies_sample</span></code>, <code class="docutils literal notranslate"><span class="pre">pennies_resamples</span></code>, <code class="docutils literal notranslate"><span class="pre">orig_pennies_sample</span></code></p>
+</dd>
+<dt><strong>Hypothesis testing &amp; CIs</strong></dt><dd><p><code class="docutils literal notranslate"><span class="pre">promotions</span></code>, <code class="docutils literal notranslate"><span class="pre">promotions_shuffled</span></code>, <code class="docutils literal notranslate"><span class="pre">mythbusters_yawn</span></code>, <code class="docutils literal notranslate"><span class="pre">movies_sample</span></code>,
+<code class="docutils literal notranslate"><span class="pre">spotify_by_genre</span></code>, <code class="docutils literal notranslate"><span class="pre">spotify_52_original</span></code>, <code class="docutils literal notranslate"><span class="pre">spotify_52_shuffled</span></code>,
+<code class="docutils literal notranslate"><span class="pre">spotify_metal_deephouse</span></code>, <code class="docutils literal notranslate"><span class="pre">offshore</span></code>, <code class="docutils literal notranslate"><span class="pre">age_at_marriage</span></code>, <code class="docutils literal notranslate"><span class="pre">zinc_tidy</span></code>,
+<code class="docutils literal notranslate"><span class="pre">cle_sac</span></code>, <code class="docutils literal notranslate"><span class="pre">gss</span></code></p>
+</dd>
+<dt><strong>Regression</strong></dt><dd><p><code class="docutils literal notranslate"><span class="pre">evals</span></code>, <code class="docutils literal notranslate"><span class="pre">saratoga_houses</span></code>, <code class="docutils literal notranslate"><span class="pre">house_prices</span></code>, <code class="docutils literal notranslate"><span class="pre">coffee_quality</span></code>, <code class="docutils literal notranslate"><span class="pre">coffee_ratings</span></code>,
+<code class="docutils literal notranslate"><span class="pre">credit</span></code>, <code class="docutils literal notranslate"><span class="pre">MA_schools</span></code>, <code class="docutils literal notranslate"><span class="pre">un_member_states_2024</span></code>, <code class="docutils literal notranslate"><span class="pre">mario_kart_auction</span></code>,
+<code class="docutils literal notranslate"><span class="pre">amazon_books</span></code>, <code class="docutils literal notranslate"><span class="pre">old_faithful_2024</span></code></p>
+</dd>
+<dt><strong>Flights &amp; weather (nycflights23 / 13)</strong></dt><dd><p><code class="docutils literal notranslate"><span class="pre">flights</span></code>, <code class="docutils literal notranslate"><span class="pre">weather</span></code>, <code class="docutils literal notranslate"><span class="pre">airlines</span></code>, <code class="docutils literal notranslate"><span class="pre">airports</span></code>, <code class="docutils literal notranslate"><span class="pre">planes</span></code>, <code class="docutils literal notranslate"><span class="pre">envoy_flights</span></code>,
+<code class="docutils literal notranslate"><span class="pre">alaska_flights</span></code>, <code class="docutils literal notranslate"><span class="pre">early_january_weather</span></code>, <code class="docutils literal notranslate"><span class="pre">early_january_2023_weather</span></code></p>
+</dd>
+<dt><strong>Other example data</strong></dt><dd><p><code class="docutils literal notranslate"><span class="pre">gapminder</span></code>, <code class="docutils literal notranslate"><span class="pre">gapminder_2007</span></code>, <code class="docutils literal notranslate"><span class="pre">drinks</span></code>, <code class="docutils literal notranslate"><span class="pre">airline_safety</span></code>, <code class="docutils literal notranslate"><span class="pre">dem_score</span></code>,
+<code class="docutils literal notranslate"><span class="pre">us_births_1994_2003</span></code>, <code class="docutils literal notranslate"><span class="pre">steves_episodes</span></code>, <code class="docutils literal notranslate"><span class="pre">avocados</span></code>, <code class="docutils literal notranslate"><span class="pre">babies</span></code>, <code class="docutils literal notranslate"><span class="pre">ipf_lifts</span></code>,
+<code class="docutils literal notranslate"><span class="pre">ev_charging</span></code>, <code class="docutils literal notranslate"><span class="pre">DD_vs_SB</span></code>, <code class="docutils literal notranslate"><span class="pre">ma_traffic_2020_vs_2019</span></code>, <code class="docutils literal notranslate"><span class="pre">mass_traffic_2020</span></code></p>
+</dd>
+</dl>
+</section>
+<section id="tips">
+<h2>Tips<a class="headerlink" href="#tips" title="Link to this heading">¶</a></h2>
+<ul class="simple">
+<li><p>Every loader returns a polars DataFrame; call <code class="docutils literal notranslate"><span class="pre">.to_pandas()</span></code> if a function
+(e.g. statsmodels) expects pandas.</p></li>
+<li><p>Datetime columns (e.g. <code class="docutils literal notranslate"><span class="pre">time_hour</span></code> in the weather/flights data) are stored in
+UTC.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">load_dataset(&quot;&lt;name&gt;&quot;)</span></code> raises a helpful <code class="docutils literal notranslate"><span class="pre">ValueError</span></code> listing valid names if
+the dataset doesn’t exist.</p></li>
+</ul>
+</section>
+</section>
+
+        </article>
+      </div>
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-  <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
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+      body:not([data-theme="light"]) {
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+          <section id="getting-started">
+<h1>Getting started<a class="headerlink" href="#getting-started" title="Link to this heading">¶</a></h1>
+<p>This page walks through a complete analysis end to end, then points you at the
+task guides for more depth.</p>
+<section id="install">
+<h2>Install<a class="headerlink" href="#install" title="Link to this heading">¶</a></h2>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>moderndive
+</pre></div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">moderndive</span></code> returns <a class="reference external" href="https://pola.rs">polars</a> DataFrames, but every function also
+accepts pandas DataFrames as input.</p>
+</section>
+<section id="load-a-dataset">
+<h2>Load a dataset<a class="headerlink" href="#load-a-dataset" title="Link to this heading">¶</a></h2>
+<p>All datasets ship with the package and load with <code class="docutils literal notranslate"><span class="pre">load_&lt;name&gt;()</span></code>:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
+
+<span class="n">promotions</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_promotions</span><span class="p">()</span>
+<span class="n">promotions</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>shape: (5, 3)
+┌─────┬──────────┬────────┐
+│ id  ┆ decision ┆ gender │
+│ i64 ┆ str      ┆ str    │
+╞═════╪══════════╪════════╡
+│ 1   ┆ promoted ┆ male   │
+│ 2   ┆ promoted ┆ male   │
+│ …   ┆ …        ┆ …      │
+└─────┴──────────┴────────┘
+</pre></div>
+</div>
+<p>List everything that’s available with <code class="docutils literal notranslate"><span class="pre">md.available_datasets()</span></code> (58 datasets), and
+see <a class="reference internal" href="datasets.html"><span class="doc">Datasets</span></a> for a thematic tour.</p>
+</section>
+<section id="a-first-summary">
+<h2>A first summary<a class="headerlink" href="#a-first-summary" title="Link to this heading">¶</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">tidy_summary</span></code> gives a per-variable five-number summary (numeric) or counts
+(categorical):</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">tidy_summary</span>
+
+<span class="n">tidy_summary</span><span class="p">(</span><span class="n">md</span><span class="o">.</span><span class="n">load_almonds_sample_100</span><span class="p">(),</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;weight&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+</section>
+<section id="the-inference-pipeline">
+<h2>The inference pipeline<a class="headerlink" href="#the-inference-pipeline" title="Link to this heading">¶</a></h2>
+<p>The core grammar mirrors R <code class="docutils literal notranslate"><span class="pre">infer</span></code>. You build a pipeline and read it like a
+sentence:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span><span class="p">,</span> <span class="n">observe</span><span class="p">,</span> <span class="n">get_p_value</span>
+
+<span class="c1"># 1. The observed statistic</span>
+<span class="n">obs</span> <span class="o">=</span> <span class="n">observe</span><span class="p">(</span>
+    <span class="n">promotions</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;decision ~ gender&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;promoted&quot;</span><span class="p">,</span>
+    <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;male&quot;</span><span class="p">,</span> <span class="s2">&quot;female&quot;</span><span class="p">),</span>
+<span class="p">)</span>
+
+<span class="c1"># 2. A null distribution: specify → hypothesize → generate → calculate</span>
+<span class="n">null</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">specify</span><span class="p">(</span><span class="n">promotions</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;decision ~ gender&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;promoted&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;independence&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;permute&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;male&quot;</span><span class="p">,</span> <span class="s2">&quot;female&quot;</span><span class="p">))</span>
+<span class="p">)</span>
+
+<span class="c1"># 3. Summarize</span>
+<span class="n">get_p_value</span><span class="p">(</span><span class="n">null</span><span class="p">,</span> <span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>   <span class="c1"># ≈ 0.025</span>
+</pre></div>
+</div>
+<p>Each verb has a focused guide: <a class="reference internal" href="guides/sampling.html"><span class="doc">Sampling</span></a>,
+<a class="reference internal" href="guides/confidence-intervals.html"><span class="doc">Bootstrapping &amp; confidence intervals</span></a>, and <a class="reference internal" href="guides/hypothesis-testing.html"><span class="doc">Hypothesis testing</span></a>.</p>
+</section>
+<section id="visualizing-choose-your-engine">
+<h2>Visualizing — choose your engine<a class="headerlink" href="#visualizing-choose-your-engine" title="Link to this heading">¶</a></h2>
+<p>Plots default to <strong>plotly</strong> (interactive). Pass <code class="docutils literal notranslate"><span class="pre">engine=&quot;plotnine&quot;</span></code> for
+grammar-of-graphics output. The composition syntax is identical:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">visualize</span><span class="p">,</span> <span class="n">shade_p_value</span>
+
+<span class="c1"># Interactive plotly figure</span>
+<span class="n">visualize</span><span class="p">(</span><span class="n">null</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_p_value</span><span class="p">(</span><span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>
+
+<span class="c1"># Same plot, plotnine</span>
+<span class="n">visualize</span><span class="p">(</span><span class="n">null</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s2">&quot;plotnine&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_p_value</span><span class="p">(</span><span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>See <a class="reference internal" href="guides/plotting.html"><span class="doc">Plotting: plotly &amp; plotnine</span></a> for shading, confidence-interval overlays, theoretical
+overlays, and the regression-model plots.</p>
+</section>
+<section id="regression">
+<h2>Regression<a class="headerlink" href="#regression" title="Link to this heading">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">statsmodels.formula.api</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">smf</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">get_regression_table</span>
+
+<span class="n">houses</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_saratoga_houses</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span><span class="s2">&quot;price ~ living_area + bedrooms&quot;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">houses</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">())</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+<span class="n">get_regression_table</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Full details in <a class="reference internal" href="guides/regression.html"><span class="doc">Regression</span></a>.</p>
+</section>
+</section>
+
+        </article>
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+          <div class="toc-tree">
+            <ul>
+<li><a class="reference internal" href="#">Getting started</a><ul>
+<li><a class="reference internal" href="#install">Install</a></li>
+<li><a class="reference internal" href="#load-a-dataset">Load a dataset</a></li>
+<li><a class="reference internal" href="#a-first-summary">A first summary</a></li>
+<li><a class="reference internal" href="#the-inference-pipeline">The inference pipeline</a></li>
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+          <section id="bootstrapping-confidence-intervals">
+<h1>Bootstrapping &amp; confidence intervals<a class="headerlink" href="#bootstrapping-confidence-intervals" title="Link to this heading">¶</a></h1>
+<p>Resample your data with replacement to build a <strong>bootstrap distribution</strong>, then
+read a confidence interval off it.</p>
+<section id="a-bootstrap-distribution-for-a-mean">
+<h2>A bootstrap distribution for a mean<a class="headerlink" href="#a-bootstrap-distribution-for-a-mean" title="Link to this heading">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span><span class="p">,</span> <span class="n">get_confidence_interval</span><span class="p">,</span> <span class="n">visualize</span><span class="p">,</span> <span class="n">shade_confidence_interval</span>
+
+<span class="n">almonds</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_almonds_sample_100</span><span class="p">()</span>
+
+<span class="n">boot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">specify</span><span class="p">(</span><span class="n">almonds</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="confidence-intervals-three-methods">
+<h2>Confidence intervals — three methods<a class="headerlink" href="#confidence-intervals-three-methods" title="Link to this heading">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Percentile method (default)</span>
+<span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;percentile&quot;</span><span class="p">)</span>
+<span class="c1"># shape: (1, 2) → lower_ci ≈ 3.61, upper_ci ≈ 3.75</span>
+
+<span class="c1"># Standard-error method (needs the point estimate)</span>
+<span class="n">point</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">specify</span><span class="p">(</span><span class="n">almonds</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">))</span>
+<span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;se&quot;</span><span class="p">,</span> <span class="n">point_estimate</span><span class="o">=</span><span class="n">point</span><span class="p">)</span>
+
+<span class="c1"># Bias-corrected</span>
+<span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bias-corrected&quot;</span><span class="p">,</span> <span class="n">point_estimate</span><span class="o">=</span><span class="n">point</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Distribution objects also expose the getter as a method:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">boot</span><span class="o">.</span><span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;percentile&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="visualize-the-interval">
+<h2>Visualize the interval<a class="headerlink" href="#visualize-the-interval" title="Link to this heading">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">ci</span> <span class="o">=</span> <span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;percentile&quot;</span><span class="p">)</span>
+
+<span class="c1"># plotly (default) — interactive</span>
+<span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_confidence_interval</span><span class="p">(</span><span class="n">ci</span><span class="p">)</span>
+
+<span class="c1"># plotnine</span>
+<span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s2">&quot;plotnine&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_confidence_interval</span><span class="p">(</span><span class="n">ci</span><span class="p">)</span>
+
+<span class="c1"># one-call keyword form</span>
+<span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">shade_ci</span><span class="o">=</span><span class="n">ci</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="a-confidence-interval-for-a-proportion">
+<h2>A confidence interval for a proportion<a class="headerlink" href="#a-confidence-interval-for-a-proportion" title="Link to this heading">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">polars</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pl</span>
+
+<span class="n">mythbusters</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_mythbusters_yawn</span><span class="p">()</span>
+
+<span class="n">boot_prop</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">specify</span><span class="p">(</span><span class="n">mythbusters</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;yawn&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;yes&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;prop&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">boot_prop</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;percentile&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="a-confidence-interval-for-a-difference">
+<h2>A confidence interval for a difference<a class="headerlink" href="#a-confidence-interval-for-a-difference" title="Link to this heading">¶</a></h2>
+<p>Add an explanatory variable and an <code class="docutils literal notranslate"><span class="pre">order=</span></code> to compare two groups:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">boot_diff</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">specify</span><span class="p">(</span><span class="n">mythbusters</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;yawn ~ group&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;yes&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;seed&quot;</span><span class="p">,</span> <span class="s2">&quot;control&quot;</span><span class="p">))</span>
+<span class="p">)</span>
+<span class="n">boot_diff</span><span class="o">.</span><span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p>For <em>theory-based</em> intervals (the formula-based <code class="docutils literal notranslate"><span class="pre">t</span></code> interval) see
+<a class="reference internal" href="theory-based.html"><span class="doc">Theory-based inference</span></a>.</p>
+</div>
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+</section>
+
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+            <ul>
+<li><a class="reference internal" href="#">Bootstrapping &amp; confidence intervals</a><ul>
+<li><a class="reference internal" href="#a-bootstrap-distribution-for-a-mean">A bootstrap distribution for a mean</a></li>
+<li><a class="reference internal" href="#confidence-intervals-three-methods">Confidence intervals — three methods</a></li>
+<li><a class="reference internal" href="#visualize-the-interval">Visualize the interval</a></li>
+<li><a class="reference internal" href="#a-confidence-interval-for-a-proportion">A confidence interval for a proportion</a></li>
+<li><a class="reference internal" href="#a-confidence-interval-for-a-difference">A confidence interval for a difference</a></li>
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+          <section id="hypothesis-testing">
+<h1>Hypothesis testing<a class="headerlink" href="#hypothesis-testing" title="Link to this heading">¶</a></h1>
+<p>Hypothesis tests follow the same grammar as confidence intervals, with an added
+<code class="docutils literal notranslate"><span class="pre">hypothesize()</span></code> step that defines the null world, and <code class="docutils literal notranslate"><span class="pre">generate()</span></code> that simulates
+from it.</p>
+<section id="two-groups-a-permutation-test">
+<h2>Two groups: a permutation test<a class="headerlink" href="#two-groups-a-permutation-test" title="Link to this heading">¶</a></h2>
+<p>The classic “promotions” experiment — does the (perceived) gender on a résumé
+change the promotion rate?</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span><span class="p">,</span> <span class="n">observe</span><span class="p">,</span> <span class="n">get_p_value</span><span class="p">,</span> <span class="n">visualize</span><span class="p">,</span> <span class="n">shade_p_value</span>
+
+<span class="n">promotions</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_promotions</span><span class="p">()</span>
+
+<span class="c1"># Observed difference in promotion proportions, male − female</span>
+<span class="n">obs</span> <span class="o">=</span> <span class="n">observe</span><span class="p">(</span>
+    <span class="n">promotions</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;decision ~ gender&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;promoted&quot;</span><span class="p">,</span>
+    <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;male&quot;</span><span class="p">,</span> <span class="s2">&quot;female&quot;</span><span class="p">),</span>
+<span class="p">)</span>   <span class="c1"># ≈ 0.292</span>
+
+<span class="c1"># Null: gender is independent of the decision → permute the labels</span>
+<span class="n">null</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">specify</span><span class="p">(</span><span class="n">promotions</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;decision ~ gender&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;promoted&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;independence&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;permute&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;male&quot;</span><span class="p">,</span> <span class="s2">&quot;female&quot;</span><span class="p">))</span>
+<span class="p">)</span>
+
+<span class="n">get_p_value</span><span class="p">(</span><span class="n">null</span><span class="p">,</span> <span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>   <span class="c1"># ≈ 0.025</span>
+</pre></div>
+</div>
+</section>
+<section id="shade-the-p-value">
+<h2>Shade the p-value<a class="headerlink" href="#shade-the-p-value" title="Link to this heading">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">visualize</span><span class="p">(</span><span class="n">null</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_p_value</span><span class="p">(</span><span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">direction</span></code> is one of <code class="docutils literal notranslate"><span class="pre">&quot;right&quot;</span></code>/<code class="docutils literal notranslate"><span class="pre">&quot;greater&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;left&quot;</span></code>/<code class="docutils literal notranslate"><span class="pre">&quot;less&quot;</span></code>, or <code class="docutils literal notranslate"><span class="pre">&quot;two-sided&quot;</span></code>.
+The two-sided shading mirrors the observed statistic about 0.</p>
+</section>
+<section id="one-mean-one-proportion-point-null">
+<h2>One mean / one proportion (point null)<a class="headerlink" href="#one-mean-one-proportion-point-null" title="Link to this heading">¶</a></h2>
+<p>Use a <code class="docutils literal notranslate"><span class="pre">&quot;point&quot;</span></code> null with bootstrap resampling, supplying the hypothesized value:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">age</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_age_at_marriage</span><span class="p">()</span>
+
+<span class="n">obs_t</span> <span class="o">=</span> <span class="n">observe</span><span class="p">(</span><span class="n">age</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">,</span> <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;t&quot;</span><span class="p">,</span> <span class="n">null</span><span class="o">=</span><span class="s2">&quot;point&quot;</span><span class="p">,</span> <span class="n">mu</span><span class="o">=</span><span class="mi">23</span><span class="p">)</span>
+
+<span class="n">null_t</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">specify</span><span class="p">(</span><span class="n">age</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;point&quot;</span><span class="p">,</span> <span class="n">mu</span><span class="o">=</span><span class="mi">23</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;t&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">get_p_value</span><span class="p">(</span><span class="n">null_t</span><span class="p">,</span> <span class="n">obs_stat</span><span class="o">=</span><span class="n">obs_t</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;two-sided&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>For a one-proportion test you can also <em>simulate</em> draws directly:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">polars</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pl</span>
+
+<span class="n">coins</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;flip&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;heads&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">30</span> <span class="o">+</span> <span class="p">[</span><span class="s2">&quot;tails&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">70</span><span class="p">})</span>
+
+<span class="n">null_p</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">specify</span><span class="p">(</span><span class="n">coins</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;flip&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;heads&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;point&quot;</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;draw&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>   <span class="c1"># &quot;simulate&quot; is an alias</span>
+    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;prop&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="available-statistics">
+<h2>Available statistics<a class="headerlink" href="#available-statistics" title="Link to this heading">¶</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">calculate(stat=...)</span></code> supports the full infer vocabulary: <code class="docutils literal notranslate"><span class="pre">&quot;mean&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;median&quot;</span></code>,
+<code class="docutils literal notranslate"><span class="pre">&quot;sum&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;sd&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;prop&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;count&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;diff</span> <span class="pre">in</span> <span class="pre">means&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;diff</span> <span class="pre">in</span> <span class="pre">medians&quot;</span></code>,
+<code class="docutils literal notranslate"><span class="pre">&quot;diff</span> <span class="pre">in</span> <span class="pre">props&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;ratio</span> <span class="pre">of</span> <span class="pre">means&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;ratio</span> <span class="pre">of</span> <span class="pre">props&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;odds</span> <span class="pre">ratio&quot;</span></code>,
+<code class="docutils literal notranslate"><span class="pre">&quot;slope&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;correlation&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;t&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;z&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;F&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;Chisq&quot;</span></code>, plus any custom
+callable <code class="docutils literal notranslate"><span class="pre">stat(response,</span> <span class="pre">explanatory)</span> <span class="pre">-&gt;</span> <span class="pre">float</span></code>.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p>Prefer a one-line classical test? See the tidy wrappers in <a class="reference internal" href="theory-based.html"><span class="doc">Theory-based inference</span></a>
+(<code class="docutils literal notranslate"><span class="pre">t_test</span></code>, <code class="docutils literal notranslate"><span class="pre">prop_test</span></code>, <code class="docutils literal notranslate"><span class="pre">chisq_test</span></code>).</p>
+</div>
+</section>
+</section>
+
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+<li><a class="reference internal" href="#">Hypothesis testing</a><ul>
+<li><a class="reference internal" href="#two-groups-a-permutation-test">Two groups: a permutation test</a></li>
+<li><a class="reference internal" href="#shade-the-p-value">Shade the p-value</a></li>
+<li><a class="reference internal" href="#one-mean-one-proportion-point-null">One mean / one proportion (point null)</a></li>
+<li><a class="reference internal" href="#available-statistics">Available statistics</a></li>
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+      <line x1="18.36" y1="18.36" x2="19.78" y2="19.78"></line>
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+  </symbol>
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+    <title>Auto light/dark, in light mode</title>
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+    </svg>
+  </symbol>
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+    <title>Auto light/dark, in dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1" stroke-linecap="round" stroke-linejoin="round"
+      class="icon-custom-derived-from-feather-sun-and-tabler-moon">
+      <path d="M 8.282 7.007 C 8.385 7.007 8.494 7.007 8.595 7.007 C 5.18 10.184 6.481 15.869 10.942 17.24 C 12.275 17.648 13.706 17.589 15 17.066 C 12.851 22.236 5.91 23.143 2.505 18.696 C -0.897 14.249 1.791 7.786 7.342 7.063 C 7.652 7.021 7.965 7 8.282 7 L 8.282 7.007 Z"/>
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+      <line style="opacity: 50%" x1="20.711" y1="10.212" x2="21.563" y2="11.063"/>
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+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="sampling.html">Sampling</a></li>
+<li class="toctree-l1"><a class="reference internal" href="confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
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+<li class="toctree-l1 current current-page"><a class="current reference internal" href="#">Plotting: plotly &amp; plotnine</a></li>
+</ul>
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+          <section id="plotting-plotly-plotnine">
+<h1>Plotting: plotly &amp; plotnine<a class="headerlink" href="#plotting-plotly-plotnine" title="Link to this heading">¶</a></h1>
+<p>Every plotting function in <code class="docutils literal notranslate"><span class="pre">moderndive</span></code> takes an <code class="docutils literal notranslate"><span class="pre">engine=</span></code> argument:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">engine=&quot;plotly&quot;</span></code> (<strong>default</strong>) — interactive figures (<code class="docutils literal notranslate"><span class="pre">plotly.graph_objects.Figure</span></code>).</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">engine=&quot;plotnine&quot;</span></code> — grammar-of-graphics figures (<code class="docutils literal notranslate"><span class="pre">plotnine.ggplot</span></code>).</p></li>
+</ul>
+<p>The composition syntax is identical across engines, so you can switch a whole
+analysis by changing one argument.</p>
+<section id="visualize-returns-an-inferplot">
+<h2>visualize() returns an InferPlot<a class="headerlink" href="#visualize-returns-an-inferplot" title="Link to this heading">¶</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">visualize()</span></code> returns a small <code class="docutils literal notranslate"><span class="pre">InferPlot</span></code> wrapper that you compose with shading via
+<code class="docutils literal notranslate"><span class="pre">+</span></code> in <strong>both</strong> engines:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span><span class="p">,</span> <span class="n">get_confidence_interval</span><span class="p">,</span> <span class="n">visualize</span><span class="p">,</span> <span class="n">shade_confidence_interval</span>
+
+<span class="n">boot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">specify</span><span class="p">(</span><span class="n">md</span><span class="o">.</span><span class="n">load_almonds_sample_100</span><span class="p">(),</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">ci</span> <span class="o">=</span> <span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;percentile&quot;</span><span class="p">)</span>
+
+<span class="n">p</span> <span class="o">=</span> <span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_confidence_interval</span><span class="p">(</span><span class="n">ci</span><span class="p">)</span>   <span class="c1"># plotly InferPlot</span>
+<span class="n">p</span><span class="o">.</span><span class="n">figure</span>          <span class="c1"># the underlying plotly Figure</span>
+<span class="n">p</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>          <span class="c1"># display it</span>
+</pre></div>
+</div>
+<p>For the plotnine engine, the raw <code class="docutils literal notranslate"><span class="pre">ggplot</span></code> is available via <code class="docutils literal notranslate"><span class="pre">.gg</span></code>:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">g</span> <span class="o">=</span> <span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s2">&quot;plotnine&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_confidence_interval</span><span class="p">(</span><span class="n">ci</span><span class="p">)</span>
+<span class="n">g</span><span class="o">.</span><span class="n">gg</span>              <span class="c1"># the underlying plotnine ggplot</span>
+</pre></div>
+</div>
+<section id="keyword-form">
+<h3>Keyword form<a class="headerlink" href="#keyword-form" title="Link to this heading">¶</a></h3>
+<p>If you’d rather not use <code class="docutils literal notranslate"><span class="pre">+</span></code>, pass the shading inline (handy for plotly):</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">shade_ci</span><span class="o">=</span><span class="n">ci</span><span class="p">)</span>
+<span class="n">visualize</span><span class="p">(</span><span class="n">null</span><span class="p">,</span> <span class="n">shade_pvalue</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;obs_stat&quot;</span><span class="p">:</span> <span class="n">obs</span><span class="p">,</span> <span class="s2">&quot;direction&quot;</span><span class="p">:</span> <span class="s2">&quot;right&quot;</span><span class="p">})</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="shading-p-values">
+<h2>Shading p-values<a class="headerlink" href="#shading-p-values" title="Link to this heading">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">observe</span><span class="p">,</span> <span class="n">shade_p_value</span>
+
+<span class="n">promotions</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_promotions</span><span class="p">()</span>
+<span class="n">obs</span> <span class="o">=</span> <span class="n">observe</span><span class="p">(</span><span class="n">promotions</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;decision ~ gender&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;promoted&quot;</span><span class="p">,</span>
+              <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;male&quot;</span><span class="p">,</span> <span class="s2">&quot;female&quot;</span><span class="p">))</span>
+<span class="n">null</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">specify</span><span class="p">(</span><span class="n">promotions</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;decision ~ gender&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;promoted&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;independence&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;permute&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;male&quot;</span><span class="p">,</span> <span class="s2">&quot;female&quot;</span><span class="p">))</span>
+<span class="p">)</span>
+
+<span class="n">visualize</span><span class="p">(</span><span class="n">null</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_p_value</span><span class="p">(</span><span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="simulation-vs-theory-overlays">
+<h2>Simulation vs. theory overlays<a class="headerlink" href="#simulation-vs-theory-overlays" title="Link to this heading">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;simulation&quot;</span><span class="p">)</span>    <span class="c1"># histogram (default)</span>
+<span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;theoretical&quot;</span><span class="p">)</span>   <span class="c1"># normal-approximation curve</span>
+<span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">)</span>          <span class="c1"># histogram + curve overlaid</span>
+</pre></div>
+</div>
+</section>
+<section id="faceted-regression-coefficient-plots">
+<h2>Faceted regression-coefficient plots<a class="headerlink" href="#faceted-regression-coefficient-plots" title="Link to this heading">¶</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">visualize_fit</span></code> shows one panel per term, and shading is <strong>per-facet</strong>:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive.infer.viz</span><span class="w"> </span><span class="kn">import</span> <span class="n">visualize_fit</span>
+
+<span class="n">f</span> <span class="o">=</span> <span class="s2">&quot;price ~ living_area + bedrooms&quot;</span>
+<span class="n">houses</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_saratoga_houses</span><span class="p">()</span>
+<span class="n">boot_fit</span> <span class="o">=</span> <span class="n">specify</span><span class="p">(</span><span class="n">houses</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+
+<span class="n">visualize_fit</span><span class="p">(</span><span class="n">boot_fit</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_confidence_interval</span><span class="p">(</span><span class="n">boot_fit</span><span class="o">.</span><span class="n">get_confidence_interval</span><span class="p">())</span>
+</pre></div>
+</div>
+</section>
+<section id="model-plots">
+<h2>Model plots<a class="headerlink" href="#model-plots" title="Link to this heading">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">gg_parallel_slopes</span><span class="p">,</span> <span class="n">gg_categorical_model</span><span class="p">,</span> <span class="n">pairplot</span>
+
+<span class="n">evals</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_evals</span><span class="p">()</span>
+<span class="n">gg_parallel_slopes</span><span class="p">(</span><span class="n">evals</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">explanatory</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">,</span> <span class="n">by</span><span class="o">=</span><span class="s2">&quot;gender&quot;</span><span class="p">)</span>
+<span class="n">gg_categorical_model</span><span class="p">(</span><span class="n">evals</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">explanatory</span><span class="o">=</span><span class="s2">&quot;rank&quot;</span><span class="p">)</span>
+
+<span class="c1"># Scatterplot matrix (~ GGally::ggpairs)</span>
+<span class="n">pairplot</span><span class="p">(</span><span class="n">md</span><span class="o">.</span><span class="n">load_coffee_quality</span><span class="p">(),</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;total_cup_points&quot;</span><span class="p">,</span> <span class="s2">&quot;aroma&quot;</span><span class="p">,</span> <span class="s2">&quot;flavor&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+</section>
+<section id="saving-figures">
+<h2>Saving figures<a class="headerlink" href="#saving-figures" title="Link to this heading">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">)</span>            <span class="c1"># plotly</span>
+<span class="n">p</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;dist.html&quot;</span><span class="p">)</span>            <span class="c1"># interactive HTML (no extra deps)</span>
+<span class="n">p</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;dist.png&quot;</span><span class="p">)</span>             <span class="c1"># static image — needs: pip install &quot;moderndive[image]&quot;</span>
+
+<span class="n">g</span> <span class="o">=</span> <span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s2">&quot;plotnine&quot;</span><span class="p">)</span>
+<span class="n">g</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;dist.png&quot;</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">150</span><span class="p">)</span>
+</pre></div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">pairplot(...,</span> <span class="pre">engine=&quot;seaborn&quot;)</span></code> (alias <code class="docutils literal notranslate"><span class="pre">&quot;plotnine&quot;</span></code>) returns a Matplotlib figure
+if you prefer the seaborn-backed scatterplot matrix.</p>
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+            <ul>
+<li><a class="reference internal" href="#">Plotting: plotly &amp; plotnine</a><ul>
+<li><a class="reference internal" href="#visualize-returns-an-inferplot">visualize() returns an InferPlot</a><ul>
+<li><a class="reference internal" href="#keyword-form">Keyword form</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#shading-p-values">Shading p-values</a></li>
+<li><a class="reference internal" href="#simulation-vs-theory-overlays">Simulation vs. theory overlays</a></li>
+<li><a class="reference internal" href="#faceted-regression-coefficient-plots">Faceted regression-coefficient plots</a></li>
+<li><a class="reference internal" href="#model-plots">Model plots</a></li>
+<li><a class="reference internal" href="#saving-figures">Saving figures</a></li>
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+        <article role="main" id="furo-main-content">
+          <section id="regression">
+<h1>Regression<a class="headerlink" href="#regression" title="Link to this heading">¶</a></h1>
+<p>The regression helpers turn a fitted <a class="reference external" href="https://www.statsmodels.org">statsmodels</a>
+model into tidy polars tables — the analog of R <code class="docutils literal notranslate"><span class="pre">moderndive</span></code>’s
+<code class="docutils literal notranslate"><span class="pre">get_regression_table()</span></code> / <code class="docutils literal notranslate"><span class="pre">get_regression_points()</span></code> / <code class="docutils literal notranslate"><span class="pre">get_regression_summaries()</span></code>.</p>
+<p>Fit models with statsmodels’ formula API, then tidy them:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">statsmodels.formula.api</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">smf</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
+    <span class="n">get_regression_table</span><span class="p">,</span> <span class="n">get_regression_points</span><span class="p">,</span> <span class="n">get_regression_summaries</span><span class="p">,</span> <span class="n">get_correlation</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">houses</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_saratoga_houses</span><span class="p">()</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span><span class="s2">&quot;price ~ living_area + bedrooms&quot;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">houses</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">())</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+</pre></div>
+</div>
+<section id="coefficient-table-with-confidence-intervals">
+<h2>Coefficient table (with confidence intervals)<a class="headerlink" href="#coefficient-table-with-confidence-intervals" title="Link to this heading">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">get_regression_table</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>shape: (3, 7)
+┌─────────────┬───────────┬───────────┬───────────┬─────────┬────────────┬───────────┐
+│ term        ┆ estimate  ┆ std_error ┆ statistic ┆ p_value ┆ lower_ci   ┆ upper_ci  │
+╞═════════════╪═══════════╪═══════════╪═══════════╪═════════╪════════════╪═══════════╡
+│ intercept   ┆ 20986.094 ┆ 6816.251  ┆ 3.079     ┆ 0.002   ┆ 7611.128   ┆ 34361.06  │
+│ living_area ┆ 93.842    ┆ 3.109     ┆ 30.183    ┆ 0.0     ┆ 87.741     ┆ 99.943    │
+│ bedrooms    ┆ -7483.095 ┆ 2783.531  ┆ -2.688    ┆ 0.007   ┆ -12944.988 ┆ -2021.203 │
+└─────────────┴───────────┴───────────┴───────────┴─────────┴────────────┴───────────┘
+</pre></div>
+</div>
+<p>Change the confidence level with <code class="docutils literal notranslate"><span class="pre">conf_level=</span></code> (e.g. <code class="docutils literal notranslate"><span class="pre">0.99</span></code>).</p>
+</section>
+<section id="fitted-values-residuals">
+<h2>Fitted values &amp; residuals<a class="headerlink" href="#fitted-values-residuals" title="Link to this heading">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">get_regression_points</span><span class="p">(</span><span class="n">model</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+<span class="c1"># columns: ID, price, living_area, bedrooms, price_hat, residual</span>
+</pre></div>
+</div>
+</section>
+<section id="model-fit-summaries">
+<h2>Model-fit summaries<a class="headerlink" href="#model-fit-summaries" title="Link to this heading">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">get_regression_summaries</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+<span class="c1"># r_squared, adj_r_squared, mse, rmse, sigma, statistic (F), p_value, df, nobs</span>
+</pre></div>
+</div>
+</section>
+<section id="correlation">
+<h2>Correlation<a class="headerlink" href="#correlation" title="Link to this heading">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">get_correlation</span><span class="p">(</span><span class="n">houses</span><span class="p">,</span> <span class="s2">&quot;price ~ living_area&quot;</span><span class="p">)</span>   <span class="c1"># ≈ 0.759</span>
+<span class="c1"># or: get_correlation(houses, x=&quot;living_area&quot;, y=&quot;price&quot;)</span>
+</pre></div>
+</div>
+</section>
+<section id="visualizing-models">
+<h2>Visualizing models<a class="headerlink" href="#visualizing-models" title="Link to this heading">¶</a></h2>
+<p>Two ports of R <code class="docutils literal notranslate"><span class="pre">moderndive</span></code>’s ggplot helpers, both dual-engine:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">gg_parallel_slopes</span><span class="p">,</span> <span class="n">gg_categorical_model</span>
+
+<span class="n">evals</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_evals</span><span class="p">()</span>
+
+<span class="c1"># Parallel-slopes model: one common slope, a separate intercept per group</span>
+<span class="n">gg_parallel_slopes</span><span class="p">(</span><span class="n">evals</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">explanatory</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">,</span> <span class="n">by</span><span class="o">=</span><span class="s2">&quot;gender&quot;</span><span class="p">)</span>            <span class="c1"># plotly</span>
+<span class="n">gg_parallel_slopes</span><span class="p">(</span><span class="n">evals</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">explanatory</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">,</span> <span class="n">by</span><span class="o">=</span><span class="s2">&quot;gender&quot;</span><span class="p">,</span>
+                   <span class="n">engine</span><span class="o">=</span><span class="s2">&quot;plotnine&quot;</span><span class="p">)</span>
+
+<span class="c1"># Regression with a single categorical predictor</span>
+<span class="n">gg_categorical_model</span><span class="p">(</span><span class="n">evals</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">explanatory</span><span class="o">=</span><span class="s2">&quot;rank&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>For the plotnine engine you can also drop the parallel-slopes lines onto your own
+<code class="docutils literal notranslate"><span class="pre">ggplot</span></code> with <code class="docutils literal notranslate"><span class="pre">geom_parallel_slopes()</span></code>.</p>
+</section>
+<section id="inference-for-regression-coefficients">
+<h2>Inference for regression coefficients<a class="headerlink" href="#inference-for-regression-coefficients" title="Link to this heading">¶</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">fit()</span></code> runs the regression on every bootstrap/permutation replicate, giving a
+distribution per coefficient. Pair it with <code class="docutils literal notranslate"><span class="pre">visualize_fit</span></code> and <strong>per-facet</strong>
+shading:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">moderndive.infer.viz</span><span class="w"> </span><span class="kn">import</span> <span class="n">visualize_fit</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">shade_confidence_interval</span><span class="p">,</span> <span class="n">shade_p_value</span>
+
+<span class="n">f</span> <span class="o">=</span> <span class="s2">&quot;price ~ living_area + bedrooms&quot;</span>
+<span class="n">obs_fit</span> <span class="o">=</span> <span class="n">specify</span><span class="p">(</span><span class="n">houses</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+
+<span class="c1"># Bootstrap distribution of each coefficient → per-term CIs</span>
+<span class="n">boot_fit</span> <span class="o">=</span> <span class="n">specify</span><span class="p">(</span><span class="n">houses</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+<span class="n">boot_fit</span><span class="o">.</span><span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">)</span>          <span class="c1"># one row per term</span>
+<span class="n">visualize_fit</span><span class="p">(</span><span class="n">boot_fit</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_confidence_interval</span><span class="p">(</span><span class="n">boot_fit</span><span class="o">.</span><span class="n">get_confidence_interval</span><span class="p">())</span>
+
+<span class="c1"># Null distribution → per-term p-values, each facet shaded at its own estimate</span>
+<span class="n">null_fit</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">specify</span><span class="p">(</span><span class="n">houses</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;independence&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;permute&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+<span class="p">)</span>
+<span class="n">null_fit</span><span class="o">.</span><span class="n">get_p_value</span><span class="p">(</span><span class="n">obs_stat</span><span class="o">=</span><span class="n">obs_fit</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;two-sided&quot;</span><span class="p">)</span>
+<span class="n">visualize_fit</span><span class="p">(</span><span class="n">null_fit</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_p_value</span><span class="p">(</span><span class="n">obs_stat</span><span class="o">=</span><span class="n">obs_fit</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;two-sided&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Per-facet shading works in both engines: <code class="docutils literal notranslate"><span class="pre">shade_p_value</span></code> / <code class="docutils literal notranslate"><span class="pre">shade_confidence_interval</span></code>
+accept the observed <code class="docutils literal notranslate"><span class="pre">FitResult</span></code> or a term-keyed table, and each facet is shaded
+from its own value.</p>
+</section>
+</section>
+
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+            <ul>
+<li><a class="reference internal" href="#">Regression</a><ul>
+<li><a class="reference internal" href="#coefficient-table-with-confidence-intervals">Coefficient table (with confidence intervals)</a></li>
+<li><a class="reference internal" href="#fitted-values-residuals">Fitted values &amp; residuals</a></li>
+<li><a class="reference internal" href="#model-fit-summaries">Model-fit summaries</a></li>
+<li><a class="reference internal" href="#correlation">Correlation</a></li>
+<li><a class="reference internal" href="#visualizing-models">Visualizing models</a></li>
+<li><a class="reference internal" href="#inference-for-regression-coefficients">Inference for regression coefficients</a></li>
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+        <article role="main" id="furo-main-content">
+          <section id="sampling">
+<h1>Sampling<a class="headerlink" href="#sampling" title="Link to this heading">¶</a></h1>
+<p>Sampling activities (the “bowl” of balls, tactile samples) are how ModernDive
+builds intuition for sampling variation. <code class="docutils literal notranslate"><span class="pre">rep_slice_sample</span></code> takes repeated samples
+and stacks them with a <code class="docutils literal notranslate"><span class="pre">replicate</span></code> column — the analog of R
+<code class="docutils literal notranslate"><span class="pre">moderndive::rep_slice_sample()</span></code> / <code class="docutils literal notranslate"><span class="pre">infer::rep_sample_n()</span></code>.</p>
+<section id="the-bowl">
+<h2>The bowl<a class="headerlink" href="#the-bowl" title="Link to this heading">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">polars</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pl</span>
+
+<span class="n">bowl</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_bowl</span><span class="p">()</span>      <span class="c1"># 2400 red/white balls</span>
+<span class="n">bowl</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+</div>
+</section>
+<section id="one-virtual-sample">
+<h2>One virtual sample<a class="headerlink" href="#one-virtual-sample" title="Link to this heading">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">rep_slice_sample</span>
+
+<span class="n">sample</span> <span class="o">=</span> <span class="n">rep_slice_sample</span><span class="p">(</span><span class="n">bowl</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="c1"># proportion red in this sample</span>
+<span class="n">sample</span><span class="o">.</span><span class="n">select</span><span class="p">((</span><span class="n">pl</span><span class="o">.</span><span class="n">col</span><span class="p">(</span><span class="s2">&quot;color&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;red&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">&quot;prop_red&quot;</span><span class="p">))</span>
+</pre></div>
+</div>
+</section>
+<section id="many-samples-a-sampling-distribution">
+<h2>Many samples → a sampling distribution<a class="headerlink" href="#many-samples-a-sampling-distribution" title="Link to this heading">¶</a></h2>
+<p>Take 1000 samples of size 50 and compute the proportion red in each:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">samples</span> <span class="o">=</span> <span class="n">rep_slice_sample</span><span class="p">(</span><span class="n">bowl</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="n">prop_red</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">samples</span>
+    <span class="o">.</span><span class="n">group_by</span><span class="p">(</span><span class="s2">&quot;replicate&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">agg</span><span class="p">((</span><span class="n">pl</span><span class="o">.</span><span class="n">col</span><span class="p">(</span><span class="s2">&quot;color&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;red&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">&quot;prop_red&quot;</span><span class="p">))</span>
+<span class="p">)</span>
+<span class="n">prop_red</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>That <code class="docutils literal notranslate"><span class="pre">prop_red</span></code> column is a <strong>sampling distribution</strong>. Visualize its spread the
+same way you would any distribution (see <a class="reference internal" href="confidence-intervals.html"><span class="doc">Bootstrapping &amp; confidence intervals</span></a> for building
+one with the inference pipeline instead).</p>
+</section>
+<section id="with-vs-without-replacement">
+<h2>With vs. without replacement<a class="headerlink" href="#with-vs-without-replacement" title="Link to this heading">¶</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">rep_slice_sample</span></code> samples <strong>without</strong> replacement by default (like dealing from a
+deck). Pass <code class="docutils literal notranslate"><span class="pre">replace=True</span></code> for bootstrap-style resampling:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">rep_slice_sample</span><span class="p">(</span><span class="n">bowl</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="tactile-samples">
+<h2>Tactile samples<a class="headerlink" href="#tactile-samples" title="Link to this heading">¶</a></h2>
+<p>The hand-collected counterparts are bundled too:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">md</span><span class="o">.</span><span class="n">load_tactile_prop_red</span><span class="p">()</span>   <span class="c1"># 33 groups&#39; samples of 50 balls</span>
+</pre></div>
+</div>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><code class="docutils literal notranslate"><span class="pre">rep_sample_n</span></code> is an alias of <code class="docutils literal notranslate"><span class="pre">rep_slice_sample</span></code> (the older infer name). Both are
+documented in the <a class="reference internal" href="../api.html"><span class="doc">API reference</span></a>.</p>
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+          <section id="theory-based-inference">
+<h1>Theory-based inference<a class="headerlink" href="#theory-based-inference" title="Link to this heading">¶</a></h1>
+<p>Alongside the simulation grammar, <code class="docutils literal notranslate"><span class="pre">moderndive</span></code> ships tidy wrappers for the
+classical (formula-based) tests and a way to draw the theoretical distributions —
+mirroring R <code class="docutils literal notranslate"><span class="pre">infer</span></code>’s <code class="docutils literal notranslate"><span class="pre">t_test</span></code>, <code class="docutils literal notranslate"><span class="pre">prop_test</span></code>, <code class="docutils literal notranslate"><span class="pre">chisq_test</span></code>, and <code class="docutils literal notranslate"><span class="pre">assume()</span></code>.</p>
+<section id="one-line-tests">
+<h2>One-line tests<a class="headerlink" href="#one-line-tests" title="Link to this heading">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">t_test</span><span class="p">,</span> <span class="n">prop_test</span><span class="p">,</span> <span class="n">chisq_test</span>
+
+<span class="c1"># One-sample t-test</span>
+<span class="n">age</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_age_at_marriage</span><span class="p">()</span>
+<span class="n">t_test</span><span class="p">(</span><span class="n">age</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">,</span> <span class="n">mu</span><span class="o">=</span><span class="mi">23</span><span class="p">)</span>
+<span class="c1"># → statistic, t_df, p_value, alternative, estimate, lower_ci, upper_ci</span>
+
+<span class="c1"># Two-sample (Welch) t-test</span>
+<span class="n">movies</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_movies_sample</span><span class="p">()</span>
+<span class="n">t_test</span><span class="p">(</span><span class="n">movies</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;rating ~ genre&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;Action&quot;</span><span class="p">,</span> <span class="s2">&quot;Romance&quot;</span><span class="p">))</span>
+
+<span class="c1"># Two-proportion z-test</span>
+<span class="n">yawn</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_mythbusters_yawn</span><span class="p">()</span>
+<span class="n">prop_test</span><span class="p">(</span><span class="n">yawn</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;yawn ~ group&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;yes&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;seed&quot;</span><span class="p">,</span> <span class="s2">&quot;control&quot;</span><span class="p">))</span>
+
+<span class="c1"># Chi-squared test of independence</span>
+<span class="n">chisq_test</span><span class="p">(</span><span class="n">yawn</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;yawn ~ group&quot;</span><span class="p">)</span>
+<span class="c1"># → statistic, chisq_df, p_value</span>
+</pre></div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">t_stat</span></code> and <code class="docutils literal notranslate"><span class="pre">chisq_stat</span></code> return just the test statistic if that’s all you need.</p>
+</section>
+<section id="theoretical-distributions-with-assume">
+<h2>Theoretical distributions with <code class="docutils literal notranslate"><span class="pre">assume()</span></code><a class="headerlink" href="#theoretical-distributions-with-assume" title="Link to this heading">¶</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">assume()</span></code> defines a theoretical sampling distribution you can visualize and use
+for p-values without simulating:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">assume</span>
+
+<span class="c1"># t-distribution with 10 degrees of freedom</span>
+<span class="n">t_dist</span> <span class="o">=</span> <span class="n">assume</span><span class="p">(</span><span class="s2">&quot;t&quot;</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">t_dist</span><span class="o">.</span><span class="n">get_p_value</span><span class="p">(</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>     <span class="c1"># one-sided p-value</span>
+
+<span class="c1"># plotly by default; engine=&quot;plotnine&quot; for ggplot output</span>
+<span class="n">t_dist</span><span class="o">.</span><span class="n">visualize</span><span class="p">()</span>
+<span class="n">t_dist</span><span class="o">.</span><span class="n">visualize</span><span class="p">(</span><span class="n">engine</span><span class="o">=</span><span class="s2">&quot;plotnine&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Supported distributions: <code class="docutils literal notranslate"><span class="pre">&quot;t&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;z&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;F&quot;</span></code> (pass <code class="docutils literal notranslate"><span class="pre">df=(df1,</span> <span class="pre">df2)</span></code>), and
+<code class="docutils literal notranslate"><span class="pre">&quot;Chisq&quot;</span></code>.</p>
+</section>
+<section id="overlaying-theory-on-a-simulation">
+<h2>Overlaying theory on a simulation<a class="headerlink" href="#overlaying-theory-on-a-simulation" title="Link to this heading">¶</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">visualize(...,</span> <span class="pre">method=...)</span></code> can show the simulation histogram, the theoretical
+curve, or both:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span><span class="p">,</span> <span class="n">visualize</span>
+
+<span class="n">boot</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">specify</span><span class="p">(</span><span class="n">age</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+
+<span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">)</span>          <span class="c1"># histogram + normal-approximation curve</span>
+<span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;theoretical&quot;</span><span class="p">)</span>   <span class="c1"># just the curve</span>
+</pre></div>
+</div>
+</section>
+<section id="population-standard-deviation">
+<h2>Population standard deviation<a class="headerlink" href="#population-standard-deviation" title="Link to this heading">¶</a></h2>
+<p>A small helper that divides by <code class="docutils literal notranslate"><span class="pre">n</span></code> (not <code class="docutils literal notranslate"><span class="pre">n</span> <span class="pre">−</span> <span class="pre">1</span></code>):</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">pop_sd</span>
+
+<span class="n">pop_sd</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>   <span class="c1"># → 1.414…</span>
+</pre></div>
+</div>
+</section>
+</section>
+
+        </article>
+      </div>
+      <footer>
+        
+        <div class="related-pages">
+          <a class="next-page" href="plotting.html">
+              <div class="page-info">
+                <div class="context">
+                  <span>Next</span>
+                </div>
+                <div class="title">Plotting: plotly &amp; plotnine</div>
+              </div>
+              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
+            </a>
+          <a class="prev-page" href="regression.html">
+              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
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+                
+                <div class="title">Regression</div>
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+          <div class="left-details">
+            <div class="copyright">
+                Copyright &#169; 2026, ModernDive
+            </div>
+            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
+            
+            <a href="https://github.com/pradyunsg/furo">Furo</a>
+            
+          </div>
+          <div class="right-details">
+            
+          </div>
+        </div>
+        
+      </footer>
+    </div>
+    <aside class="toc-drawer">
+      
+      
+      <div class="toc-sticky toc-scroll">
+        <div class="toc-title-container">
+          <span class="toc-title">
+            On this page
+          </span>
+        </div>
+        <div class="toc-tree-container">
+          <div class="toc-tree">
+            <ul>
+<li><a class="reference internal" href="#">Theory-based inference</a><ul>
+<li><a class="reference internal" href="#one-line-tests">One-line tests</a></li>
+<li><a class="reference internal" href="#theoretical-distributions-with-assume">Theoretical distributions with <code class="docutils literal notranslate"><span class="pre">assume()</span></code></a></li>
+<li><a class="reference internal" href="#overlaying-theory-on-a-simulation">Overlaying theory on a simulation</a></li>
+<li><a class="reference internal" href="#population-standard-deviation">Population standard deviation</a></li>
+</ul>
+</li>
+</ul>
+
+          </div>
+        </div>
+      </div>
+      
+      
+    </aside>
+  </div>
+</div><script src="../_static/documentation_options.js?v=01f34227"></script>
+    <script src="../_static/doctools.js?v=fd6eb6e6"></script>
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+    <script src="../_static/copybutton.js?v=f281be69"></script>
+    </body>
+</html>
\ No newline at end of file
diff --git a/doc/_build/html/index.html b/doc/_build/html/index.html
index dfce7f1..dd5e83a 100644
--- a/doc/_build/html/index.html
+++ b/doc/_build/html/index.html
@@ -3,7 +3,7 @@
   <head><meta charset="utf-8">
     <meta name="viewport" content="width=device-width,initial-scale=1">
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
-<link rel="index" title="Index" href="genindex.html"><link rel="search" title="Search" href="search.html"><link rel="next" title="API reference" href="api.html">
+<link rel="index" title="Index" href="genindex.html"><link rel="search" title="Search" href="search.html"><link rel="next" title="Getting started" href="getting-started.html">
         <link rel="prefetch" href="_static/moderndive-logo.png" as="image">
 
     <link rel="shortcut icon" href="_static/moderndive-logo.png"><!-- Generated with Sphinx 9.1.0 and Furo 2025.12.19 -->
@@ -195,9 +195,23 @@
   <input type="hidden" name="area" value="default">
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
-<ul class="current">
-<li class="toctree-l1 current current-page"><a class="current reference internal" href="#">moderndive (Python)</a></li>
+  <p class="caption" role="heading"><span class="caption-text">Get started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="getting-started.html">Getting started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="coming-from-r.html">Coming from R</a></li>
+<li class="toctree-l1"><a class="reference internal" href="datasets.html">Datasets</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guides/sampling.html">Sampling</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/hypothesis-testing.html">Hypothesis testing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/regression.html">Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/theory-based.html">Theory-based inference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/plotting.html">Plotting: plotly &amp; plotnine</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="api.html">API reference</a></li>
 </ul>
 
@@ -240,49 +254,106 @@
           <section id="moderndive-python">
 <h1>moderndive (Python)<a class="headerlink" href="#moderndive-python" title="Link to this heading">¶</a></h1>
 <p>The Python companion package for <strong>ModernDive: Statistical Inference via Data
-Science</strong> — a faithful port of the R <code class="docutils literal notranslate"><span class="pre">moderndive</span></code> and <code class="docutils literal notranslate"><span class="pre">infer</span></code> packages to a
-modern Python data-science stack (polars, plotnine, statsmodels).</p>
-<div class="toctree-wrapper compound">
-<p class="caption" role="heading"><span class="caption-text">Contents</span></p>
-<ul class="current">
-<li class="toctree-l1 current"><a class="current reference internal" href="#">moderndive (Python)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="api.html">API reference</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="api.html#inference-grammar">Inference grammar</a></li>
-<li class="toctree-l2"><a class="reference internal" href="api.html#getters-and-visualization">Getters and visualization</a></li>
-<li class="toctree-l2"><a class="reference internal" href="api.html#theory-based-tests">Theory-based tests</a></li>
-<li class="toctree-l2"><a class="reference internal" href="api.html#regression-helpers">Regression helpers</a></li>
-<li class="toctree-l2"><a class="reference internal" href="api.html#sampling-and-plots">Sampling and plots</a></li>
-<li class="toctree-l2"><a class="reference internal" href="api.html#datasets">Datasets</a></li>
+Science</strong> — a faithful port of the R <a class="reference external" href="https://moderndive.github.io/moderndive/"><code class="docutils literal notranslate"><span class="pre">moderndive</span></code></a>
+and <a class="reference external" href="https://infer.tidymodels.org"><code class="docutils literal notranslate"><span class="pre">infer</span></code></a> packages to a modern Python
+data-science stack (<a class="reference external" href="https://pola.rs">polars</a>, <a class="reference external" href="https://plotly.com/python/">plotly</a>,
+<a class="reference external" href="https://plotnine.org">plotnine</a>, <a class="reference external" href="https://www.statsmodels.org">statsmodels</a>).</p>
+<p>If you teach or learn statistical inference the <em>tidy</em> way — <code class="docutils literal notranslate"><span class="pre">specify</span></code> →
+<code class="docutils literal notranslate"><span class="pre">hypothesize</span></code> → <code class="docutils literal notranslate"><span class="pre">generate</span></code> → <code class="docutils literal notranslate"><span class="pre">calculate</span></code> → <code class="docutils literal notranslate"><span class="pre">visualize</span></code> — this package gives you
+the same grammar in Python, plus regression helpers and the book’s datasets.</p>
+<section id="why-moderndive">
+<h2>Why moderndive?<a class="headerlink" href="#why-moderndive" title="Link to this heading">¶</a></h2>
+<ul class="simple">
+<li><p><strong>A tidy inference grammar.</strong> Bootstrap confidence intervals and
+permutation/​simulation hypothesis tests read like sentences, mirroring R <code class="docutils literal notranslate"><span class="pre">infer</span></code>.</p></li>
+<li><p><strong>Regression helpers</strong> that return tidy tables: <code class="docutils literal notranslate"><span class="pre">get_regression_table</span></code>,
+<code class="docutils literal notranslate"><span class="pre">get_regression_points</span></code>, <code class="docutils literal notranslate"><span class="pre">get_regression_summaries</span></code>, <code class="docutils literal notranslate"><span class="pre">get_correlation</span></code>.</p></li>
+<li><p><strong>Dual-engine plots.</strong> Every plot takes <code class="docutils literal notranslate"><span class="pre">engine=&quot;plotly&quot;</span></code> (default, interactive)
+or <code class="docutils literal notranslate"><span class="pre">engine=&quot;plotnine&quot;</span></code> (grammar-of-graphics) — same code, your choice of output.</p></li>
+<li><p><strong>58 bundled datasets</strong> via <code class="docutils literal notranslate"><span class="pre">load_*()</span></code> loaders returning polars DataFrames.</p></li>
+<li><p><strong>polars-first</strong>, works with pandas too.</p></li>
 </ul>
-</li>
-</ul>
-</div>
+</section>
 <section id="installation">
 <h2>Installation<a class="headerlink" href="#installation" title="Link to this heading">¶</a></h2>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>moderndive
 </pre></div>
 </div>
+<p>Optional extra for saving plotly figures as static images (PNG/SVG):</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span><span class="s2">&quot;moderndive[image]&quot;</span><span class="w">   </span><span class="c1"># adds kaleido</span>
+</pre></div>
+</div>
 </section>
-<section id="quick-start">
-<h2>Quick start<a class="headerlink" href="#quick-start" title="Link to this heading">¶</a></h2>
+<section id="second-example">
+<h2>30-second example<a class="headerlink" href="#second-example" title="Link to this heading">¶</a></h2>
+<p>Did promotion decisions depend on the applicant’s (perceived) gender? Shuffle the
+labels 1000 times to build a null distribution and read off a p-value:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span><span class="p">,</span> <span class="n">get_p_value</span><span class="p">,</span> <span class="n">visualize</span><span class="p">,</span> <span class="n">shade_p_value</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span><span class="p">,</span> <span class="n">observe</span><span class="p">,</span> <span class="n">get_p_value</span><span class="p">,</span> <span class="n">visualize</span><span class="p">,</span> <span class="n">shade_p_value</span>
 
-<span class="n">spotify</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_spotify_metal_deephouse</span><span class="p">()</span>
+<span class="n">promotions</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_promotions</span><span class="p">()</span>
 
-<span class="n">obs</span> <span class="o">=</span> <span class="n">specify</span><span class="p">(</span><span class="n">spotify</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;popular_or_not ~ track_genre&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;popular&quot;</span><span class="p">)</span> \
-    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;metal&quot;</span><span class="p">,</span> <span class="s2">&quot;deep-house&quot;</span><span class="p">))</span>
+<span class="c1"># Observed difference in promotion rates (male − female)</span>
+<span class="n">obs</span> <span class="o">=</span> <span class="n">observe</span><span class="p">(</span>
+    <span class="n">promotions</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;decision ~ gender&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;promoted&quot;</span><span class="p">,</span>
+    <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;male&quot;</span><span class="p">,</span> <span class="s2">&quot;female&quot;</span><span class="p">),</span>
+<span class="p">)</span>
 
+<span class="c1"># Null distribution under &quot;gender doesn&#39;t matter&quot; (permutation)</span>
 <span class="n">null</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">spotify</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;popular_or_not ~ track_genre&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;popular&quot;</span><span class="p">)</span>
+    <span class="n">specify</span><span class="p">(</span><span class="n">promotions</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;decision ~ gender&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;promoted&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;independence&quot;</span><span class="p">)</span>
-    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;permute&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">76</span><span class="p">)</span>
-    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;metal&quot;</span><span class="p">,</span> <span class="s2">&quot;deep-house&quot;</span><span class="p">))</span>
+    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;permute&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;male&quot;</span><span class="p">,</span> <span class="s2">&quot;female&quot;</span><span class="p">))</span>
 <span class="p">)</span>
-<span class="n">get_p_value</span><span class="p">(</span><span class="n">null</span><span class="p">,</span> <span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>
+
+<span class="n">get_p_value</span><span class="p">(</span><span class="n">null</span><span class="p">,</span> <span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>   <span class="c1"># ≈ 0.025</span>
+
+<span class="c1"># Visualize it (interactive plotly by default; engine=&quot;plotnine&quot; also works)</span>
+<span class="n">visualize</span><span class="p">(</span><span class="n">null</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_p_value</span><span class="p">(</span><span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>See the <a class="reference internal" href="api.html"><span class="doc">API reference</span></a> for the full reference.</p>
+</section>
+<section id="where-to-next">
+<h2>Where to next<a class="headerlink" href="#where-to-next" title="Link to this heading">¶</a></h2>
+<ul class="simple">
+<li><p>New here? Start with <a class="reference internal" href="getting-started.html"><span class="doc">Getting started</span></a>.</p></li>
+<li><p>Coming from R? See <a class="reference internal" href="coming-from-r.html"><span class="doc">Coming from R</span></a> for a function-by-function map.</p></li>
+<li><p>Browse the task guides below, or jump to the <a class="reference internal" href="api.html"><span class="doc">API reference</span></a>.</p></li>
+</ul>
+<div class="toctree-wrapper compound">
+<p class="caption" role="heading"><span class="caption-text">Get started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="getting-started.html">Getting started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="coming-from-r.html">Coming from R</a></li>
+<li class="toctree-l1"><a class="reference internal" href="datasets.html">Datasets</a></li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guides/sampling.html">Sampling</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/hypothesis-testing.html">Hypothesis testing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/regression.html">Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/theory-based.html">Theory-based inference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/plotting.html">Plotting: plotly &amp; plotnine</a></li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="api.html">API reference</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="api.html#inference-grammar">Inference grammar</a></li>
+<li class="toctree-l2"><a class="reference internal" href="api.html#getters-and-visualization">Getters and visualization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="api.html#theory-based-tests">Theory-based tests</a></li>
+<li class="toctree-l2"><a class="reference internal" href="api.html#regression-helpers">Regression helpers</a></li>
+<li class="toctree-l2"><a class="reference internal" href="api.html#sampling-and-plots">Sampling and plots</a></li>
+<li class="toctree-l2"><a class="reference internal" href="api.html#datasets">Datasets</a></li>
+</ul>
+</li>
+</ul>
+</div>
 </section>
 </section>
 
@@ -291,12 +362,12 @@ <h2>Quick start<a class="headerlink" href="#quick-start" title="Link to this hea
       <footer>
         
         <div class="related-pages">
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-                <div class="title">API reference</div>
+                <div class="title">Getting started</div>
               </div>
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
             </a>
@@ -332,8 +403,12 @@ <h2>Quick start<a class="headerlink" href="#quick-start" title="Link to this hea
           <div class="toc-tree">
             <ul>
 <li><a class="reference internal" href="#">moderndive (Python)</a><ul>
+<li><a class="reference internal" href="#why-moderndive">Why moderndive?</a></li>
 <li><a class="reference internal" href="#installation">Installation</a></li>
-<li><a class="reference internal" href="#quick-start">Quick start</a></li>
+<li><a class="reference internal" href="#second-example">30-second example</a></li>
+<li><a class="reference internal" href="#where-to-next">Where to next</a><ul>
+</ul>
+</li>
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+  <p class="caption" role="heading"><span class="caption-text">Get started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="getting-started.html">Getting started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="coming-from-r.html">Coming from R</a></li>
+<li class="toctree-l1"><a class="reference internal" href="datasets.html">Datasets</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="guides/sampling.html">Sampling</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/hypothesis-testing.html">Hypothesis testing</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="guides/plotting.html">Plotting: plotly &amp; plotnine</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="index.html">moderndive (Python)</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="guides/confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/hypothesis-testing.html">Hypothesis testing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/regression.html">Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/theory-based.html">Theory-based inference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="guides/plotting.html">Plotting: plotly &amp; plotnine</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="index.html">moderndive (Python)</a></li>
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diff --git a/doc/coming-from-r.md b/doc/coming-from-r.md
new file mode 100644
index 0000000..fca9f7f
--- /dev/null
+++ b/doc/coming-from-r.md
@@ -0,0 +1,75 @@
+# Coming from R
+
+If you know the R `moderndive` and `infer` packages, this page maps the API to
+Python. The grammar is the same; the main differences are Python method-chaining
+(`.hypothesize()` instead of the `|>`/`%>%` pipe) and polars DataFrames.
+
+## The infer pipeline
+
+```r
+# R
+mtcars %>%
+  specify(response = mpg) %>%
+  hypothesize(null = "point", mu = 20) %>%
+  generate(reps = 1000, type = "bootstrap") %>%
+  calculate(stat = "mean")
+```
+
+```python
+# Python — verbs are methods on the returned objects
+(
+    specify(data, response="mpg")
+    .hypothesize(null="point", mu=20)
+    .generate(reps=1000, type="bootstrap", seed=1)
+    .calculate(stat="mean")
+)
+```
+
+`specify(formula="y ~ x")` works just like R's formula interface; `success=` marks
+the success level for categorical responses.
+
+## Function map
+
+| R (`infer` / `moderndive`)        | Python (`moderndive`)                                  |
+| --------------------------------- | ------------------------------------------------------ |
+| `specify()`                       | `specify()`                                            |
+| `hypothesize()` / `hypothesise()` | `.hypothesize()` / `.hypothesise()`                    |
+| `generate()`                      | `.generate()`                                          |
+| `calculate()`                     | `.calculate()`                                         |
+| `fit()`                           | `.fit()`                                               |
+| `assume()`                        | `assume()`                                             |
+| `observe()`                       | `observe()`                                            |
+| `get_p_value()` / `get_pvalue()`  | `get_p_value()` / `get_pvalue()`                       |
+| `get_confidence_interval()` / `get_ci()` | `get_confidence_interval()` / `get_ci()`        |
+| `visualize()` / `visualise()`     | `visualize()` / `visualise()`                          |
+| `shade_p_value()` / `shade_pvalue()` | `shade_p_value()` / `shade_pvalue()`                |
+| `shade_confidence_interval()` / `shade_ci()` | `shade_confidence_interval()` / `shade_ci()` |
+| `t_test()`, `prop_test()`, `chisq_test()` | same names                                     |
+| `t_stat()`, `chisq_stat()`        | same names                                             |
+| `rep_sample_n()` / `rep_slice_sample()` | `rep_sample_n()` / `rep_slice_sample()`          |
+| `get_regression_table()`          | `get_regression_table(model)`                          |
+| `get_regression_points()`         | `get_regression_points(model)`                         |
+| `get_regression_summaries()`      | `get_regression_summaries(model)`                      |
+| `get_correlation()`               | `get_correlation(data, "y ~ x")`                       |
+| `pop_sd()`                        | `pop_sd()`                                             |
+| `geom_parallel_slopes()`          | `geom_parallel_slopes()` (plotnine) / `gg_parallel_slopes(engine=...)` |
+| `geom_categorical_model()`        | `gg_categorical_model(engine=...)`                     |
+| `tidy_summary()`                  | `tidy_summary()`                                       |
+
+## Key differences
+
+- **Pipe → methods.** `x %>% hypothesize(...)` becomes `x.hypothesize(...)`.
+- **Models.** Where R passes an `lm()` object, Python passes a fitted
+  [statsmodels](https://www.statsmodels.org) model:
+  `smf.ols("y ~ x", data=df.to_pandas()).fit()`.
+- **Plotting engine.** R returns ggplot2; Python defaults to **plotly**
+  (interactive) with `engine="plotnine"` available everywhere. Plots compose with
+  `+` in both engines.
+- **DataFrames.** Inputs/outputs are polars; pass `.to_pandas()` when a downstream
+  tool needs pandas.
+- **Reproducibility.** Pass `seed=` to `generate()` (R uses `set.seed()`).
+
+## Same datasets
+
+Most R `moderndive`/`infer` datasets are bundled here under the same name —
+`load_promotions()`, `load_pennies()`, `load_gss()`, etc. See {doc}`datasets`.
diff --git a/doc/datasets.md b/doc/datasets.md
new file mode 100644
index 0000000..5b8c693
--- /dev/null
+++ b/doc/datasets.md
@@ -0,0 +1,48 @@
+# Datasets
+
+`moderndive` bundles 58 datasets (the R `moderndive` + `infer` data, plus a few
+derived tables). Each loads with `load_<name>()` and returns a polars DataFrame.
+
+```python
+import moderndive as md
+
+md.available_datasets()          # sorted list of every dataset name
+md.load_dataset("promotions")    # load by name (string)
+md.load_promotions()             # or via the generated loader
+```
+
+## By topic
+
+**Sampling & simulation**
+: `bowl`, `bowl_sample_1`, `bowl_samples`, `tactile_prop_red`,
+  `almonds_bowl`, `almonds_sample`, `almonds_sample_100`,
+  `pennies`, `pennies_sample`, `pennies_resamples`, `orig_pennies_sample`
+
+**Hypothesis testing & CIs**
+: `promotions`, `promotions_shuffled`, `mythbusters_yawn`, `movies_sample`,
+  `spotify_by_genre`, `spotify_52_original`, `spotify_52_shuffled`,
+  `spotify_metal_deephouse`, `offshore`, `age_at_marriage`, `zinc_tidy`,
+  `cle_sac`, `gss`
+
+**Regression**
+: `evals`, `saratoga_houses`, `house_prices`, `coffee_quality`, `coffee_ratings`,
+  `credit`, `MA_schools`, `un_member_states_2024`, `mario_kart_auction`,
+  `amazon_books`, `old_faithful_2024`
+
+**Flights & weather (nycflights23 / 13)**
+: `flights`, `weather`, `airlines`, `airports`, `planes`, `envoy_flights`,
+  `alaska_flights`, `early_january_weather`, `early_january_2023_weather`
+
+**Other example data**
+: `gapminder`, `gapminder_2007`, `drinks`, `airline_safety`, `dem_score`,
+  `us_births_1994_2003`, `steves_episodes`, `avocados`, `babies`, `ipf_lifts`,
+  `ev_charging`, `DD_vs_SB`, `ma_traffic_2020_vs_2019`, `mass_traffic_2020`
+
+## Tips
+
+- Every loader returns a polars DataFrame; call `.to_pandas()` if a function
+  (e.g. statsmodels) expects pandas.
+- Datetime columns (e.g. `time_hour` in the weather/flights data) are stored in
+  UTC.
+- `load_dataset("<name>")` raises a helpful `ValueError` listing valid names if
+  the dataset doesn't exist.
diff --git a/doc/getting-started.md b/doc/getting-started.md
new file mode 100644
index 0000000..2453b27
--- /dev/null
+++ b/doc/getting-started.md
@@ -0,0 +1,110 @@
+# Getting started
+
+This page walks through a complete analysis end to end, then points you at the
+task guides for more depth.
+
+## Install
+
+```bash
+pip install moderndive
+```
+
+`moderndive` returns [polars](https://pola.rs) DataFrames, but every function also
+accepts pandas DataFrames as input.
+
+## Load a dataset
+
+All datasets ship with the package and load with `load_<name>()`:
+
+```python
+import moderndive as md
+
+promotions = md.load_promotions()
+promotions.head()
+```
+
+```text
+shape: (5, 3)
+┌─────┬──────────┬────────┐
+│ id  ┆ decision ┆ gender │
+│ i64 ┆ str      ┆ str    │
+╞═════╪══════════╪════════╡
+│ 1   ┆ promoted ┆ male   │
+│ 2   ┆ promoted ┆ male   │
+│ …   ┆ …        ┆ …      │
+└─────┴──────────┴────────┘
+```
+
+List everything that's available with `md.available_datasets()` (58 datasets), and
+see {doc}`datasets` for a thematic tour.
+
+## A first summary
+
+`tidy_summary` gives a per-variable five-number summary (numeric) or counts
+(categorical):
+
+```python
+from moderndive import tidy_summary
+
+tidy_summary(md.load_almonds_sample_100(), columns=["weight"])
+```
+
+## The inference pipeline
+
+The core grammar mirrors R `infer`. You build a pipeline and read it like a
+sentence:
+
+```python
+from moderndive import specify, observe, get_p_value
+
+# 1. The observed statistic
+obs = observe(
+    promotions, formula="decision ~ gender", success="promoted",
+    stat="diff in props", order=("male", "female"),
+)
+
+# 2. A null distribution: specify → hypothesize → generate → calculate
+null = (
+    specify(promotions, formula="decision ~ gender", success="promoted")
+    .hypothesize(null="independence")
+    .generate(reps=1000, type="permute", seed=42)
+    .calculate(stat="diff in props", order=("male", "female"))
+)
+
+# 3. Summarize
+get_p_value(null, obs_stat=obs, direction="right")   # ≈ 0.025
+```
+
+Each verb has a focused guide: {doc}`guides/sampling`,
+{doc}`guides/confidence-intervals`, and {doc}`guides/hypothesis-testing`.
+
+## Visualizing — choose your engine
+
+Plots default to **plotly** (interactive). Pass `engine="plotnine"` for
+grammar-of-graphics output. The composition syntax is identical:
+
+```python
+from moderndive import visualize, shade_p_value
+
+# Interactive plotly figure
+visualize(null) + shade_p_value(obs_stat=obs, direction="right")
+
+# Same plot, plotnine
+visualize(null, engine="plotnine") + shade_p_value(obs_stat=obs, direction="right")
+```
+
+See {doc}`guides/plotting` for shading, confidence-interval overlays, theoretical
+overlays, and the regression-model plots.
+
+## Regression
+
+```python
+import statsmodels.formula.api as smf
+from moderndive import get_regression_table
+
+houses = md.load_saratoga_houses()
+model = smf.ols("price ~ living_area + bedrooms", data=houses.to_pandas()).fit()
+get_regression_table(model)
+```
+
+Full details in {doc}`guides/regression`.
diff --git a/doc/guides/confidence-intervals.md b/doc/guides/confidence-intervals.md
new file mode 100644
index 0000000..e02676e
--- /dev/null
+++ b/doc/guides/confidence-intervals.md
@@ -0,0 +1,88 @@
+# Bootstrapping & confidence intervals
+
+Resample your data with replacement to build a **bootstrap distribution**, then
+read a confidence interval off it.
+
+## A bootstrap distribution for a mean
+
+```python
+import moderndive as md
+from moderndive import specify, get_confidence_interval, visualize, shade_confidence_interval
+
+almonds = md.load_almonds_sample_100()
+
+boot = (
+    specify(almonds, response="weight")
+    .generate(reps=1000, type="bootstrap", seed=1)
+    .calculate(stat="mean")
+)
+```
+
+## Confidence intervals — three methods
+
+```python
+# Percentile method (default)
+get_confidence_interval(boot, level=0.95, type="percentile")
+# shape: (1, 2) → lower_ci ≈ 3.61, upper_ci ≈ 3.75
+
+# Standard-error method (needs the point estimate)
+point = float(specify(almonds, response="weight").calculate(stat="mean"))
+get_confidence_interval(boot, level=0.95, type="se", point_estimate=point)
+
+# Bias-corrected
+get_confidence_interval(boot, level=0.95, type="bias-corrected", point_estimate=point)
+```
+
+Distribution objects also expose the getter as a method:
+
+```python
+boot.get_confidence_interval(level=0.95, type="percentile")
+```
+
+## Visualize the interval
+
+```python
+ci = get_confidence_interval(boot, level=0.95, type="percentile")
+
+# plotly (default) — interactive
+visualize(boot) + shade_confidence_interval(ci)
+
+# plotnine
+visualize(boot, engine="plotnine") + shade_confidence_interval(ci)
+
+# one-call keyword form
+visualize(boot, shade_ci=ci)
+```
+
+## A confidence interval for a proportion
+
+```python
+import polars as pl
+
+mythbusters = md.load_mythbusters_yawn()
+
+boot_prop = (
+    specify(mythbusters, response="yawn", success="yes")
+    .generate(reps=1000, type="bootstrap", seed=1)
+    .calculate(stat="prop")
+)
+get_confidence_interval(boot_prop, level=0.95, type="percentile")
+```
+
+## A confidence interval for a difference
+
+Add an explanatory variable and an `order=` to compare two groups:
+
+```python
+boot_diff = (
+    specify(mythbusters, formula="yawn ~ group", success="yes")
+    .generate(reps=1000, type="bootstrap", seed=1)
+    .calculate(stat="diff in props", order=("seed", "control"))
+)
+boot_diff.get_confidence_interval(level=0.95)
+```
+
+```{seealso}
+For *theory-based* intervals (the formula-based `t` interval) see
+{doc}`theory-based`.
+```
diff --git a/doc/guides/hypothesis-testing.md b/doc/guides/hypothesis-testing.md
new file mode 100644
index 0000000..3a0d8c2
--- /dev/null
+++ b/doc/guides/hypothesis-testing.md
@@ -0,0 +1,88 @@
+# Hypothesis testing
+
+Hypothesis tests follow the same grammar as confidence intervals, with an added
+`hypothesize()` step that defines the null world, and `generate()` that simulates
+from it.
+
+## Two groups: a permutation test
+
+The classic "promotions" experiment — does the (perceived) gender on a résumé
+change the promotion rate?
+
+```python
+import moderndive as md
+from moderndive import specify, observe, get_p_value, visualize, shade_p_value
+
+promotions = md.load_promotions()
+
+# Observed difference in promotion proportions, male − female
+obs = observe(
+    promotions, formula="decision ~ gender", success="promoted",
+    stat="diff in props", order=("male", "female"),
+)   # ≈ 0.292
+
+# Null: gender is independent of the decision → permute the labels
+null = (
+    specify(promotions, formula="decision ~ gender", success="promoted")
+    .hypothesize(null="independence")
+    .generate(reps=1000, type="permute", seed=42)
+    .calculate(stat="diff in props", order=("male", "female"))
+)
+
+get_p_value(null, obs_stat=obs, direction="right")   # ≈ 0.025
+```
+
+## Shade the p-value
+
+```python
+visualize(null) + shade_p_value(obs_stat=obs, direction="right")
+```
+
+`direction` is one of `"right"`/`"greater"`, `"left"`/`"less"`, or `"two-sided"`.
+The two-sided shading mirrors the observed statistic about 0.
+
+## One mean / one proportion (point null)
+
+Use a `"point"` null with bootstrap resampling, supplying the hypothesized value:
+
+```python
+age = md.load_age_at_marriage()
+
+obs_t = observe(age, response="age", stat="t", null="point", mu=23)
+
+null_t = (
+    specify(age, response="age")
+    .hypothesize(null="point", mu=23)
+    .generate(reps=1000, type="bootstrap", seed=1)
+    .calculate(stat="t")
+)
+get_p_value(null_t, obs_stat=obs_t, direction="two-sided")
+```
+
+For a one-proportion test you can also *simulate* draws directly:
+
+```python
+import polars as pl
+
+coins = pl.DataFrame({"flip": ["heads"] * 30 + ["tails"] * 70})
+
+null_p = (
+    specify(coins, response="flip", success="heads")
+    .hypothesize(null="point", p=0.5)
+    .generate(reps=1000, type="draw", seed=1)   # "simulate" is an alias
+    .calculate(stat="prop")
+)
+```
+
+## Available statistics
+
+`calculate(stat=...)` supports the full infer vocabulary: `"mean"`, `"median"`,
+`"sum"`, `"sd"`, `"prop"`, `"count"`, `"diff in means"`, `"diff in medians"`,
+`"diff in props"`, `"ratio of means"`, `"ratio of props"`, `"odds ratio"`,
+`"slope"`, `"correlation"`, `"t"`, `"z"`, `"F"`, `"Chisq"`, plus any custom
+callable `stat(response, explanatory) -> float`.
+
+```{seealso}
+Prefer a one-line classical test? See the tidy wrappers in {doc}`theory-based`
+(`t_test`, `prop_test`, `chisq_test`).
+```
diff --git a/doc/guides/plotting.md b/doc/guides/plotting.md
new file mode 100644
index 0000000..1d5cf94
--- /dev/null
+++ b/doc/guides/plotting.md
@@ -0,0 +1,113 @@
+# Plotting: plotly & plotnine
+
+Every plotting function in `moderndive` takes an `engine=` argument:
+
+- `engine="plotly"` (**default**) — interactive figures (`plotly.graph_objects.Figure`).
+- `engine="plotnine"` — grammar-of-graphics figures (`plotnine.ggplot`).
+
+The composition syntax is identical across engines, so you can switch a whole
+analysis by changing one argument.
+
+## visualize() returns an InferPlot
+
+`visualize()` returns a small `InferPlot` wrapper that you compose with shading via
+`+` in **both** engines:
+
+```python
+import moderndive as md
+from moderndive import specify, get_confidence_interval, visualize, shade_confidence_interval
+
+boot = (
+    specify(md.load_almonds_sample_100(), response="weight")
+    .generate(reps=1000, type="bootstrap", seed=1)
+    .calculate(stat="mean")
+)
+ci = get_confidence_interval(boot, level=0.95, type="percentile")
+
+p = visualize(boot) + shade_confidence_interval(ci)   # plotly InferPlot
+p.figure          # the underlying plotly Figure
+p.show()          # display it
+```
+
+For the plotnine engine, the raw `ggplot` is available via `.gg`:
+
+```python
+g = visualize(boot, engine="plotnine") + shade_confidence_interval(ci)
+g.gg              # the underlying plotnine ggplot
+```
+
+### Keyword form
+
+If you'd rather not use `+`, pass the shading inline (handy for plotly):
+
+```python
+visualize(boot, shade_ci=ci)
+visualize(null, shade_pvalue={"obs_stat": obs, "direction": "right"})
+```
+
+## Shading p-values
+
+```python
+from moderndive import observe, shade_p_value
+
+promotions = md.load_promotions()
+obs = observe(promotions, formula="decision ~ gender", success="promoted",
+              stat="diff in props", order=("male", "female"))
+null = (
+    specify(promotions, formula="decision ~ gender", success="promoted")
+    .hypothesize(null="independence")
+    .generate(reps=1000, type="permute", seed=42)
+    .calculate(stat="diff in props", order=("male", "female"))
+)
+
+visualize(null) + shade_p_value(obs_stat=obs, direction="right")
+```
+
+## Simulation vs. theory overlays
+
+```python
+visualize(boot, method="simulation")    # histogram (default)
+visualize(boot, method="theoretical")   # normal-approximation curve
+visualize(boot, method="both")          # histogram + curve overlaid
+```
+
+## Faceted regression-coefficient plots
+
+`visualize_fit` shows one panel per term, and shading is **per-facet**:
+
+```python
+from moderndive.infer.viz import visualize_fit
+
+f = "price ~ living_area + bedrooms"
+houses = md.load_saratoga_houses()
+boot_fit = specify(houses, formula=f).generate(reps=1000, type="bootstrap", seed=1).fit()
+
+visualize_fit(boot_fit) + shade_confidence_interval(boot_fit.get_confidence_interval())
+```
+
+## Model plots
+
+```python
+from moderndive import gg_parallel_slopes, gg_categorical_model, pairplot
+
+evals = md.load_evals()
+gg_parallel_slopes(evals, response="score", explanatory="age", by="gender")
+gg_categorical_model(evals, response="score", explanatory="rank")
+
+# Scatterplot matrix (~ GGally::ggpairs)
+pairplot(md.load_coffee_quality(), columns=["total_cup_points", "aroma", "flavor"])
+```
+
+## Saving figures
+
+```python
+p = visualize(boot)            # plotly
+p.save("dist.html")            # interactive HTML (no extra deps)
+p.save("dist.png")             # static image — needs: pip install "moderndive[image]"
+
+g = visualize(boot, engine="plotnine")
+g.save("dist.png", width=6, height=4, dpi=150)
+```
+
+`pairplot(..., engine="seaborn")` (alias `"plotnine"`) returns a Matplotlib figure
+if you prefer the seaborn-backed scatterplot matrix.
diff --git a/doc/guides/regression.md b/doc/guides/regression.md
new file mode 100644
index 0000000..b89f2b1
--- /dev/null
+++ b/doc/guides/regression.md
@@ -0,0 +1,111 @@
+# Regression
+
+The regression helpers turn a fitted [statsmodels](https://www.statsmodels.org)
+model into tidy polars tables — the analog of R `moderndive`'s
+`get_regression_table()` / `get_regression_points()` / `get_regression_summaries()`.
+
+Fit models with statsmodels' formula API, then tidy them:
+
+```python
+import statsmodels.formula.api as smf
+import moderndive as md
+from moderndive import (
+    get_regression_table, get_regression_points, get_regression_summaries, get_correlation,
+)
+
+houses = md.load_saratoga_houses()
+model = smf.ols("price ~ living_area + bedrooms", data=houses.to_pandas()).fit()
+```
+
+## Coefficient table (with confidence intervals)
+
+```python
+get_regression_table(model)
+```
+
+```text
+shape: (3, 7)
+┌─────────────┬───────────┬───────────┬───────────┬─────────┬────────────┬───────────┐
+│ term        ┆ estimate  ┆ std_error ┆ statistic ┆ p_value ┆ lower_ci   ┆ upper_ci  │
+╞═════════════╪═══════════╪═══════════╪═══════════╪═════════╪════════════╪═══════════╡
+│ intercept   ┆ 20986.094 ┆ 6816.251  ┆ 3.079     ┆ 0.002   ┆ 7611.128   ┆ 34361.06  │
+│ living_area ┆ 93.842    ┆ 3.109     ┆ 30.183    ┆ 0.0     ┆ 87.741     ┆ 99.943    │
+│ bedrooms    ┆ -7483.095 ┆ 2783.531  ┆ -2.688    ┆ 0.007   ┆ -12944.988 ┆ -2021.203 │
+└─────────────┴───────────┴───────────┴───────────┴─────────┴────────────┴───────────┘
+```
+
+Change the confidence level with `conf_level=` (e.g. `0.99`).
+
+## Fitted values & residuals
+
+```python
+get_regression_points(model).head()
+# columns: ID, price, living_area, bedrooms, price_hat, residual
+```
+
+## Model-fit summaries
+
+```python
+get_regression_summaries(model)
+# r_squared, adj_r_squared, mse, rmse, sigma, statistic (F), p_value, df, nobs
+```
+
+## Correlation
+
+```python
+get_correlation(houses, "price ~ living_area")   # ≈ 0.759
+# or: get_correlation(houses, x="living_area", y="price")
+```
+
+## Visualizing models
+
+Two ports of R `moderndive`'s ggplot helpers, both dual-engine:
+
+```python
+from moderndive import gg_parallel_slopes, gg_categorical_model
+
+evals = md.load_evals()
+
+# Parallel-slopes model: one common slope, a separate intercept per group
+gg_parallel_slopes(evals, response="score", explanatory="age", by="gender")            # plotly
+gg_parallel_slopes(evals, response="score", explanatory="age", by="gender",
+                   engine="plotnine")
+
+# Regression with a single categorical predictor
+gg_categorical_model(evals, response="score", explanatory="rank")
+```
+
+For the plotnine engine you can also drop the parallel-slopes lines onto your own
+`ggplot` with `geom_parallel_slopes()`.
+
+## Inference for regression coefficients
+
+`fit()` runs the regression on every bootstrap/permutation replicate, giving a
+distribution per coefficient. Pair it with `visualize_fit` and **per-facet**
+shading:
+
+```python
+from moderndive import specify
+from moderndive.infer.viz import visualize_fit
+from moderndive import shade_confidence_interval, shade_p_value
+
+f = "price ~ living_area + bedrooms"
+obs_fit = specify(houses, formula=f).fit()
+
+# Bootstrap distribution of each coefficient → per-term CIs
+boot_fit = specify(houses, formula=f).generate(reps=1000, type="bootstrap", seed=1).fit()
+boot_fit.get_confidence_interval(level=0.95)          # one row per term
+visualize_fit(boot_fit) + shade_confidence_interval(boot_fit.get_confidence_interval())
+
+# Null distribution → per-term p-values, each facet shaded at its own estimate
+null_fit = (
+    specify(houses, formula=f).hypothesize(null="independence")
+    .generate(reps=1000, type="permute", seed=1).fit()
+)
+null_fit.get_p_value(obs_stat=obs_fit, direction="two-sided")
+visualize_fit(null_fit) + shade_p_value(obs_stat=obs_fit, direction="two-sided")
+```
+
+Per-facet shading works in both engines: `shade_p_value` / `shade_confidence_interval`
+accept the observed `FitResult` or a term-keyed table, and each facet is shaded
+from its own value.
diff --git a/doc/guides/sampling.md b/doc/guides/sampling.md
new file mode 100644
index 0000000..cb7a7bc
--- /dev/null
+++ b/doc/guides/sampling.md
@@ -0,0 +1,67 @@
+# Sampling
+
+Sampling activities (the "bowl" of balls, tactile samples) are how ModernDive
+builds intuition for sampling variation. `rep_slice_sample` takes repeated samples
+and stacks them with a `replicate` column — the analog of R
+`moderndive::rep_slice_sample()` / `infer::rep_sample_n()`.
+
+## The bowl
+
+```python
+import moderndive as md
+import polars as pl
+
+bowl = md.load_bowl()      # 2400 red/white balls
+bowl.head()
+```
+
+## One virtual sample
+
+```python
+from moderndive import rep_slice_sample
+
+sample = rep_slice_sample(bowl, n=50, seed=1)
+# proportion red in this sample
+sample.select((pl.col("color") == "red").mean().alias("prop_red"))
+```
+
+## Many samples → a sampling distribution
+
+Take 1000 samples of size 50 and compute the proportion red in each:
+
+```python
+samples = rep_slice_sample(bowl, n=50, reps=1000, seed=1)
+
+prop_red = (
+    samples
+    .group_by("replicate")
+    .agg((pl.col("color") == "red").mean().alias("prop_red"))
+)
+prop_red.head()
+```
+
+That `prop_red` column is a **sampling distribution**. Visualize its spread the
+same way you would any distribution (see {doc}`confidence-intervals` for building
+one with the inference pipeline instead).
+
+## With vs. without replacement
+
+`rep_slice_sample` samples **without** replacement by default (like dealing from a
+deck). Pass `replace=True` for bootstrap-style resampling:
+
+```python
+rep_slice_sample(bowl, n=50, reps=1000, replace=True, seed=1)
+```
+
+## Tactile samples
+
+The hand-collected counterparts are bundled too:
+
+```python
+md.load_tactile_prop_red()   # 33 groups' samples of 50 balls
+```
+
+```{seealso}
+`rep_sample_n` is an alias of `rep_slice_sample` (the older infer name). Both are
+documented in the {doc}`../api`.
+```
diff --git a/doc/guides/theory-based.md b/doc/guides/theory-based.md
new file mode 100644
index 0000000..3bb59b0
--- /dev/null
+++ b/doc/guides/theory-based.md
@@ -0,0 +1,79 @@
+# Theory-based inference
+
+Alongside the simulation grammar, `moderndive` ships tidy wrappers for the
+classical (formula-based) tests and a way to draw the theoretical distributions —
+mirroring R `infer`'s `t_test`, `prop_test`, `chisq_test`, and `assume()`.
+
+## One-line tests
+
+```python
+import moderndive as md
+from moderndive import t_test, prop_test, chisq_test
+
+# One-sample t-test
+age = md.load_age_at_marriage()
+t_test(age, response="age", mu=23)
+# → statistic, t_df, p_value, alternative, estimate, lower_ci, upper_ci
+
+# Two-sample (Welch) t-test
+movies = md.load_movies_sample()
+t_test(movies, formula="rating ~ genre", order=("Action", "Romance"))
+
+# Two-proportion z-test
+yawn = md.load_mythbusters_yawn()
+prop_test(yawn, formula="yawn ~ group", success="yes", order=("seed", "control"))
+
+# Chi-squared test of independence
+chisq_test(yawn, formula="yawn ~ group")
+# → statistic, chisq_df, p_value
+```
+
+`t_stat` and `chisq_stat` return just the test statistic if that's all you need.
+
+## Theoretical distributions with `assume()`
+
+`assume()` defines a theoretical sampling distribution you can visualize and use
+for p-values without simulating:
+
+```python
+from moderndive import assume
+
+# t-distribution with 10 degrees of freedom
+t_dist = assume("t", df=10)
+t_dist.get_p_value(2.0, direction="right")     # one-sided p-value
+
+# plotly by default; engine="plotnine" for ggplot output
+t_dist.visualize()
+t_dist.visualize(engine="plotnine")
+```
+
+Supported distributions: `"t"`, `"z"`, `"F"` (pass `df=(df1, df2)`), and
+`"Chisq"`.
+
+## Overlaying theory on a simulation
+
+`visualize(..., method=...)` can show the simulation histogram, the theoretical
+curve, or both:
+
+```python
+from moderndive import specify, visualize
+
+boot = (
+    specify(age, response="age")
+    .generate(reps=1000, type="bootstrap", seed=1)
+    .calculate(stat="mean")
+)
+
+visualize(boot, method="both")          # histogram + normal-approximation curve
+visualize(boot, method="theoretical")   # just the curve
+```
+
+## Population standard deviation
+
+A small helper that divides by `n` (not `n − 1`):
+
+```python
+from moderndive import pop_sd
+
+pop_sd([1, 2, 3, 4, 5])   # → 1.414…
+```
diff --git a/doc/index.md b/doc/index.md
index 43ff464..516de35 100644
--- a/doc/index.md
+++ b/doc/index.md
@@ -1,16 +1,25 @@
 # moderndive (Python)
 
 The Python companion package for **ModernDive: Statistical Inference via Data
-Science** — a faithful port of the R `moderndive` and `infer` packages to a
-modern Python data-science stack (polars, plotnine, statsmodels).
+Science** — a faithful port of the R [`moderndive`](https://moderndive.github.io/moderndive/)
+and [`infer`](https://infer.tidymodels.org) packages to a modern Python
+data-science stack ([polars](https://pola.rs), [plotly](https://plotly.com/python/),
+[plotnine](https://plotnine.org), [statsmodels](https://www.statsmodels.org)).
 
-```{toctree}
-:maxdepth: 2
-:caption: Contents
+If you teach or learn statistical inference the *tidy* way — `specify` →
+`hypothesize` → `generate` → `calculate` → `visualize` — this package gives you
+the same grammar in Python, plus regression helpers and the book's datasets.
 
-self
-api
-```
+## Why moderndive?
+
+- **A tidy inference grammar.** Bootstrap confidence intervals and
+  permutation/​simulation hypothesis tests read like sentences, mirroring R `infer`.
+- **Regression helpers** that return tidy tables: `get_regression_table`,
+  `get_regression_points`, `get_regression_summaries`, `get_correlation`.
+- **Dual-engine plots.** Every plot takes `engine="plotly"` (default, interactive)
+  or `engine="plotnine"` (grammar-of-graphics) — same code, your choice of output.
+- **58 bundled datasets** via `load_*()` loaders returning polars DataFrames.
+- **polars-first**, works with pandas too.
 
 ## Installation
 
@@ -18,24 +27,73 @@ api
 pip install moderndive
 ```
 
-## Quick start
+Optional extra for saving plotly figures as static images (PNG/SVG):
+
+```bash
+pip install "moderndive[image]"   # adds kaleido
+```
+
+## 30-second example
+
+Did promotion decisions depend on the applicant's (perceived) gender? Shuffle the
+labels 1000 times to build a null distribution and read off a p-value:
 
 ```python
 import moderndive as md
-from moderndive import specify, get_p_value, visualize, shade_p_value
+from moderndive import specify, observe, get_p_value, visualize, shade_p_value
 
-spotify = md.load_spotify_metal_deephouse()
+promotions = md.load_promotions()
 
-obs = specify(spotify, formula="popular_or_not ~ track_genre", success="popular") \
-    .calculate(stat="diff in props", order=("metal", "deep-house"))
+# Observed difference in promotion rates (male − female)
+obs = observe(
+    promotions, formula="decision ~ gender", success="promoted",
+    stat="diff in props", order=("male", "female"),
+)
 
+# Null distribution under "gender doesn't matter" (permutation)
 null = (
-    specify(spotify, formula="popular_or_not ~ track_genre", success="popular")
+    specify(promotions, formula="decision ~ gender", success="promoted")
     .hypothesize(null="independence")
-    .generate(reps=1000, type="permute", seed=76)
-    .calculate(stat="diff in props", order=("metal", "deep-house"))
+    .generate(reps=1000, type="permute", seed=42)
+    .calculate(stat="diff in props", order=("male", "female"))
 )
-get_p_value(null, obs_stat=obs, direction="right")
+
+get_p_value(null, obs_stat=obs, direction="right")   # ≈ 0.025
+
+# Visualize it (interactive plotly by default; engine="plotnine" also works)
+visualize(null) + shade_p_value(obs_stat=obs, direction="right")
+```
+
+## Where to next
+
+- New here? Start with {doc}`getting-started`.
+- Coming from R? See {doc}`coming-from-r` for a function-by-function map.
+- Browse the task guides below, or jump to the {doc}`api`.
+
+```{toctree}
+:maxdepth: 1
+:caption: Get started
+
+getting-started
+coming-from-r
+datasets
+```
+
+```{toctree}
+:maxdepth: 1
+:caption: Guides
+
+guides/sampling
+guides/confidence-intervals
+guides/hypothesis-testing
+guides/regression
+guides/theory-based
+guides/plotting
 ```
 
-See the {doc}`api` for the full reference.
+```{toctree}
+:maxdepth: 2
+:caption: Reference
+
+api
+```

From f9557a8499570a0a89f403bff4bcfbc733d538cb Mon Sep 17 00:00:00 2001
From: Chester Ismay <chester.ismay@gmail.com>
Date: Fri, 19 Jun 2026 07:37:12 -0700
Subject: [PATCH 2/9] docs: use the corrected hex logo (bottom point) in the
 docs build

Pulls the fixed logo from the PR #10 branch so this docs branch doesn't ship the
old bottom-cropped hex regardless of PR merge order.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017CTL1QSTg1DmDUpqYuPEog
---
 doc/_build/html/.doctrees/environment.pickle | Bin 3498118 -> 3498282 bytes
 doc/_build/html/_static/moderndive-logo.png  | Bin 845850 -> 848889 bytes
 doc/_build/html/searchindex.js               |   2 +-
 doc/_static/moderndive-logo.png              | Bin 845850 -> 848889 bytes
 4 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/_build/html/.doctrees/environment.pickle b/doc/_build/html/.doctrees/environment.pickle
index d7a8559f7c7c92322472b6bfc3e358bb062ea440..d7a0d5a6fb1e317a6b2a0e09bd3fecfff832f761 100644
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diff --git a/doc/_build/html/_static/moderndive-logo.png b/doc/_build/html/_static/moderndive-logo.png
index 4c9ae700c0eb80789575fcdce24097a47f719e37..1c6c251624e8e61a5e96aa711ced9add23fbd702 100644
GIT binary patch
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From 0f32615c8ca134ca2c514e2709dcbfe619f7e7b5 Mon Sep 17 00:00:00 2001
From: Chester Ismay <chester.ismay@gmail.com>
Date: Fri, 19 Jun 2026 08:06:27 -0700
Subject: [PATCH 3/9] docs: execute examples (real outputs + plots), drop
 promotions/mtcars
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Switch the docs to myst-nb so every example runs at build time and its output
(DataFrames + plots) renders on the site:

- conf.py: myst_nb engine; execute {code-cell} blocks; prefer static image/png
  over plotly's heavy interactive html; strip kernel stderr.
- Plots render as static PNGs — plotnine via matplotlib, plotly via kaleido
  (renderer set to "png" in each page's hidden setup cell).
- doc extra: add myst-nb, ipykernel, kaleido. .readthedocs.yaml installs Chrome
  for kaleido (>=1.0 no longer bundles it) via a post_install job.
- Landing 30-second example now uses the spotify dataset (not promotions); the
  Coming-from-R pipeline example uses pennies (not mtcars), and its function map
  now lists only the genuine R->Python differences, noting the rest are identical.

Every page executes cleanly in the kernel and the build is warning-free under -W.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017CTL1QSTg1DmDUpqYuPEog
---
 .readthedocs.yaml                             |    5 +
 doc/_build/html/.doctrees/api.doctree         |  Bin 3764428 -> 3765443 bytes
 .../html/.doctrees/coming-from-r.doctree      |  Bin 32029 -> 24818 bytes
 doc/_build/html/.doctrees/datasets.doctree    |  Bin 19326 -> 24389 bytes
 doc/_build/html/.doctrees/environment.pickle  |  Bin 3498282 -> 3507939 bytes
 .../html/.doctrees/getting-started.doctree    |  Bin 15230 -> 30097 bytes
 .../guides/confidence-intervals.doctree       |  Bin 10716 -> 63512 bytes
 .../guides/hypothesis-testing.doctree         |  Bin 15466 -> 64540 bytes
 .../html/.doctrees/guides/plotting.doctree    |  Bin 15736 -> 356028 bytes
 .../html/.doctrees/guides/regression.doctree  |  Bin 16985 -> 140614 bytes
 .../html/.doctrees/guides/sampling.doctree    |  Bin 11059 -> 29363 bytes
 .../.doctrees/guides/theory-based.doctree     |  Bin 11598 -> 53331 bytes
 doc/_build/html/.doctrees/index.doctree       |  Bin 17546 -> 66991 bytes
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 doc/_build/html/_modules/index.html           |    1 +
 .../html/_modules/moderndive/correlation.html |   19 +-
 doc/_build/html/_modules/moderndive/data.html |   19 +-
 .../html/_modules/moderndive/infer/core.html  |   27 +-
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 .../html/_modules/moderndive/infer/viz.html   |  154 +-
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 .../html/_modules/moderndive/plots.html       |   19 +-
 .../_modules/moderndive/plots/models.html     |   19 +-
 .../html/_modules/moderndive/sampling.html    |   25 +-
 .../html/_modules/moderndive/theory.html      |   25 +-
 doc/_build/html/_sources/coming-from-r.md.txt |   78 +-
 doc/_build/html/_sources/datasets.md.txt      |   20 +-
 .../html/_sources/getting-started.md.txt      |   39 +-
 .../guides/confidence-intervals.md.txt        |   30 +-
 .../_sources/guides/hypothesis-testing.md.txt |   26 +-
 .../html/_sources/guides/plotting.md.txt      |  103 +-
 .../html/_sources/guides/regression.md.txt    |   42 +-
 .../html/_sources/guides/sampling.md.txt      |   28 +-
 .../html/_sources/guides/theory-based.md.txt  |   26 +-
 doc/_build/html/_sources/index.md.txt         |   49 +-
 ...17855267ff0bd372690045ca344a6b62be495e.css | 2449 +++++++++++++++++
 doc/_build/html/api.html                      |    1 +
 doc/_build/html/coming-from-r.html            |  141 +-
 doc/_build/html/datasets.html                 |   20 +-
 doc/_build/html/genindex.html                 |    1 +
 doc/_build/html/getting-started.html          |   84 +-
 .../html/guides/confidence-intervals.html     |   80 +-
 .../html/guides/hypothesis-testing.html       |   50 +-
 doc/_build/html/guides/plotting.html          |  165 +-
 doc/_build/html/guides/regression.html        |   97 +-
 doc/_build/html/guides/sampling.html          |   80 +-
 doc/_build/html/guides/theory-based.html      |   50 +-
 doc/_build/html/index.html                    |   69 +-
 doc/_build/html/py-modindex.html              |    1 +
 doc/_build/html/search.html                   |    1 +
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 doc/_build/jupyter_execute/datasets.ipynb     |  158 ++
 .../jupyter_execute/getting-started.ipynb     |  361 +++
 .../guides/confidence-intervals.ipynb         |  396 +++
 .../guides/hypothesis-testing.ipynb           |  304 ++
 .../jupyter_execute/guides/plotting.ipynb     |  752 +++++
 .../jupyter_execute/guides/regression.ipynb   |  565 ++++
 .../jupyter_execute/guides/sampling.ipynb     |  363 +++
 .../jupyter_execute/guides/theory-based.ipynb |  272 ++
 doc/_build/jupyter_execute/index.ipynb        |  275 ++
 doc/coming-from-r.md                          |   78 +-
 doc/conf.py                                   |   16 +-
 doc/datasets.md                               |   20 +-
 doc/getting-started.md                        |   39 +-
 doc/guides/confidence-intervals.md            |   30 +-
 doc/guides/hypothesis-testing.md              |   26 +-
 doc/guides/plotting.md                        |  103 +-
 doc/guides/regression.md                      |   42 +-
 doc/guides/sampling.md                        |   28 +-
 doc/guides/theory-based.md                    |   26 +-
 doc/index.md                                  |   49 +-
 pyproject.toml                                |    4 +-
 102 files changed, 7526 insertions(+), 539 deletions(-)
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diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 7154c30..7d91901 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -6,6 +6,11 @@ build:
   os: ubuntu-24.04
   tools:
     python: "3.12"
+  jobs:
+    # The docs execute {python} cells with myst-nb and render plotly figures as
+    # static PNGs via kaleido, which (>=1.0) needs a Chrome it doesn't bundle.
+    post_install:
+      - python -c "import kaleido; kaleido.get_chrome_sync()"
 
 sphinx:
   configuration: doc/conf.py
diff --git a/doc/_build/html/.doctrees/api.doctree b/doc/_build/html/.doctrees/api.doctree
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diff --git a/doc/_build/html/.doctrees/environment.pickle b/doc/_build/html/.doctrees/environment.pickle
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diff --git a/doc/_build/html/.doctrees/guides/confidence-intervals.doctree b/doc/_build/html/.doctrees/guides/confidence-intervals.doctree
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diff --git a/doc/_build/html/.doctrees/guides/hypothesis-testing.doctree b/doc/_build/html/.doctrees/guides/hypothesis-testing.doctree
index 251ce3cdc0a6de1f22cbfed6015132381244e6e1..1b0568732a56191b186b25d6918c02e555f81fb3 100644
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diff --git a/doc/_build/html/.doctrees/guides/regression.doctree b/doc/_build/html/.doctrees/guides/regression.doctree
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diff --git a/doc/_build/html/_modules/index.html b/doc/_build/html/_modules/index.html
index 43f426b..3da3475 100644
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+    <link rel="stylesheet" type="text/css" href="../_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
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diff --git a/doc/_build/html/_modules/moderndive/correlation.html b/doc/_build/html/_modules/moderndive/correlation.html
index 81b2ea9..2a558e2 100644
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+    <link rel="stylesheet" type="text/css" href="../../_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
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 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+  <p class="caption" role="heading"><span class="caption-text">Get started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../getting-started.html">Getting started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../coming-from-r.html">Coming from R</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../datasets.html">Datasets</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/sampling.html">Sampling</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/hypothesis-testing.html">Hypothesis testing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/regression.html">Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/theory-based.html">Theory-based inference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/plotting.html">Plotting: plotly &amp; plotnine</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../../index.html">moderndive (Python)</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../api.html">API reference</a></li>
 </ul>
 
diff --git a/doc/_build/html/_modules/moderndive/data.html b/doc/_build/html/_modules/moderndive/data.html
index 570e072..5fdde9e 100644
--- a/doc/_build/html/_modules/moderndive/data.html
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@@ -194,9 +195,23 @@
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 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+  <p class="caption" role="heading"><span class="caption-text">Get started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../getting-started.html">Getting started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../coming-from-r.html">Coming from R</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../datasets.html">Datasets</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/sampling.html">Sampling</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/hypothesis-testing.html">Hypothesis testing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/regression.html">Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/theory-based.html">Theory-based inference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/plotting.html">Plotting: plotly &amp; plotnine</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../../index.html">moderndive (Python)</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../api.html">API reference</a></li>
 </ul>
 
diff --git a/doc/_build/html/_modules/moderndive/infer/core.html b/doc/_build/html/_modules/moderndive/infer/core.html
index 8170b7c..811d484 100644
--- a/doc/_build/html/_modules/moderndive/infer/core.html
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@@ -10,6 +10,7 @@
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@@ -194,9 +195,23 @@
   <input type="hidden" name="area" value="default">
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+  <p class="caption" role="heading"><span class="caption-text">Get started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../getting-started.html">Getting started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../coming-from-r.html">Coming from R</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../datasets.html">Datasets</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/sampling.html">Sampling</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/hypothesis-testing.html">Hypothesis testing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/regression.html">Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/theory-based.html">Theory-based inference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/plotting.html">Plotting: plotly &amp; plotnine</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../index.html">moderndive (Python)</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../../api.html">API reference</a></li>
 </ul>
 
@@ -684,10 +699,14 @@ <h1>Source code for moderndive.infer.core</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">get_fit_p_value</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">obs_stat</span><span class="o">=</span><span class="n">obs_stat</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="n">direction</span><span class="p">)</span>
 
-    <span class="k">def</span><span class="w"> </span><span class="nf">visualize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bins</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">20</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">engine</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;plotly&quot;</span><span class="p">):</span>
+    <span class="k">def</span><span class="w"> </span><span class="nf">visualize</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">bins</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">20</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">engine</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;plotly&quot;</span><span class="p">,</span> <span class="n">shade_pvalue</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">shade_ci</span><span class="o">=</span><span class="kc">None</span>
+    <span class="p">):</span>
         <span class="kn">from</span><span class="w"> </span><span class="nn">.viz</span><span class="w"> </span><span class="kn">import</span> <span class="n">visualize_fit</span>
 
-        <span class="k">return</span> <span class="n">visualize_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="n">engine</span><span class="p">)</span></div>
+        <span class="k">return</span> <span class="n">visualize_fit</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="n">engine</span><span class="p">,</span> <span class="n">shade_pvalue</span><span class="o">=</span><span class="n">shade_pvalue</span><span class="p">,</span> <span class="n">shade_ci</span><span class="o">=</span><span class="n">shade_ci</span>
+        <span class="p">)</span></div>
 
 
 
diff --git a/doc/_build/html/_modules/moderndive/infer/intervals.html b/doc/_build/html/_modules/moderndive/infer/intervals.html
index ac684e8..431af48 100644
--- a/doc/_build/html/_modules/moderndive/infer/intervals.html
+++ b/doc/_build/html/_modules/moderndive/infer/intervals.html
@@ -3,12 +3,14 @@
   <head><meta charset="utf-8">
     <meta name="viewport" content="width=device-width,initial-scale=1">
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html"><link rel="search" title="Search" href="../../../search.html">
+        <link rel="prefetch" href="../../../_static/moderndive-logo.png" as="image">
 
-    <!-- Generated with Sphinx 9.1.0 and Furo 2025.12.19 -->
+    <link rel="shortcut icon" href="../../../_static/moderndive-logo.png"><!-- Generated with Sphinx 9.1.0 and Furo 2025.12.19 -->
         <title>moderndive.infer.intervals - moderndive 0.1.0</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="../../../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -181,6 +183,9 @@
     <div class="sidebar-container">
       
       <div class="sidebar-sticky"><a class="sidebar-brand" href="../../../index.html">
+  <div class="sidebar-logo-container">
+    <img class="sidebar-logo" src="../../../_static/moderndive-logo.png" alt="Logo"/>
+  </div>
   
   <span class="sidebar-brand-text">moderndive 0.1.0</span>
   
@@ -190,9 +195,23 @@
   <input type="hidden" name="area" value="default">
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+  <p class="caption" role="heading"><span class="caption-text">Get started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../getting-started.html">Getting started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../coming-from-r.html">Coming from R</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../datasets.html">Datasets</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/sampling.html">Sampling</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/hypothesis-testing.html">Hypothesis testing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/regression.html">Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/theory-based.html">Theory-based inference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/plotting.html">Plotting: plotly &amp; plotnine</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../index.html">moderndive (Python)</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../../api.html">API reference</a></li>
 </ul>
 
diff --git a/doc/_build/html/_modules/moderndive/infer/pvalue.html b/doc/_build/html/_modules/moderndive/infer/pvalue.html
index 92537c9..4d7d451 100644
--- a/doc/_build/html/_modules/moderndive/infer/pvalue.html
+++ b/doc/_build/html/_modules/moderndive/infer/pvalue.html
@@ -3,12 +3,14 @@
   <head><meta charset="utf-8">
     <meta name="viewport" content="width=device-width,initial-scale=1">
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html"><link rel="search" title="Search" href="../../../search.html">
+        <link rel="prefetch" href="../../../_static/moderndive-logo.png" as="image">
 
-    <!-- Generated with Sphinx 9.1.0 and Furo 2025.12.19 -->
+    <link rel="shortcut icon" href="../../../_static/moderndive-logo.png"><!-- Generated with Sphinx 9.1.0 and Furo 2025.12.19 -->
         <title>moderndive.infer.pvalue - moderndive 0.1.0</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="../../../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -181,6 +183,9 @@
     <div class="sidebar-container">
       
       <div class="sidebar-sticky"><a class="sidebar-brand" href="../../../index.html">
+  <div class="sidebar-logo-container">
+    <img class="sidebar-logo" src="../../../_static/moderndive-logo.png" alt="Logo"/>
+  </div>
   
   <span class="sidebar-brand-text">moderndive 0.1.0</span>
   
@@ -190,9 +195,23 @@
   <input type="hidden" name="area" value="default">
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+  <p class="caption" role="heading"><span class="caption-text">Get started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../getting-started.html">Getting started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../coming-from-r.html">Coming from R</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../datasets.html">Datasets</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/sampling.html">Sampling</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/hypothesis-testing.html">Hypothesis testing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/regression.html">Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/theory-based.html">Theory-based inference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/plotting.html">Plotting: plotly &amp; plotnine</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../index.html">moderndive (Python)</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../../api.html">API reference</a></li>
 </ul>
 
diff --git a/doc/_build/html/_modules/moderndive/infer/theoretical.html b/doc/_build/html/_modules/moderndive/infer/theoretical.html
index 1dc9654..ae218ab 100644
--- a/doc/_build/html/_modules/moderndive/infer/theoretical.html
+++ b/doc/_build/html/_modules/moderndive/infer/theoretical.html
@@ -10,6 +10,7 @@
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="../../../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -194,9 +195,23 @@
   <input type="hidden" name="area" value="default">
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+  <p class="caption" role="heading"><span class="caption-text">Get started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../getting-started.html">Getting started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../coming-from-r.html">Coming from R</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../datasets.html">Datasets</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/sampling.html">Sampling</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/hypothesis-testing.html">Hypothesis testing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/regression.html">Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/theory-based.html">Theory-based inference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/plotting.html">Plotting: plotly &amp; plotnine</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../index.html">moderndive (Python)</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../../api.html">API reference</a></li>
 </ul>
 
diff --git a/doc/_build/html/_modules/moderndive/infer/viz.html b/doc/_build/html/_modules/moderndive/infer/viz.html
index a4e985f..89d5e12 100644
--- a/doc/_build/html/_modules/moderndive/infer/viz.html
+++ b/doc/_build/html/_modules/moderndive/infer/viz.html
@@ -10,6 +10,7 @@
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="../../../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -194,9 +195,23 @@
   <input type="hidden" name="area" value="default">
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+  <p class="caption" role="heading"><span class="caption-text">Get started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../getting-started.html">Getting started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../coming-from-r.html">Coming from R</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../datasets.html">Datasets</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/sampling.html">Sampling</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/hypothesis-testing.html">Hypothesis testing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/regression.html">Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/theory-based.html">Theory-based inference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/plotting.html">Plotting: plotly &amp; plotnine</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../index.html">moderndive (Python)</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../../api.html">API reference</a></li>
 </ul>
 
@@ -273,7 +288,14 @@ <h1>Source code for moderndive.infer.viz</h1><div class="highlight"><pre>
 
 <span class="nd">@dataclass</span><span class="p">(</span><span class="n">frozen</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="k">class</span><span class="w"> </span><span class="nc">ShadeSpec</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Engine-neutral description of a shading layer (p-value tail or CI).&quot;&quot;&quot;</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Engine-neutral description of a shading layer (p-value tail or CI).</span>
+
+<span class="sd">    For a single-panel plot the scalar fields are used. For a faceted</span>
+<span class="sd">    regression-fit plot (:func:`visualize_fit`), ``per_term`` holds one value per</span>
+<span class="sd">    term so each facet is shaded independently — a tuple of ``(term, payload)``</span>
+<span class="sd">    pairs where ``payload`` is the observed statistic (p-value) or a</span>
+<span class="sd">    ``(lower, upper)`` pair (confidence interval).</span>
+<span class="sd">    &quot;&quot;&quot;</span>
 
     <span class="n">kind</span><span class="p">:</span> <span class="nb">str</span>  <span class="c1"># &quot;p_value&quot; | &quot;confidence_interval&quot;</span>
     <span class="n">obs_stat</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
@@ -281,6 +303,7 @@ <h1>Source code for moderndive.infer.viz</h1><div class="highlight"><pre>
     <span class="n">lower</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
     <span class="n">upper</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
     <span class="n">color</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">per_term</span><span class="p">:</span> <span class="nb">tuple</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
 
 
 <span class="k">class</span><span class="w"> </span><span class="nc">InferPlot</span><span class="p">:</span>
@@ -291,11 +314,15 @@ <h1>Source code for moderndive.infer.viz</h1><div class="highlight"><pre>
 <span class="sd">    raw ``ggplot`` is also available via :attr:`gg`.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">figure</span><span class="p">,</span> <span class="n">engine</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">figure</span><span class="p">,</span> <span class="n">engine</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">terms</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">figure</span> <span class="o">=</span> <span class="n">figure</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">engine</span> <span class="o">=</span> <span class="n">engine</span>
+        <span class="c1"># Facet terms for a regression-fit plot (enables per-facet shading).</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">terms</span> <span class="o">=</span> <span class="n">terms</span>
 
     <span class="k">def</span><span class="w"> </span><span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">ShadeSpec</span><span class="p">)</span> <span class="ow">and</span> <span class="n">other</span><span class="o">.</span><span class="n">per_term</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_add_per_facet</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span> <span class="o">==</span> <span class="s2">&quot;plotnine&quot;</span><span class="p">:</span>
             <span class="kn">from</span><span class="w"> </span><span class="nn">.</span><span class="w"> </span><span class="kn">import</span> <span class="n">_plotnine</span> <span class="k">as</span> <span class="n">P</span>
 
@@ -305,19 +332,36 @@ <h1>Source code for moderndive.infer.viz</h1><div class="highlight"><pre>
                     <span class="k">if</span> <span class="n">other</span><span class="o">.</span><span class="n">kind</span> <span class="o">==</span> <span class="s2">&quot;p_value&quot;</span>
                     <span class="k">else</span> <span class="n">P</span><span class="o">.</span><span class="n">shade_ci_layers</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>
                 <span class="p">)</span>
-                <span class="k">return</span> <span class="n">InferPlot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">figure</span> <span class="o">+</span> <span class="n">layers</span><span class="p">,</span> <span class="s2">&quot;plotnine&quot;</span><span class="p">)</span>
+                <span class="k">return</span> <span class="n">InferPlot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">figure</span> <span class="o">+</span> <span class="n">layers</span><span class="p">,</span> <span class="s2">&quot;plotnine&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">terms</span><span class="p">)</span>
             <span class="c1"># Any other plotnine object/layer/list.</span>
-            <span class="k">return</span> <span class="n">InferPlot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">figure</span> <span class="o">+</span> <span class="n">other</span><span class="p">,</span> <span class="s2">&quot;plotnine&quot;</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">InferPlot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">figure</span> <span class="o">+</span> <span class="n">other</span><span class="p">,</span> <span class="s2">&quot;plotnine&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">terms</span><span class="p">)</span>
 
         <span class="kn">from</span><span class="w"> </span><span class="nn">.</span><span class="w"> </span><span class="kn">import</span> <span class="n">_plotly</span> <span class="k">as</span> <span class="n">PX</span>
 
         <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">ShadeSpec</span><span class="p">):</span>
-            <span class="k">return</span> <span class="n">InferPlot</span><span class="p">(</span><span class="n">PX</span><span class="o">.</span><span class="n">apply_shade_px</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">figure</span><span class="p">,</span> <span class="n">other</span><span class="p">),</span> <span class="s2">&quot;plotly&quot;</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">InferPlot</span><span class="p">(</span><span class="n">PX</span><span class="o">.</span><span class="n">apply_shade_px</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">figure</span><span class="p">,</span> <span class="n">other</span><span class="p">),</span> <span class="s2">&quot;plotly&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">terms</span><span class="p">)</span>
         <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
             <span class="s2">&quot;Only a ShadeSpec (from shade_p_value/shade_confidence_interval) can be &quot;</span>
             <span class="s2">&quot;added to a plotly InferPlot.&quot;</span>
         <span class="p">)</span>
 
+    <span class="k">def</span><span class="w"> </span><span class="nf">_add_per_facet</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">spec</span><span class="p">:</span> <span class="n">ShadeSpec</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Apply per-term shading to a faceted regression-fit plot (both engines).&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">terms</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="s2">&quot;Per-term shading requires a faceted fit plot from visualize_fit(); &quot;</span>
+                <span class="s2">&quot;pass a per-term obs_stat/endpoints (a FitResult or a term-keyed table).&quot;</span>
+            <span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span> <span class="o">==</span> <span class="s2">&quot;plotnine&quot;</span><span class="p">:</span>
+            <span class="kn">from</span><span class="w"> </span><span class="nn">.</span><span class="w"> </span><span class="kn">import</span> <span class="n">_plotnine</span> <span class="k">as</span> <span class="n">P</span>
+
+            <span class="k">return</span> <span class="n">InferPlot</span><span class="p">(</span>
+                <span class="n">P</span><span class="o">.</span><span class="n">apply_fit_shade_gg</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">figure</span><span class="p">,</span> <span class="n">spec</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">terms</span><span class="p">),</span> <span class="s2">&quot;plotnine&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">terms</span>
+            <span class="p">)</span>
+        <span class="kn">from</span><span class="w"> </span><span class="nn">.</span><span class="w"> </span><span class="kn">import</span> <span class="n">_plotly</span> <span class="k">as</span> <span class="n">PX</span>
+
+        <span class="k">return</span> <span class="n">InferPlot</span><span class="p">(</span><span class="n">PX</span><span class="o">.</span><span class="n">apply_fit_shade_px</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">figure</span><span class="p">,</span> <span class="n">spec</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">terms</span><span class="p">),</span> <span class="s2">&quot;plotly&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">terms</span><span class="p">)</span>
+
     <span class="nd">@property</span>
     <span class="k">def</span><span class="w"> </span><span class="nf">gg</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;The underlying plotnine ``ggplot`` (only when ``engine=&#39;plotnine&#39;``).&quot;&quot;&quot;</span>
@@ -341,6 +385,23 @@ <h1>Source code for moderndive.infer.viz</h1><div class="highlight"><pre>
             <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">figure</span><span class="o">.</span><span class="n">write_html</span><span class="p">(</span><span class="n">path_str</span><span class="p">)</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">figure</span><span class="o">.</span><span class="n">write_image</span><span class="p">(</span><span class="n">path_str</span><span class="p">)</span>
 
+    <span class="k">def</span><span class="w"> </span><span class="nf">_repr_mimebundle_</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">include</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">exclude</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Rich display for Jupyter/Quarto across both engines.</span>
+
+<span class="sd">        Both plotnine ``ggplot`` and plotly ``Figure`` expose</span>
+<span class="sd">        ``_repr_mimebundle_``, so we delegate to it and the wrapped figure renders</span>
+<span class="sd">        exactly as a bare figure would. We fall back to ``text/html`` and finally</span>
+<span class="sd">        to no output. This is required because ``_repr_html_`` alone returns</span>
+<span class="sd">        ``None`` for a ``ggplot`` — without this method, plotnine-engine</span>
+<span class="sd">        ``InferPlot``s render blank in notebooks and Quarto.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">fig</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">figure</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="s2">&quot;_repr_mimebundle_&quot;</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">fig</span><span class="o">.</span><span class="n">_repr_mimebundle_</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="n">include</span><span class="p">,</span> <span class="n">exclude</span><span class="o">=</span><span class="n">exclude</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="s2">&quot;_repr_html_&quot;</span><span class="p">):</span>
+            <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;text/html&quot;</span><span class="p">:</span> <span class="n">fig</span><span class="o">.</span><span class="n">_repr_html_</span><span class="p">()}</span>
+        <span class="k">return</span> <span class="kc">None</span>
+
     <span class="k">def</span><span class="w"> </span><span class="nf">_repr_html_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="n">fig</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">figure</span>
         <span class="k">return</span> <span class="n">fig</span><span class="o">.</span><span class="n">_repr_html_</span><span class="p">()</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="s2">&quot;_repr_html_&quot;</span><span class="p">)</span> <span class="k">else</span> <span class="kc">None</span>
@@ -402,16 +463,32 @@ <h1>Source code for moderndive.infer.viz</h1><div class="highlight"><pre>
 
 
 
-<span class="k">def</span><span class="w"> </span><span class="nf">visualize_fit</span><span class="p">(</span><span class="n">fit</span><span class="p">,</span> <span class="n">bins</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">20</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">engine</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;plotly&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">InferPlot</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Faceted histogram of a regression fit distribution, one panel per term.&quot;&quot;&quot;</span>
+<span class="k">def</span><span class="w"> </span><span class="nf">visualize_fit</span><span class="p">(</span>
+    <span class="n">fit</span><span class="p">,</span> <span class="n">bins</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">20</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">engine</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;plotly&quot;</span><span class="p">,</span> <span class="n">shade_pvalue</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">shade_ci</span><span class="o">=</span><span class="kc">None</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">InferPlot</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Faceted histogram of a regression fit distribution, one panel per term.</span>
+
+<span class="sd">    Pass ``shade_pvalue=``/``shade_ci=`` to shade each facet from per-term values</span>
+<span class="sd">    (a ``FitResult`` of observed estimates, or a term-keyed CI/p-value table), or</span>
+<span class="sd">    compose the same per-term :class:`ShadeSpec` with ``+``.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
     <span class="n">engine</span> <span class="o">=</span> <span class="n">C</span><span class="o">.</span><span class="n">resolve_engine</span><span class="p">(</span><span class="n">engine</span><span class="p">)</span>
+    <span class="n">terms</span> <span class="o">=</span> <span class="n">fit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s2">&quot;term&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">maintain_order</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">to_list</span><span class="p">()</span>
     <span class="k">if</span> <span class="n">engine</span> <span class="o">==</span> <span class="s2">&quot;plotnine&quot;</span><span class="p">:</span>
         <span class="kn">from</span><span class="w"> </span><span class="nn">.</span><span class="w"> </span><span class="kn">import</span> <span class="n">_plotnine</span> <span class="k">as</span> <span class="n">P</span>
 
-        <span class="k">return</span> <span class="n">InferPlot</span><span class="p">(</span><span class="n">P</span><span class="o">.</span><span class="n">visualize_fit_gg</span><span class="p">(</span><span class="n">fit</span><span class="p">,</span> <span class="n">bins</span><span class="p">),</span> <span class="n">engine</span><span class="p">)</span>
-    <span class="kn">from</span><span class="w"> </span><span class="nn">.</span><span class="w"> </span><span class="kn">import</span> <span class="n">_plotly</span> <span class="k">as</span> <span class="n">PX</span>
+        <span class="n">fig</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">visualize_fit_gg</span><span class="p">(</span><span class="n">fit</span><span class="p">,</span> <span class="n">bins</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="kn">from</span><span class="w"> </span><span class="nn">.</span><span class="w"> </span><span class="kn">import</span> <span class="n">_plotly</span> <span class="k">as</span> <span class="n">PX</span>
 
-    <span class="k">return</span> <span class="n">InferPlot</span><span class="p">(</span><span class="n">PX</span><span class="o">.</span><span class="n">visualize_fit_px</span><span class="p">(</span><span class="n">fit</span><span class="p">,</span> <span class="n">bins</span><span class="p">),</span> <span class="n">engine</span><span class="p">)</span>
+        <span class="n">fig</span> <span class="o">=</span> <span class="n">PX</span><span class="o">.</span><span class="n">visualize_fit_px</span><span class="p">(</span><span class="n">fit</span><span class="p">,</span> <span class="n">bins</span><span class="p">)</span>
+
+    <span class="n">plot</span> <span class="o">=</span> <span class="n">InferPlot</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">engine</span><span class="p">,</span> <span class="n">terms</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">shade_pvalue</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">plot</span> <span class="o">=</span> <span class="n">plot</span> <span class="o">+</span> <span class="n">_coerce_pvalue_spec</span><span class="p">(</span><span class="n">shade_pvalue</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">shade_ci</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">plot</span> <span class="o">=</span> <span class="n">plot</span> <span class="o">+</span> <span class="n">_coerce_ci_spec</span><span class="p">(</span><span class="n">shade_ci</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">plot</span>
 
 
 <span class="k">def</span><span class="w"> </span><span class="nf">visualize_theoretical</span><span class="p">(</span><span class="n">theoretical</span><span class="p">,</span> <span class="n">bins</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">engine</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;plotly&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">InferPlot</span><span class="p">:</span>
@@ -431,16 +508,52 @@ <h1>Source code for moderndive.infer.viz</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">InferPlot</span><span class="p">(</span><span class="n">PX</span><span class="o">.</span><span class="n">density_curve_px</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">density</span><span class="p">,</span> <span class="n">title</span><span class="p">),</span> <span class="n">engine</span><span class="p">)</span>
 
 
+<span class="k">def</span><span class="w"> </span><span class="nf">_per_term_obs</span><span class="p">(</span><span class="n">obs_stat</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span> <span class="o">|</span> <span class="kc">None</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Extract a ``{term: observed}`` mapping for per-facet p-value shading.</span>
+
+<span class="sd">    Accepts an observed ``FitResult`` (term/estimate), a term-keyed polars frame</span>
+<span class="sd">    (``term`` + ``estimate`` or ``stat``), or a dict. Returns ``None`` for a scalar.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span><span class="w"> </span><span class="nn">polars</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pl</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obs_stat</span><span class="p">,</span> <span class="n">pl</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="ow">and</span> <span class="s2">&quot;term&quot;</span> <span class="ow">in</span> <span class="n">obs_stat</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+        <span class="n">valcol</span> <span class="o">=</span> <span class="s2">&quot;estimate&quot;</span> <span class="k">if</span> <span class="s2">&quot;estimate&quot;</span> <span class="ow">in</span> <span class="n">obs_stat</span><span class="o">.</span><span class="n">columns</span> <span class="k">else</span> <span class="s2">&quot;stat&quot;</span>
+        <span class="k">return</span> <span class="p">{</span><span class="n">r</span><span class="p">[</span><span class="s2">&quot;term&quot;</span><span class="p">]:</span> <span class="nb">float</span><span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="n">valcol</span><span class="p">])</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">obs_stat</span><span class="o">.</span><span class="n">iter_rows</span><span class="p">(</span><span class="n">named</span><span class="o">=</span><span class="kc">True</span><span class="p">)}</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">obs_stat</span><span class="p">,</span> <span class="s2">&quot;data&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pl</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="ow">and</span> <span class="p">{</span><span class="s2">&quot;term&quot;</span><span class="p">,</span> <span class="s2">&quot;estimate&quot;</span><span class="p">}</span> <span class="o">&lt;=</span> <span class="nb">set</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="p">):</span>
+        <span class="k">return</span> <span class="p">{</span><span class="n">r</span><span class="p">[</span><span class="s2">&quot;term&quot;</span><span class="p">]:</span> <span class="nb">float</span><span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="s2">&quot;estimate&quot;</span><span class="p">])</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">iter_rows</span><span class="p">(</span><span class="n">named</span><span class="o">=</span><span class="kc">True</span><span class="p">)}</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obs_stat</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+        <span class="k">return</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="nb">float</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">obs_stat</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
+    <span class="k">return</span> <span class="kc">None</span>
+
+
+<span class="k">def</span><span class="w"> </span><span class="nf">_per_term_ci</span><span class="p">(</span><span class="n">endpoints</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span> <span class="o">|</span> <span class="kc">None</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Extract a ``{term: (lower, upper)}`` mapping for per-facet CI shading.&quot;&quot;&quot;</span>
+    <span class="kn">import</span><span class="w"> </span><span class="nn">polars</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pl</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">endpoints</span><span class="p">,</span> <span class="n">pl</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="ow">and</span> <span class="s2">&quot;term&quot;</span> <span class="ow">in</span> <span class="n">endpoints</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">{</span>
+            <span class="n">r</span><span class="p">[</span><span class="s2">&quot;term&quot;</span><span class="p">]:</span> <span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="s2">&quot;lower_ci&quot;</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="s2">&quot;upper_ci&quot;</span><span class="p">]))</span>
+            <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">endpoints</span><span class="o">.</span><span class="n">iter_rows</span><span class="p">(</span><span class="n">named</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="p">}</span>
+    <span class="k">return</span> <span class="kc">None</span>
+
+
 <div class="viewcode-block" id="shade_p_value">
 <a class="viewcode-back" href="../../../api.html#moderndive.shade_p_value">[docs]</a>
 <span class="k">def</span><span class="w"> </span><span class="nf">shade_p_value</span><span class="p">(</span><span class="n">obs_stat</span><span class="p">,</span> <span class="n">direction</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">color</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ShadeSpec</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;A p-value shading spec; add it to a ``visualize()`` plot with ``+``.</span>
 
-<span class="sd">    ``direction`` ∈ {right/greater, left/less, two-sided}.</span>
+<span class="sd">    ``direction`` ∈ {right/greater, left/less, two-sided}. For a faceted</span>
+<span class="sd">    :func:`visualize_fit` plot, pass a per-term ``obs_stat`` — an observed</span>
+<span class="sd">    ``FitResult``, a ``term``-keyed frame, or a dict — to shade each facet.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">ShadeSpec</span><span class="p">(</span>
-        <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;p_value&quot;</span><span class="p">,</span> <span class="n">obs_stat</span><span class="o">=</span><span class="nb">float</span><span class="p">(</span><span class="n">obs_stat</span><span class="p">),</span> <span class="n">direction</span><span class="o">=</span><span class="n">direction</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span>
-    <span class="p">)</span></div>
+    <span class="n">per</span> <span class="o">=</span> <span class="n">_per_term_obs</span><span class="p">(</span><span class="n">obs_stat</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">per</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">ShadeSpec</span><span class="p">(</span>
+            <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;p_value&quot;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="n">direction</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">per_term</span><span class="o">=</span><span class="nb">tuple</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">per</span><span class="o">.</span><span class="n">items</span><span class="p">()))</span>
+        <span class="p">)</span>
+    <span class="k">return</span> <span class="n">ShadeSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s2">&quot;p_value&quot;</span><span class="p">,</span> <span class="n">obs_stat</span><span class="o">=</span><span class="nb">float</span><span class="p">(</span><span class="n">obs_stat</span><span class="p">),</span> <span class="n">direction</span><span class="o">=</span><span class="n">direction</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">)</span></div>
 
 
 
@@ -449,8 +562,15 @@ <h1>Source code for moderndive.infer.viz</h1><div class="highlight"><pre>
 <span class="k">def</span><span class="w"> </span><span class="nf">shade_confidence_interval</span><span class="p">(</span><span class="n">endpoints</span><span class="p">,</span> <span class="n">color</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ShadeSpec</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;A confidence-interval shading spec; add it to a ``visualize()`` plot with ``+``.</span>
 
-<span class="sd">    ``endpoints`` is a CI DataFrame (``lower_ci``/``upper_ci``) or a ``(lower, upper)`` tuple.</span>
+<span class="sd">    ``endpoints`` is a CI DataFrame (``lower_ci``/``upper_ci``) or a ``(lower, upper)``</span>
+<span class="sd">    tuple. For a faceted :func:`visualize_fit` plot, pass a per-term CI table (with a</span>
+<span class="sd">    ``term`` column) to shade each facet from its own interval.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">per</span> <span class="o">=</span> <span class="n">_per_term_ci</span><span class="p">(</span><span class="n">endpoints</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">per</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">ShadeSpec</span><span class="p">(</span>
+            <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;confidence_interval&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">per_term</span><span class="o">=</span><span class="nb">tuple</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">per</span><span class="o">.</span><span class="n">items</span><span class="p">()))</span>
+        <span class="p">)</span>
     <span class="n">lower</span><span class="p">,</span> <span class="n">upper</span> <span class="o">=</span> <span class="n">C</span><span class="o">.</span><span class="n">ci_endpoints</span><span class="p">(</span><span class="n">endpoints</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">ShadeSpec</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s2">&quot;confidence_interval&quot;</span><span class="p">,</span> <span class="n">lower</span><span class="o">=</span><span class="n">lower</span><span class="p">,</span> <span class="n">upper</span><span class="o">=</span><span class="n">upper</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">)</span></div>
 
diff --git a/doc/_build/html/_modules/moderndive/infer/wrappers.html b/doc/_build/html/_modules/moderndive/infer/wrappers.html
index 2701959..793e932 100644
--- a/doc/_build/html/_modules/moderndive/infer/wrappers.html
+++ b/doc/_build/html/_modules/moderndive/infer/wrappers.html
@@ -3,12 +3,14 @@
   <head><meta charset="utf-8">
     <meta name="viewport" content="width=device-width,initial-scale=1">
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html"><link rel="search" title="Search" href="../../../search.html">
+        <link rel="prefetch" href="../../../_static/moderndive-logo.png" as="image">
 
-    <!-- Generated with Sphinx 9.1.0 and Furo 2025.12.19 -->
+    <link rel="shortcut icon" href="../../../_static/moderndive-logo.png"><!-- Generated with Sphinx 9.1.0 and Furo 2025.12.19 -->
         <title>moderndive.infer.wrappers - moderndive 0.1.0</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="../../../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -181,6 +183,9 @@
     <div class="sidebar-container">
       
       <div class="sidebar-sticky"><a class="sidebar-brand" href="../../../index.html">
+  <div class="sidebar-logo-container">
+    <img class="sidebar-logo" src="../../../_static/moderndive-logo.png" alt="Logo"/>
+  </div>
   
   <span class="sidebar-brand-text">moderndive 0.1.0</span>
   
@@ -190,9 +195,23 @@
   <input type="hidden" name="area" value="default">
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+  <p class="caption" role="heading"><span class="caption-text">Get started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../getting-started.html">Getting started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../coming-from-r.html">Coming from R</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../datasets.html">Datasets</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/sampling.html">Sampling</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/hypothesis-testing.html">Hypothesis testing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/regression.html">Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/theory-based.html">Theory-based inference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/plotting.html">Plotting: plotly &amp; plotnine</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../index.html">moderndive (Python)</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../../api.html">API reference</a></li>
 </ul>
 
diff --git a/doc/_build/html/_modules/moderndive/modeling.html b/doc/_build/html/_modules/moderndive/modeling.html
index ccebe3c..482acb1 100644
--- a/doc/_build/html/_modules/moderndive/modeling.html
+++ b/doc/_build/html/_modules/moderndive/modeling.html
@@ -10,6 +10,7 @@
       <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../../_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -194,9 +195,23 @@
   <input type="hidden" name="area" value="default">
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+  <p class="caption" role="heading"><span class="caption-text">Get started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../getting-started.html">Getting started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../coming-from-r.html">Coming from R</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../datasets.html">Datasets</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/sampling.html">Sampling</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/hypothesis-testing.html">Hypothesis testing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/regression.html">Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/theory-based.html">Theory-based inference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/plotting.html">Plotting: plotly &amp; plotnine</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../../index.html">moderndive (Python)</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../api.html">API reference</a></li>
 </ul>
 
diff --git a/doc/_build/html/_modules/moderndive/plots.html b/doc/_build/html/_modules/moderndive/plots.html
index 5640726..5ab75de 100644
--- a/doc/_build/html/_modules/moderndive/plots.html
+++ b/doc/_build/html/_modules/moderndive/plots.html
@@ -10,6 +10,7 @@
       <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../../_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -194,9 +195,23 @@
   <input type="hidden" name="area" value="default">
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+  <p class="caption" role="heading"><span class="caption-text">Get started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../getting-started.html">Getting started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../coming-from-r.html">Coming from R</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../datasets.html">Datasets</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/sampling.html">Sampling</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/hypothesis-testing.html">Hypothesis testing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/regression.html">Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/theory-based.html">Theory-based inference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/plotting.html">Plotting: plotly &amp; plotnine</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../../index.html">moderndive (Python)</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../api.html">API reference</a></li>
 </ul>
 
diff --git a/doc/_build/html/_modules/moderndive/plots/models.html b/doc/_build/html/_modules/moderndive/plots/models.html
index 292aa8e..77cf29d 100644
--- a/doc/_build/html/_modules/moderndive/plots/models.html
+++ b/doc/_build/html/_modules/moderndive/plots/models.html
@@ -10,6 +10,7 @@
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="../../../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -194,9 +195,23 @@
   <input type="hidden" name="area" value="default">
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+  <p class="caption" role="heading"><span class="caption-text">Get started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../getting-started.html">Getting started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../coming-from-r.html">Coming from R</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../datasets.html">Datasets</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/sampling.html">Sampling</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/hypothesis-testing.html">Hypothesis testing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/regression.html">Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/theory-based.html">Theory-based inference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../guides/plotting.html">Plotting: plotly &amp; plotnine</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../index.html">moderndive (Python)</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../../api.html">API reference</a></li>
 </ul>
 
diff --git a/doc/_build/html/_modules/moderndive/sampling.html b/doc/_build/html/_modules/moderndive/sampling.html
index 97df8ae..a05fe88 100644
--- a/doc/_build/html/_modules/moderndive/sampling.html
+++ b/doc/_build/html/_modules/moderndive/sampling.html
@@ -3,12 +3,14 @@
   <head><meta charset="utf-8">
     <meta name="viewport" content="width=device-width,initial-scale=1">
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../genindex.html"><link rel="search" title="Search" href="../../search.html">
+        <link rel="prefetch" href="../../_static/moderndive-logo.png" as="image">
 
-    <!-- Generated with Sphinx 9.1.0 and Furo 2025.12.19 -->
+    <link rel="shortcut icon" href="../../_static/moderndive-logo.png"><!-- Generated with Sphinx 9.1.0 and Furo 2025.12.19 -->
         <title>moderndive.sampling - moderndive 0.1.0</title>
       <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../../_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -181,6 +183,9 @@
     <div class="sidebar-container">
       
       <div class="sidebar-sticky"><a class="sidebar-brand" href="../../index.html">
+  <div class="sidebar-logo-container">
+    <img class="sidebar-logo" src="../../_static/moderndive-logo.png" alt="Logo"/>
+  </div>
   
   <span class="sidebar-brand-text">moderndive 0.1.0</span>
   
@@ -190,9 +195,23 @@
   <input type="hidden" name="area" value="default">
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+  <p class="caption" role="heading"><span class="caption-text">Get started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../getting-started.html">Getting started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../coming-from-r.html">Coming from R</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../datasets.html">Datasets</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/sampling.html">Sampling</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/hypothesis-testing.html">Hypothesis testing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/regression.html">Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/theory-based.html">Theory-based inference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/plotting.html">Plotting: plotly &amp; plotnine</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../../index.html">moderndive (Python)</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../api.html">API reference</a></li>
 </ul>
 
diff --git a/doc/_build/html/_modules/moderndive/theory.html b/doc/_build/html/_modules/moderndive/theory.html
index 0fbe872..c153d41 100644
--- a/doc/_build/html/_modules/moderndive/theory.html
+++ b/doc/_build/html/_modules/moderndive/theory.html
@@ -3,12 +3,14 @@
   <head><meta charset="utf-8">
     <meta name="viewport" content="width=device-width,initial-scale=1">
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../genindex.html"><link rel="search" title="Search" href="../../search.html">
+        <link rel="prefetch" href="../../_static/moderndive-logo.png" as="image">
 
-    <!-- Generated with Sphinx 9.1.0 and Furo 2025.12.19 -->
+    <link rel="shortcut icon" href="../../_static/moderndive-logo.png"><!-- Generated with Sphinx 9.1.0 and Furo 2025.12.19 -->
         <title>moderndive.theory - moderndive 0.1.0</title>
       <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../../_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="../../_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -181,6 +183,9 @@
     <div class="sidebar-container">
       
       <div class="sidebar-sticky"><a class="sidebar-brand" href="../../index.html">
+  <div class="sidebar-logo-container">
+    <img class="sidebar-logo" src="../../_static/moderndive-logo.png" alt="Logo"/>
+  </div>
   
   <span class="sidebar-brand-text">moderndive 0.1.0</span>
   
@@ -190,9 +195,23 @@
   <input type="hidden" name="area" value="default">
 </form>
 <div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
-  <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+  <p class="caption" role="heading"><span class="caption-text">Get started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../getting-started.html">Getting started</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../coming-from-r.html">Coming from R</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../datasets.html">Datasets</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Guides</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/sampling.html">Sampling</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/confidence-intervals.html">Bootstrapping &amp; confidence intervals</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/hypothesis-testing.html">Hypothesis testing</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/regression.html">Regression</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/theory-based.html">Theory-based inference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../guides/plotting.html">Plotting: plotly &amp; plotnine</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../../index.html">moderndive (Python)</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../api.html">API reference</a></li>
 </ul>
 
diff --git a/doc/_build/html/_sources/coming-from-r.md.txt b/doc/_build/html/_sources/coming-from-r.md.txt
index fca9f7f..510cc69 100644
--- a/doc/_build/html/_sources/coming-from-r.md.txt
+++ b/doc/_build/html/_sources/coming-from-r.md.txt
@@ -8,9 +8,9 @@ Python. The grammar is the same; the main differences are Python method-chaining
 
 ```r
 # R
-mtcars %>%
-  specify(response = mpg) %>%
-  hypothesize(null = "point", mu = 20) %>%
+pennies %>%
+  specify(response = year) %>%
+  hypothesize(null = "point", mu = 1995) %>%
   generate(reps = 1000, type = "bootstrap") %>%
   calculate(stat = "mean")
 ```
@@ -18,8 +18,8 @@ mtcars %>%
 ```python
 # Python — verbs are methods on the returned objects
 (
-    specify(data, response="mpg")
-    .hypothesize(null="point", mu=20)
+    specify(md.load_pennies(), response="year")
+    .hypothesize(null="point", mu=1995)
     .generate(reps=1000, type="bootstrap", seed=1)
     .calculate(stat="mean")
 )
@@ -28,48 +28,40 @@ mtcars %>%
 `specify(formula="y ~ x")` works just like R's formula interface; `success=` marks
 the success level for categorical responses.
 
-## Function map
+## Most names are identical
 
-| R (`infer` / `moderndive`)        | Python (`moderndive`)                                  |
-| --------------------------------- | ------------------------------------------------------ |
-| `specify()`                       | `specify()`                                            |
-| `hypothesize()` / `hypothesise()` | `.hypothesize()` / `.hypothesise()`                    |
-| `generate()`                      | `.generate()`                                          |
-| `calculate()`                     | `.calculate()`                                         |
-| `fit()`                           | `.fit()`                                               |
-| `assume()`                        | `assume()`                                             |
-| `observe()`                       | `observe()`                                            |
-| `get_p_value()` / `get_pvalue()`  | `get_p_value()` / `get_pvalue()`                       |
-| `get_confidence_interval()` / `get_ci()` | `get_confidence_interval()` / `get_ci()`        |
-| `visualize()` / `visualise()`     | `visualize()` / `visualise()`                          |
-| `shade_p_value()` / `shade_pvalue()` | `shade_p_value()` / `shade_pvalue()`                |
-| `shade_confidence_interval()` / `shade_ci()` | `shade_confidence_interval()` / `shade_ci()` |
-| `t_test()`, `prop_test()`, `chisq_test()` | same names                                     |
-| `t_stat()`, `chisq_stat()`        | same names                                             |
-| `rep_sample_n()` / `rep_slice_sample()` | `rep_sample_n()` / `rep_slice_sample()`          |
-| `get_regression_table()`          | `get_regression_table(model)`                          |
-| `get_regression_points()`         | `get_regression_points(model)`                         |
-| `get_regression_summaries()`      | `get_regression_summaries(model)`                      |
-| `get_correlation()`               | `get_correlation(data, "y ~ x")`                       |
-| `pop_sd()`                        | `pop_sd()`                                             |
-| `geom_parallel_slopes()`          | `geom_parallel_slopes()` (plotnine) / `gg_parallel_slopes(engine=...)` |
-| `geom_categorical_model()`        | `gg_categorical_model(engine=...)`                     |
-| `tidy_summary()`                  | `tidy_summary()`                                       |
+The overwhelming majority of functions keep **the same name** (including the
+British-spelling and short-form aliases). They're called just as in R — as
+methods on the pipeline where applicable:
 
-## Key differences
+> `specify`, `hypothesize`/`hypothesise`, `generate`, `calculate`, `fit`,
+> `assume`, `observe`, `get_p_value`/`get_pvalue`,
+> `get_confidence_interval`/`get_ci`, `visualize`/`visualise`,
+> `shade_p_value`/`shade_pvalue`, `shade_confidence_interval`/`shade_ci`,
+> `t_test`, `prop_test`, `chisq_test`, `t_stat`, `chisq_stat`,
+> `rep_sample_n`/`rep_slice_sample`, `get_regression_table`,
+> `get_regression_points`, `get_regression_summaries`, `get_correlation`,
+> `pop_sd`, `tidy_summary`, `geom_parallel_slopes`.
 
-- **Pipe → methods.** `x %>% hypothesize(...)` becomes `x.hypothesize(...)`.
-- **Models.** Where R passes an `lm()` object, Python passes a fitted
-  [statsmodels](https://www.statsmodels.org) model:
-  `smf.ols("y ~ x", data=df.to_pandas()).fit()`.
-- **Plotting engine.** R returns ggplot2; Python defaults to **plotly**
-  (interactive) with `engine="plotnine"` available everywhere. Plots compose with
-  `+` in both engines.
-- **DataFrames.** Inputs/outputs are polars; pass `.to_pandas()` when a downstream
-  tool needs pandas.
-- **Reproducibility.** Pass `seed=` to `generate()` (R uses `set.seed()`).
+## What's actually different
+
+| R | Python | Why |
+| --- | --- | --- |
+| `x %>% f(...)` / `x \|> f(...)` | `x.f(...)` (method chaining) | no pipe operator in Python |
+| `geom_categorical_model()` | `gg_categorical_model(engine=...)` | renamed to match `gg_parallel_slopes` |
+| ggplot2 `geom_*` layers | plotly by default; `engine="plotnine"` for ggplot-style | dual-engine plotting |
+| `lm(y ~ x, data)` object | a fitted **statsmodels** model: `smf.ols("y ~ x", data=df.to_pandas()).fit()` | regression backend |
+| `get_correlation(df, y ~ x)` | `get_correlation(df, "y ~ x")` *or* `get_correlation(df, x="x", y="y")` | formula passed as a string |
+
+## Other things to know
+
+- **Plots compose with `+`** in both engines, and default to **plotly**
+  (interactive); pass `engine="plotnine"` anywhere for ggplot-style output.
+- **DataFrames** are polars in and out; pass `.to_pandas()` when a downstream tool
+  needs pandas.
+- **Reproducibility:** pass `seed=` to `generate()` (R uses `set.seed()`).
 
 ## Same datasets
 
 Most R `moderndive`/`infer` datasets are bundled here under the same name —
-`load_promotions()`, `load_pennies()`, `load_gss()`, etc. See {doc}`datasets`.
+`load_pennies()`, `load_promotions()`, `load_gss()`, etc. See {doc}`datasets`.
diff --git a/doc/_build/html/_sources/datasets.md.txt b/doc/_build/html/_sources/datasets.md.txt
index 5b8c693..ed19957 100644
--- a/doc/_build/html/_sources/datasets.md.txt
+++ b/doc/_build/html/_sources/datasets.md.txt
@@ -1,9 +1,27 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  name: python3
+---
+
+```{code-cell} python
+:tags: [remove-input]
+import matplotlib
+matplotlib.use("Agg")
+import plotly.io as pio
+pio.renderers.default = "png"
+```
+
 # Datasets
 
 `moderndive` bundles 58 datasets (the R `moderndive` + `infer` data, plus a few
 derived tables). Each loads with `load_<name>()` and returns a polars DataFrame.
 
-```python
+```{code-cell} python
 import moderndive as md
 
 md.available_datasets()          # sorted list of every dataset name
diff --git a/doc/_build/html/_sources/getting-started.md.txt b/doc/_build/html/_sources/getting-started.md.txt
index 2453b27..2a3e65f 100644
--- a/doc/_build/html/_sources/getting-started.md.txt
+++ b/doc/_build/html/_sources/getting-started.md.txt
@@ -1,3 +1,21 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  name: python3
+---
+
+```{code-cell} python
+:tags: [remove-input]
+import matplotlib
+matplotlib.use("Agg")
+import plotly.io as pio
+pio.renderers.default = "png"
+```
+
 # Getting started
 
 This page walks through a complete analysis end to end, then points you at the
@@ -16,24 +34,13 @@ accepts pandas DataFrames as input.
 
 All datasets ship with the package and load with `load_<name>()`:
 
-```python
+```{code-cell} python
 import moderndive as md
 
 promotions = md.load_promotions()
 promotions.head()
 ```
 
-```text
-shape: (5, 3)
-┌─────┬──────────┬────────┐
-│ id  ┆ decision ┆ gender │
-│ i64 ┆ str      ┆ str    │
-╞═════╪══════════╪════════╡
-│ 1   ┆ promoted ┆ male   │
-│ 2   ┆ promoted ┆ male   │
-│ …   ┆ …        ┆ …      │
-└─────┴──────────┴────────┘
-```
 
 List everything that's available with `md.available_datasets()` (58 datasets), and
 see {doc}`datasets` for a thematic tour.
@@ -43,7 +50,7 @@ see {doc}`datasets` for a thematic tour.
 `tidy_summary` gives a per-variable five-number summary (numeric) or counts
 (categorical):
 
-```python
+```{code-cell} python
 from moderndive import tidy_summary
 
 tidy_summary(md.load_almonds_sample_100(), columns=["weight"])
@@ -54,7 +61,7 @@ tidy_summary(md.load_almonds_sample_100(), columns=["weight"])
 The core grammar mirrors R `infer`. You build a pipeline and read it like a
 sentence:
 
-```python
+```{code-cell} python
 from moderndive import specify, observe, get_p_value
 
 # 1. The observed statistic
@@ -83,7 +90,7 @@ Each verb has a focused guide: {doc}`guides/sampling`,
 Plots default to **plotly** (interactive). Pass `engine="plotnine"` for
 grammar-of-graphics output. The composition syntax is identical:
 
-```python
+```{code-cell} python
 from moderndive import visualize, shade_p_value
 
 # Interactive plotly figure
@@ -98,7 +105,7 @@ overlays, and the regression-model plots.
 
 ## Regression
 
-```python
+```{code-cell} python
 import statsmodels.formula.api as smf
 from moderndive import get_regression_table
 
diff --git a/doc/_build/html/_sources/guides/confidence-intervals.md.txt b/doc/_build/html/_sources/guides/confidence-intervals.md.txt
index e02676e..b507a51 100644
--- a/doc/_build/html/_sources/guides/confidence-intervals.md.txt
+++ b/doc/_build/html/_sources/guides/confidence-intervals.md.txt
@@ -1,3 +1,21 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  name: python3
+---
+
+```{code-cell} python
+:tags: [remove-input]
+import matplotlib
+matplotlib.use("Agg")
+import plotly.io as pio
+pio.renderers.default = "png"
+```
+
 # Bootstrapping & confidence intervals
 
 Resample your data with replacement to build a **bootstrap distribution**, then
@@ -5,7 +23,7 @@ read a confidence interval off it.
 
 ## A bootstrap distribution for a mean
 
-```python
+```{code-cell} python
 import moderndive as md
 from moderndive import specify, get_confidence_interval, visualize, shade_confidence_interval
 
@@ -20,7 +38,7 @@ boot = (
 
 ## Confidence intervals — three methods
 
-```python
+```{code-cell} python
 # Percentile method (default)
 get_confidence_interval(boot, level=0.95, type="percentile")
 # shape: (1, 2) → lower_ci ≈ 3.61, upper_ci ≈ 3.75
@@ -35,13 +53,13 @@ get_confidence_interval(boot, level=0.95, type="bias-corrected", point_estimate=
 
 Distribution objects also expose the getter as a method:
 
-```python
+```{code-cell} python
 boot.get_confidence_interval(level=0.95, type="percentile")
 ```
 
 ## Visualize the interval
 
-```python
+```{code-cell} python
 ci = get_confidence_interval(boot, level=0.95, type="percentile")
 
 # plotly (default) — interactive
@@ -56,7 +74,7 @@ visualize(boot, shade_ci=ci)
 
 ## A confidence interval for a proportion
 
-```python
+```{code-cell} python
 import polars as pl
 
 mythbusters = md.load_mythbusters_yawn()
@@ -73,7 +91,7 @@ get_confidence_interval(boot_prop, level=0.95, type="percentile")
 
 Add an explanatory variable and an `order=` to compare two groups:
 
-```python
+```{code-cell} python
 boot_diff = (
     specify(mythbusters, formula="yawn ~ group", success="yes")
     .generate(reps=1000, type="bootstrap", seed=1)
diff --git a/doc/_build/html/_sources/guides/hypothesis-testing.md.txt b/doc/_build/html/_sources/guides/hypothesis-testing.md.txt
index 3a0d8c2..e57cdcc 100644
--- a/doc/_build/html/_sources/guides/hypothesis-testing.md.txt
+++ b/doc/_build/html/_sources/guides/hypothesis-testing.md.txt
@@ -1,3 +1,21 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  name: python3
+---
+
+```{code-cell} python
+:tags: [remove-input]
+import matplotlib
+matplotlib.use("Agg")
+import plotly.io as pio
+pio.renderers.default = "png"
+```
+
 # Hypothesis testing
 
 Hypothesis tests follow the same grammar as confidence intervals, with an added
@@ -9,7 +27,7 @@ from it.
 The classic "promotions" experiment — does the (perceived) gender on a résumé
 change the promotion rate?
 
-```python
+```{code-cell} python
 import moderndive as md
 from moderndive import specify, observe, get_p_value, visualize, shade_p_value
 
@@ -34,7 +52,7 @@ get_p_value(null, obs_stat=obs, direction="right")   # ≈ 0.025
 
 ## Shade the p-value
 
-```python
+```{code-cell} python
 visualize(null) + shade_p_value(obs_stat=obs, direction="right")
 ```
 
@@ -45,7 +63,7 @@ The two-sided shading mirrors the observed statistic about 0.
 
 Use a `"point"` null with bootstrap resampling, supplying the hypothesized value:
 
-```python
+```{code-cell} python
 age = md.load_age_at_marriage()
 
 obs_t = observe(age, response="age", stat="t", null="point", mu=23)
@@ -61,7 +79,7 @@ get_p_value(null_t, obs_stat=obs_t, direction="two-sided")
 
 For a one-proportion test you can also *simulate* draws directly:
 
-```python
+```{code-cell} python
 import polars as pl
 
 coins = pl.DataFrame({"flip": ["heads"] * 30 + ["tails"] * 70})
diff --git a/doc/_build/html/_sources/guides/plotting.md.txt b/doc/_build/html/_sources/guides/plotting.md.txt
index 1d5cf94..2492c09 100644
--- a/doc/_build/html/_sources/guides/plotting.md.txt
+++ b/doc/_build/html/_sources/guides/plotting.md.txt
@@ -1,3 +1,21 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  name: python3
+---
+
+```{code-cell} python
+:tags: [remove-input]
+import matplotlib
+matplotlib.use("Agg")
+import plotly.io as pio
+pio.renderers.default = "png"
+```
+
 # Plotting: plotly & plotnine
 
 Every plotting function in `moderndive` takes an `engine=` argument:
@@ -6,16 +24,15 @@ Every plotting function in `moderndive` takes an `engine=` argument:
 - `engine="plotnine"` — grammar-of-graphics figures (`plotnine.ggplot`).
 
 The composition syntax is identical across engines, so you can switch a whole
-analysis by changing one argument.
-
-## visualize() returns an InferPlot
+analysis by changing one argument. (In this documentation, plotly figures are
+shown as static images; in a notebook they're fully interactive.)
 
-`visualize()` returns a small `InferPlot` wrapper that you compose with shading via
-`+` in **both** engines:
-
-```python
+```{code-cell} python
 import moderndive as md
-from moderndive import specify, get_confidence_interval, visualize, shade_confidence_interval
+from moderndive import (
+    specify, observe, get_confidence_interval,
+    visualize, shade_confidence_interval, shade_p_value,
+)
 
 boot = (
     specify(md.load_almonds_sample_100(), response="weight")
@@ -23,51 +40,54 @@ boot = (
     .calculate(stat="mean")
 )
 ci = get_confidence_interval(boot, level=0.95, type="percentile")
-
-p = visualize(boot) + shade_confidence_interval(ci)   # plotly InferPlot
-p.figure          # the underlying plotly Figure
-p.show()          # display it
+ci
 ```
 
-For the plotnine engine, the raw `ggplot` is available via `.gg`:
+## visualize() returns an InferPlot
 
-```python
-g = visualize(boot, engine="plotnine") + shade_confidence_interval(ci)
-g.gg              # the underlying plotnine ggplot
+`visualize()` returns a small `InferPlot` wrapper that you compose with shading via
+`+` in **both** engines. The underlying figure is on `.figure` (and, for plotnine,
+the raw `ggplot` on `.gg`).
+
+```{code-cell} python
+visualize(boot) + shade_confidence_interval(ci)
+```
+
+```{code-cell} python
+# the same plot via the plotnine engine
+visualize(boot, engine="plotnine") + shade_confidence_interval(ci)
 ```
 
 ### Keyword form
 
 If you'd rather not use `+`, pass the shading inline (handy for plotly):
 
-```python
+```{code-cell} python
 visualize(boot, shade_ci=ci)
-visualize(null, shade_pvalue={"obs_stat": obs, "direction": "right"})
 ```
 
 ## Shading p-values
 
-```python
-from moderndive import observe, shade_p_value
-
-promotions = md.load_promotions()
-obs = observe(promotions, formula="decision ~ gender", success="promoted",
-              stat="diff in props", order=("male", "female"))
+```{code-cell} python
+spotify = md.load_spotify_metal_deephouse()
+obs = observe(spotify, formula="popular_or_not ~ track_genre", success="popular",
+              stat="diff in props", order=("metal", "deep-house"))
 null = (
-    specify(promotions, formula="decision ~ gender", success="promoted")
+    specify(spotify, formula="popular_or_not ~ track_genre", success="popular")
     .hypothesize(null="independence")
-    .generate(reps=1000, type="permute", seed=42)
-    .calculate(stat="diff in props", order=("male", "female"))
+    .generate(reps=1000, type="permute", seed=76)
+    .calculate(stat="diff in props", order=("metal", "deep-house"))
 )
-
 visualize(null) + shade_p_value(obs_stat=obs, direction="right")
 ```
 
 ## Simulation vs. theory overlays
 
-```python
-visualize(boot, method="simulation")    # histogram (default)
+```{code-cell} python
 visualize(boot, method="theoretical")   # normal-approximation curve
+```
+
+```{code-cell} python
 visualize(boot, method="both")          # histogram + curve overlaid
 ```
 
@@ -75,24 +95,35 @@ visualize(boot, method="both")          # histogram + curve overlaid
 
 `visualize_fit` shows one panel per term, and shading is **per-facet**:
 
-```python
+```{code-cell} python
 from moderndive.infer.viz import visualize_fit
 
-f = "price ~ living_area + bedrooms"
 houses = md.load_saratoga_houses()
-boot_fit = specify(houses, formula=f).generate(reps=1000, type="bootstrap", seed=1).fit()
-
+boot_fit = (
+    specify(houses, formula="price ~ living_area + bedrooms")
+    .generate(reps=1000, type="bootstrap", seed=1)
+    .fit()
+)
 visualize_fit(boot_fit) + shade_confidence_interval(boot_fit.get_confidence_interval())
 ```
 
 ## Model plots
 
-```python
-from moderndive import gg_parallel_slopes, gg_categorical_model, pairplot
+```{code-cell} python
+from moderndive import gg_parallel_slopes
 
 evals = md.load_evals()
 gg_parallel_slopes(evals, response="score", explanatory="age", by="gender")
+```
+
+```{code-cell} python
+from moderndive import gg_categorical_model
+
 gg_categorical_model(evals, response="score", explanatory="rank")
+```
+
+```{code-cell} python
+from moderndive import pairplot
 
 # Scatterplot matrix (~ GGally::ggpairs)
 pairplot(md.load_coffee_quality(), columns=["total_cup_points", "aroma", "flavor"])
diff --git a/doc/_build/html/_sources/guides/regression.md.txt b/doc/_build/html/_sources/guides/regression.md.txt
index b89f2b1..3971971 100644
--- a/doc/_build/html/_sources/guides/regression.md.txt
+++ b/doc/_build/html/_sources/guides/regression.md.txt
@@ -1,3 +1,21 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  name: python3
+---
+
+```{code-cell} python
+:tags: [remove-input]
+import matplotlib
+matplotlib.use("Agg")
+import plotly.io as pio
+pio.renderers.default = "png"
+```
+
 # Regression
 
 The regression helpers turn a fitted [statsmodels](https://www.statsmodels.org)
@@ -6,7 +24,7 @@ model into tidy polars tables — the analog of R `moderndive`'s
 
 Fit models with statsmodels' formula API, then tidy them:
 
-```python
+```{code-cell} python
 import statsmodels.formula.api as smf
 import moderndive as md
 from moderndive import (
@@ -19,40 +37,30 @@ model = smf.ols("price ~ living_area + bedrooms", data=houses.to_pandas()).fit()
 
 ## Coefficient table (with confidence intervals)
 
-```python
+```{code-cell} python
 get_regression_table(model)
 ```
 
-```text
-shape: (3, 7)
-┌─────────────┬───────────┬───────────┬───────────┬─────────┬────────────┬───────────┐
-│ term        ┆ estimate  ┆ std_error ┆ statistic ┆ p_value ┆ lower_ci   ┆ upper_ci  │
-╞═════════════╪═══════════╪═══════════╪═══════════╪═════════╪════════════╪═══════════╡
-│ intercept   ┆ 20986.094 ┆ 6816.251  ┆ 3.079     ┆ 0.002   ┆ 7611.128   ┆ 34361.06  │
-│ living_area ┆ 93.842    ┆ 3.109     ┆ 30.183    ┆ 0.0     ┆ 87.741     ┆ 99.943    │
-│ bedrooms    ┆ -7483.095 ┆ 2783.531  ┆ -2.688    ┆ 0.007   ┆ -12944.988 ┆ -2021.203 │
-└─────────────┴───────────┴───────────┴───────────┴─────────┴────────────┴───────────┘
-```
 
 Change the confidence level with `conf_level=` (e.g. `0.99`).
 
 ## Fitted values & residuals
 
-```python
+```{code-cell} python
 get_regression_points(model).head()
 # columns: ID, price, living_area, bedrooms, price_hat, residual
 ```
 
 ## Model-fit summaries
 
-```python
+```{code-cell} python
 get_regression_summaries(model)
 # r_squared, adj_r_squared, mse, rmse, sigma, statistic (F), p_value, df, nobs
 ```
 
 ## Correlation
 
-```python
+```{code-cell} python
 get_correlation(houses, "price ~ living_area")   # ≈ 0.759
 # or: get_correlation(houses, x="living_area", y="price")
 ```
@@ -61,7 +69,7 @@ get_correlation(houses, "price ~ living_area")   # ≈ 0.759
 
 Two ports of R `moderndive`'s ggplot helpers, both dual-engine:
 
-```python
+```{code-cell} python
 from moderndive import gg_parallel_slopes, gg_categorical_model
 
 evals = md.load_evals()
@@ -84,7 +92,7 @@ For the plotnine engine you can also drop the parallel-slopes lines onto your ow
 distribution per coefficient. Pair it with `visualize_fit` and **per-facet**
 shading:
 
-```python
+```{code-cell} python
 from moderndive import specify
 from moderndive.infer.viz import visualize_fit
 from moderndive import shade_confidence_interval, shade_p_value
diff --git a/doc/_build/html/_sources/guides/sampling.md.txt b/doc/_build/html/_sources/guides/sampling.md.txt
index cb7a7bc..6720779 100644
--- a/doc/_build/html/_sources/guides/sampling.md.txt
+++ b/doc/_build/html/_sources/guides/sampling.md.txt
@@ -1,3 +1,21 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  name: python3
+---
+
+```{code-cell} python
+:tags: [remove-input]
+import matplotlib
+matplotlib.use("Agg")
+import plotly.io as pio
+pio.renderers.default = "png"
+```
+
 # Sampling
 
 Sampling activities (the "bowl" of balls, tactile samples) are how ModernDive
@@ -7,7 +25,7 @@ and stacks them with a `replicate` column — the analog of R
 
 ## The bowl
 
-```python
+```{code-cell} python
 import moderndive as md
 import polars as pl
 
@@ -17,7 +35,7 @@ bowl.head()
 
 ## One virtual sample
 
-```python
+```{code-cell} python
 from moderndive import rep_slice_sample
 
 sample = rep_slice_sample(bowl, n=50, seed=1)
@@ -29,7 +47,7 @@ sample.select((pl.col("color") == "red").mean().alias("prop_red"))
 
 Take 1000 samples of size 50 and compute the proportion red in each:
 
-```python
+```{code-cell} python
 samples = rep_slice_sample(bowl, n=50, reps=1000, seed=1)
 
 prop_red = (
@@ -49,7 +67,7 @@ one with the inference pipeline instead).
 `rep_slice_sample` samples **without** replacement by default (like dealing from a
 deck). Pass `replace=True` for bootstrap-style resampling:
 
-```python
+```{code-cell} python
 rep_slice_sample(bowl, n=50, reps=1000, replace=True, seed=1)
 ```
 
@@ -57,7 +75,7 @@ rep_slice_sample(bowl, n=50, reps=1000, replace=True, seed=1)
 
 The hand-collected counterparts are bundled too:
 
-```python
+```{code-cell} python
 md.load_tactile_prop_red()   # 33 groups' samples of 50 balls
 ```
 
diff --git a/doc/_build/html/_sources/guides/theory-based.md.txt b/doc/_build/html/_sources/guides/theory-based.md.txt
index 3bb59b0..51f6247 100644
--- a/doc/_build/html/_sources/guides/theory-based.md.txt
+++ b/doc/_build/html/_sources/guides/theory-based.md.txt
@@ -1,3 +1,21 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  name: python3
+---
+
+```{code-cell} python
+:tags: [remove-input]
+import matplotlib
+matplotlib.use("Agg")
+import plotly.io as pio
+pio.renderers.default = "png"
+```
+
 # Theory-based inference
 
 Alongside the simulation grammar, `moderndive` ships tidy wrappers for the
@@ -6,7 +24,7 @@ mirroring R `infer`'s `t_test`, `prop_test`, `chisq_test`, and `assume()`.
 
 ## One-line tests
 
-```python
+```{code-cell} python
 import moderndive as md
 from moderndive import t_test, prop_test, chisq_test
 
@@ -35,7 +53,7 @@ chisq_test(yawn, formula="yawn ~ group")
 `assume()` defines a theoretical sampling distribution you can visualize and use
 for p-values without simulating:
 
-```python
+```{code-cell} python
 from moderndive import assume
 
 # t-distribution with 10 degrees of freedom
@@ -55,7 +73,7 @@ Supported distributions: `"t"`, `"z"`, `"F"` (pass `df=(df1, df2)`), and
 `visualize(..., method=...)` can show the simulation histogram, the theoretical
 curve, or both:
 
-```python
+```{code-cell} python
 from moderndive import specify, visualize
 
 boot = (
@@ -72,7 +90,7 @@ visualize(boot, method="theoretical")   # just the curve
 
 A small helper that divides by `n` (not `n − 1`):
 
-```python
+```{code-cell} python
 from moderndive import pop_sd
 
 pop_sd([1, 2, 3, 4, 5])   # → 1.414…
diff --git a/doc/_build/html/_sources/index.md.txt b/doc/_build/html/_sources/index.md.txt
index 516de35..dda2806 100644
--- a/doc/_build/html/_sources/index.md.txt
+++ b/doc/_build/html/_sources/index.md.txt
@@ -1,3 +1,21 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  name: python3
+---
+
+```{code-cell} python
+:tags: [remove-input]
+import matplotlib
+matplotlib.use("Agg")
+import plotly.io as pio
+pio.renderers.default = "png"  # render plotly figures as static images in the docs
+```
+
 # moderndive (Python)
 
 The Python companion package for **ModernDive: Statistical Inference via Data
@@ -35,31 +53,36 @@ pip install "moderndive[image]"   # adds kaleido
 
 ## 30-second example
 
-Did promotion decisions depend on the applicant's (perceived) gender? Shuffle the
-labels 1000 times to build a null distribution and read off a p-value:
+Are tracks more likely to be popular in *metal* than in *deep house*? Compute the
+observed difference in "popular" rates, then shuffle the genre labels 1000 times
+to build a null distribution and read off a p-value.
 
-```python
+```{code-cell} python
 import moderndive as md
 from moderndive import specify, observe, get_p_value, visualize, shade_p_value
 
-promotions = md.load_promotions()
+spotify = md.load_spotify_metal_deephouse()
 
-# Observed difference in promotion rates (male − female)
+# Observed difference in "popular" proportions (metal − deep-house)
 obs = observe(
-    promotions, formula="decision ~ gender", success="promoted",
-    stat="diff in props", order=("male", "female"),
+    spotify, formula="popular_or_not ~ track_genre", success="popular",
+    stat="diff in props", order=("metal", "deep-house"),
 )
+obs
+```
 
-# Null distribution under "gender doesn't matter" (permutation)
+```{code-cell} python
+# Null distribution under "genre doesn't matter" (permutation)
 null = (
-    specify(promotions, formula="decision ~ gender", success="promoted")
+    specify(spotify, formula="popular_or_not ~ track_genre", success="popular")
     .hypothesize(null="independence")
-    .generate(reps=1000, type="permute", seed=42)
-    .calculate(stat="diff in props", order=("male", "female"))
+    .generate(reps=1000, type="permute", seed=76)
+    .calculate(stat="diff in props", order=("metal", "deep-house"))
 )
+get_p_value(null, obs_stat=obs, direction="right")
+```
 
-get_p_value(null, obs_stat=obs, direction="right")   # ≈ 0.025
-
+```{code-cell} python
 # Visualize it (interactive plotly by default; engine="plotnine" also works)
 visualize(null) + shade_p_value(obs_stat=obs, direction="right")
 ```
diff --git a/doc/_build/html/_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css b/doc/_build/html/_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css
new file mode 100644
index 0000000..9f3d764
--- /dev/null
+++ b/doc/_build/html/_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css
@@ -0,0 +1,2449 @@
+/* Dark mode support:
+ * if <html> (e.g. Furo theme) or <body> (e.g. PyData theme) has `data-theme` set, respect it.
+ * else default to the system color scheme
+ */
+@media (prefers-color-scheme: dark) {
+	:root {
+		--light: ;
+		--dark: initial;
+	}
+}
+
+@media (prefers-color-scheme: light) {
+	:root {
+		--dark: ;
+		--light: initial;
+	}
+}
+
+:is(html, body)[data-theme="dark"] {
+	--light: ;
+	--dark: initial;
+}
+
+:is(html, body)[data-theme="light"] {
+	--dark: ;
+	--light: initial;
+}
+
+/* Variables */
+:root {
+  /*
+  Following palettes are generated by using https://m2.material.io/design/color/the-color-system.html#tools-for-picking-colors
+  - neutral palette with #fcfcfc and danger palette with #ffdddd as base colors.
+  50 means lightest, 900 means darkest; less used intermediate shades are omitted
+  but can be added when needed by accessing full palette from the above link.
+  */
+  --mystnb-neutral-palette-50: #fcfcfc;
+  --mystnb-neutral-palette-100: #f7f7f7;
+  --mystnb-neutral-palette-400: #cccccc;
+  --mystnb-neutral-palette-500: #afafaf;
+  --mystnb-neutral-palette-800: #505050;
+  --mystnb-neutral-palette-900: #2d2d2d;
+
+  --mystnb-danger-palette-50: #ffdddd;
+  --mystnb-danger-palette-100: #f5acad;
+  --mystnb-danger-palette-400: #c42029;
+  --mystnb-danger-palette-500: #b40008;
+  --mystnb-danger-palette-800: #850010;
+  --mystnb-danger-palette-900: #680010;
+
+  /* MyST-NB specific variables; colors should be logically picked from palettes */
+  --mystnb-source-bg-color: var(--light, var(--mystnb-neutral-palette-100)) var(--dark, var(--mystnb-neutral-palette-800));
+  --mystnb-stdout-bg-color: var(--light, var(--mystnb-neutral-palette-50)) var(--dark, var(--mystnb-neutral-palette-900));
+  --mystnb-stderr-bg-color: var(--light, var(--mystnb-danger-palette-50)) var(--dark, var(--mystnb-danger-palette-900));
+  --mystnb-traceback-bg-color: var(--light, var(--mystnb-neutral-palette-50)) var(--dark, var(--mystnb-neutral-palette-900));
+  --mystnb-source-border-color: var(--light, var(--mystnb-neutral-palette-400)) var(--dark, var(--mystnb-neutral-palette-500));
+  --mystnb-source-margin-color: green;
+  --mystnb-stdout-border-color: var(--light, var(--mystnb-neutral-palette-100)) var(--dark, var(--mystnb-neutral-palette-800));
+  --mystnb-stderr-border-color: var(--light, var(--mystnb-neutral-palette-100)) var(--dark, var(--mystnb-neutral-palette-800));
+  --mystnb-traceback-border-color: var(--light, var(--mystnb-danger-palette-100)) var(--dark, var(--mystnb-danger-palette-800));
+  --mystnb-hide-prompt-opacity: 70%;
+  --mystnb-source-border-radius: .4em;
+  --mystnb-source-border-width: 1px;
+  --mystnb-scrollbar-width: 0.3rem;
+  --mystnb-scrollbar-height: 0.3rem;
+  --mystnb-scrollbar-thumb-color: var(--light, var(--mystnb-neutral-palette-400)) var(--dark, var(--mystnb-neutral-palette-500));
+  --mystnb-scrollbar-thumb-hover-color: var(--light, var(--mystnb-neutral-palette-500)) var(--dark, var(--mystnb-neutral-palette-400));
+  --mystnb-scrollbar-thumb-border-radius: 0.25rem;
+}
+
+
+/* Whole cell */
+div.container.cell {
+  padding-left: 0;
+  margin-bottom: 1em;
+}
+
+/* Removing all background formatting so we can control at the div level */
+.cell_input div.highlight,
+.cell_output pre,
+.cell_input pre,
+.cell_output .output {
+  border: none;
+  box-shadow: none;
+}
+
+.cell_output .output pre,
+.cell_input pre {
+  margin: 0px;
+}
+
+/* Input cells */
+div.cell > div.cell_input {
+  padding-left: 0em;
+  padding-right: 0em;
+  border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid;
+  background-color: var(--mystnb-source-bg-color);
+  border-left-color: var(--mystnb-source-margin-color);
+  border-left-width: medium;
+  border-radius: var(--mystnb-source-border-radius);
+}
+
+div.cell_input>div,
+div.cell_output div.output>div.highlight {
+  margin: 0em !important;
+  border: none !important;
+}
+
+/* All cell outputs */
+.cell_output {
+  padding-left: 1em;
+  padding-right: 0em;
+  margin-top: 1em;
+}
+
+/* Text outputs from cells */
+.cell_output .output.text_plain,
+.cell_output .output.traceback,
+.cell_output .output.stream,
+.cell_output .output.stderr {
+  margin-top: 1em;
+  margin-bottom: 0em;
+  box-shadow: none;
+}
+
+.cell_output .output.text_plain:not(:has(.highlight)),
+.cell_output .output.stream:not(:has(.highlight)) {
+  /* plain (or stream of) output, not containing a pygments-highlighted block */
+  background: var(--mystnb-stdout-bg-color);
+  border: 1px solid var(--mystnb-stdout-border-color);
+}
+
+.cell_output .output.stderr {
+  background: var(--mystnb-stderr-bg-color);
+  border: 1px solid var(--mystnb-stderr-border-color);
+}
+
+.cell_output .output.traceback {
+  background: var(--mystnb-traceback-bg-color);
+  border: 1px solid var(--mystnb-traceback-border-color);
+}
+
+/* --- Collapsible cell content --- */
+
+/*
+encourage summary container to blend in with its parent.
+p.admonition-title should hold the title styles.
+*/
+div.cell details.hide summary {
+  border-left: unset;
+  padding: inherit;
+  margin: inherit;
+  background-color: inherit;
+}
+
+/* Neighboring input/output elements - spacing, borders */
+div.cell details.hide.above-input + details.below-input,
+div.cell div.cell_input + details.below-input
+{
+  margin-top: 0;
+}
+
+div.cell details.hide.above-input:has(+ details.below-input),
+div.cell div.cell_input:has(+ details.below-input)
+{
+  margin-bottom: 0;
+}
+
+div.cell:has(> *:nth-child(2)) div.cell_input:first-child,
+div.cell:has(> *:nth-child(2)) details:first-child
+{
+  border-bottom-left-radius: 0;
+  border-bottom-right-radius: 0;
+}
+
+div.cell:has(> *:nth-child(2)) div.cell_input:last-child,
+div.cell:has(> *:nth-child(2)) details:last-child
+{
+  border-top-left-radius: 0;
+  border-top-right-radius: 0;
+}
+
+/* intra-label styles for collapsibles */
+div.cell.container details.hide.above-input>summary,
+div.cell.container details.hide.below-input>summary,
+div.cell.container details.hide.above-output>summary
+{
+  display: block;
+  border-left: none;
+}
+
+div.cell details.hide>summary>p.admonition-title {
+  display: list-item;
+  margin-bottom: 0;
+}
+
+div.cell details.hide:not([open]) {
+  padding-bottom: 0;
+}
+
+div.cell details.hide[open]>summary>p.collapsed {
+  display: none;
+}
+
+div.cell details.hide:not([open])>summary>p.expanded {
+  display: none;
+}
+
+@keyframes collapsed-fade-in {
+  0% {
+    opacity: 0;
+  }
+
+  100% {
+    opacity: 1;
+  }
+}
+div.cell details.hide[open]>summary~* {
+  -moz-animation: collapsed-fade-in 0.3s ease-in-out;
+  -webkit-animation: collapsed-fade-in 0.3s ease-in-out;
+  animation: collapsed-fade-in 0.3s ease-in-out;
+}
+
+/* Clear conflicting styles for details and admonitions set by some themes */
+div.cell details.admonition summary::before {
+  content: unset;
+}
+
+/* Math align to the left */
+.cell_output .MathJax_Display {
+  text-align: left !important;
+}
+
+/** source code line numbers **/
+span.linenos {
+  opacity: 0.5;
+}
+
+/* Inline text from `paste` operation */
+
+span.pasted-text {
+  font-weight: bold;
+}
+
+span.pasted-inline img {
+  max-height: 2em;
+}
+
+tbody span.pasted-inline img {
+  max-height: none;
+}
+
+
+/* Adding scroll bars if tags: output_scroll, scroll-output, and scroll-input
+ * On screens, we want to scroll, but on print show all
+ *
+ * It was before in https://github.com/executablebooks/sphinx-book-theme/blob/eb1b6baf098b27605e8f2b7b2979b17ebf1b9540/src/sphinx_book_theme/assets/styles/extensions/_myst-nb.scss
+*/
+div.cell:is(
+    .tag_output_scroll,
+    .tag_scroll-output,
+    .config_scroll_outputs
+  )
+  div.cell_output,
+div.cell.tag_scroll-input div.cell_input {
+  max-height: 24em;
+  overflow-y: auto;
+  max-width: 100%;
+  overflow-x: auto;
+}
+
+div.cell.config_scroll_outputs div.cell_output:has(img) {
+  /* If the output cell has image(s), allow it to take 90% of viewport height
+  but still bounded between 24em and 60em */
+  max-height: clamp(24em, 90vh, 60em);
+}
+
+/* Custom scrollbars */
+div.cell:is(
+    .tag_output_scroll,
+    .tag_scroll-output,
+    .config_scroll_outputs
+  )
+  div.cell_output::-webkit-scrollbar,
+div.cell.tag_scroll-input div.cell_input::-webkit-scrollbar {
+  width: var(--mystnb-scrollbar-width);
+  height: var(--mystnb-scrollbar-height);
+}
+
+div.cell:is(
+    .tag_output_scroll,
+    .tag_scroll-output,
+    .config_scroll_outputs
+  )
+  div.cell_output::-webkit-scrollbar-thumb,
+div.cell.tag_scroll-input div.cell_input::-webkit-scrollbar-thumb {
+  background: var(--mystnb-scrollbar-thumb-color);
+  border-radius: var(--mystnb-scrollbar-thumb-border-radius);
+}
+
+div.cell:is(
+    .tag_output_scroll,
+    .tag_scroll-output,
+    .config_scroll_outputs
+  )
+  div.cell_output::-webkit-scrollbar-thumb:hover,
+div.cell.tag_scroll-input div.cell_input::-webkit-scrollbar-thumb:hover {
+  background: var(--mystnb-scrollbar-thumb-hover-color);
+}
+
+/* In print mode, unset scroll styles */
+@media print {
+  div.cell:is(
+      .tag_output_scroll,
+      .tag_scroll-output,
+      .config_scroll_outputs
+    )
+    div.cell_output,
+  div.cell.tag_scroll-input div.cell_input {
+    max-height: unset;
+    overflow-y: visible;
+    max-width: unset;
+    overflow-x: visible;
+  }
+}
+
+/* Font colors for translated ANSI escape sequences
+Color values are copied from Jupyter Notebook
+https://github.com/jupyter/notebook/blob/52581f8eda9b319eb0390ac77fe5903c38f81e3e/notebook/static/notebook/less/ansicolors.less#L14-L21
+Background colors from
+https://nbsphinx.readthedocs.io/en/latest/code-cells.html#ANSI-Colors
+*/
+div.highlight .-Color-Bold {
+  font-weight: bold;
+}
+
+div.highlight .-Color[class*=-Black] {
+  color: #3E424D
+}
+
+div.highlight .-Color[class*=-Red] {
+  color: #E75C58
+}
+
+div.highlight .-Color[class*=-Green] {
+  color: #00A250
+}
+
+div.highlight .-Color[class*=-Yellow] {
+  color: #DDB62B
+}
+
+div.highlight .-Color[class*=-Blue] {
+  color: #208FFB
+}
+
+div.highlight .-Color[class*=-Magenta] {
+  color: #D160C4
+}
+
+div.highlight .-Color[class*=-Cyan] {
+  color: #60C6C8
+}
+
+div.highlight .-Color[class*=-White] {
+  color: #C5C1B4
+}
+
+div.highlight .-Color[class*=-BGBlack] {
+  background-color: #3E424D
+}
+
+div.highlight .-Color[class*=-BGRed] {
+  background-color: #E75C58
+}
+
+div.highlight .-Color[class*=-BGGreen] {
+  background-color: #00A250
+}
+
+div.highlight .-Color[class*=-BGYellow] {
+  background-color: #DDB62B
+}
+
+div.highlight .-Color[class*=-BGBlue] {
+  background-color: #208FFB
+}
+
+div.highlight .-Color[class*=-BGMagenta] {
+  background-color: #D160C4
+}
+
+div.highlight .-Color[class*=-BGCyan] {
+  background-color: #60C6C8
+}
+
+div.highlight .-Color[class*=-BGWhite] {
+  background-color: #C5C1B4
+}
+
+/* Font colors for 8-bit ANSI */
+
+div.highlight .-Color[class*=-C0] {
+  color: #000000
+}
+
+div.highlight .-Color[class*=-BGC0] {
+  background-color: #000000
+}
+
+div.highlight .-Color[class*=-C1] {
+  color: #800000
+}
+
+div.highlight .-Color[class*=-BGC1] {
+  background-color: #800000
+}
+
+div.highlight .-Color[class*=-C2] {
+  color: #008000
+}
+
+div.highlight .-Color[class*=-BGC2] {
+  background-color: #008000
+}
+
+div.highlight .-Color[class*=-C3] {
+  color: #808000
+}
+
+div.highlight .-Color[class*=-BGC3] {
+  background-color: #808000
+}
+
+div.highlight .-Color[class*=-C4] {
+  color: #000080
+}
+
+div.highlight .-Color[class*=-BGC4] {
+  background-color: #000080
+}
+
+div.highlight .-Color[class*=-C5] {
+  color: #800080
+}
+
+div.highlight .-Color[class*=-BGC5] {
+  background-color: #800080
+}
+
+div.highlight .-Color[class*=-C6] {
+  color: #008080
+}
+
+div.highlight .-Color[class*=-BGC6] {
+  background-color: #008080
+}
+
+div.highlight .-Color[class*=-C7] {
+  color: #C0C0C0
+}
+
+div.highlight .-Color[class*=-BGC7] {
+  background-color: #C0C0C0
+}
+
+div.highlight .-Color[class*=-C8] {
+  color: #808080
+}
+
+div.highlight .-Color[class*=-BGC8] {
+  background-color: #808080
+}
+
+div.highlight .-Color[class*=-C9] {
+  color: #FF0000
+}
+
+div.highlight .-Color[class*=-BGC9] {
+  background-color: #FF0000
+}
+
+div.highlight .-Color[class*=-C10] {
+  color: #00FF00
+}
+
+div.highlight .-Color[class*=-BGC10] {
+  background-color: #00FF00
+}
+
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+  color: #FFFF00
+}
+
+div.highlight .-Color[class*=-BGC11] {
+  background-color: #FFFF00
+}
+
+div.highlight .-Color[class*=-C12] {
+  color: #0000FF
+}
+
+div.highlight .-Color[class*=-BGC12] {
+  background-color: #0000FF
+}
+
+div.highlight .-Color[class*=-C13] {
+  color: #FF00FF
+}
+
+div.highlight .-Color[class*=-BGC13] {
+  background-color: #FF00FF
+}
+
+div.highlight .-Color[class*=-C14] {
+  color: #00FFFF
+}
+
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+  background-color: #00FFFF
+}
+
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+  color: #FFFFFF
+}
+
+div.highlight .-Color[class*=-BGC15] {
+  background-color: #FFFFFF
+}
+
+div.highlight .-Color[class*=-C16] {
+  color: #000000
+}
+
+div.highlight .-Color[class*=-BGC16] {
+  background-color: #000000
+}
+
+div.highlight .-Color[class*=-C17] {
+  color: #00005F
+}
+
+div.highlight .-Color[class*=-BGC17] {
+  background-color: #00005F
+}
+
+div.highlight .-Color[class*=-C18] {
+  color: #000087
+}
+
+div.highlight .-Color[class*=-BGC18] {
+  background-color: #000087
+}
+
+div.highlight .-Color[class*=-C19] {
+  color: #0000AF
+}
+
+div.highlight .-Color[class*=-BGC19] {
+  background-color: #0000AF
+}
+
+div.highlight .-Color[class*=-C20] {
+  color: #0000D7
+}
+
+div.highlight .-Color[class*=-BGC20] {
+  background-color: #0000D7
+}
+
+div.highlight .-Color[class*=-C21] {
+  color: #0000FF
+}
+
+div.highlight .-Color[class*=-BGC21] {
+  background-color: #0000FF
+}
+
+div.highlight .-Color[class*=-C22] {
+  color: #005F00
+}
+
+div.highlight .-Color[class*=-BGC22] {
+  background-color: #005F00
+}
+
+div.highlight .-Color[class*=-C23] {
+  color: #005F5F
+}
+
+div.highlight .-Color[class*=-BGC23] {
+  background-color: #005F5F
+}
+
+div.highlight .-Color[class*=-C24] {
+  color: #005F87
+}
+
+div.highlight .-Color[class*=-BGC24] {
+  background-color: #005F87
+}
+
+div.highlight .-Color[class*=-C25] {
+  color: #005FAF
+}
+
+div.highlight .-Color[class*=-BGC25] {
+  background-color: #005FAF
+}
+
+div.highlight .-Color[class*=-C26] {
+  color: #005FD7
+}
+
+div.highlight .-Color[class*=-BGC26] {
+  background-color: #005FD7
+}
+
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+  color: #005FFF
+}
+
+div.highlight .-Color[class*=-BGC27] {
+  background-color: #005FFF
+}
+
+div.highlight .-Color[class*=-C28] {
+  color: #008700
+}
+
+div.highlight .-Color[class*=-BGC28] {
+  background-color: #008700
+}
+
+div.highlight .-Color[class*=-C29] {
+  color: #00875F
+}
+
+div.highlight .-Color[class*=-BGC29] {
+  background-color: #00875F
+}
+
+div.highlight .-Color[class*=-C30] {
+  color: #008787
+}
+
+div.highlight .-Color[class*=-BGC30] {
+  background-color: #008787
+}
+
+div.highlight .-Color[class*=-C31] {
+  color: #0087AF
+}
+
+div.highlight .-Color[class*=-BGC31] {
+  background-color: #0087AF
+}
+
+div.highlight .-Color[class*=-C32] {
+  color: #0087D7
+}
+
+div.highlight .-Color[class*=-BGC32] {
+  background-color: #0087D7
+}
+
+div.highlight .-Color[class*=-C33] {
+  color: #0087FF
+}
+
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+  background-color: #0087FF
+}
+
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+  color: #00AF00
+}
+
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+  background-color: #00AF00
+}
+
+div.highlight .-Color[class*=-C35] {
+  color: #00AF5F
+}
+
+div.highlight .-Color[class*=-BGC35] {
+  background-color: #00AF5F
+}
+
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+  color: #00AF87
+}
+
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+  background-color: #00AF87
+}
+
+div.highlight .-Color[class*=-C37] {
+  color: #00AFAF
+}
+
+div.highlight .-Color[class*=-BGC37] {
+  background-color: #00AFAF
+}
+
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+  color: #00AFD7
+}
+
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+  background-color: #00AFD7
+}
+
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+  color: #00AFFF
+}
+
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+  background-color: #00AFFF
+}
+
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+  color: #00D700
+}
+
+div.highlight .-Color[class*=-BGC40] {
+  background-color: #00D700
+}
+
+div.highlight .-Color[class*=-C41] {
+  color: #00D75F
+}
+
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+  background-color: #00D75F
+}
+
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+  color: #00D787
+}
+
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+  background-color: #00D787
+}
+
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+  color: #00D7AF
+}
+
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+  background-color: #00D7AF
+}
+
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+  color: #00D7D7
+}
+
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+  background-color: #00D7D7
+}
+
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+  color: #00D7FF
+}
+
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+  background-color: #00D7FF
+}
+
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+  color: #00FF00
+}
+
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+  background-color: #00FF00
+}
+
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+  color: #00FF5F
+}
+
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+  background-color: #00FF5F
+}
+
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+  color: #00FF87
+}
+
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+  background-color: #00FF87
+}
+
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+  color: #00FFAF
+}
+
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+  background-color: #00FFAF
+}
+
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+  color: #00FFD7
+}
+
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+  background-color: #00FFD7
+}
+
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+  color: #00FFFF
+}
+
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+  background-color: #00FFFF
+}
+
+div.highlight .-Color[class*=-C52] {
+  color: #5F0000
+}
+
+div.highlight .-Color[class*=-BGC52] {
+  background-color: #5F0000
+}
+
+div.highlight .-Color[class*=-C53] {
+  color: #5F005F
+}
+
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+  background-color: #5F005F
+}
+
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+  color: #5F0087
+}
+
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+  background-color: #5F0087
+}
+
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+  color: #5F00AF
+}
+
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+  background-color: #5F00AF
+}
+
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+  color: #5F00D7
+}
+
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+  background-color: #5F00D7
+}
+
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+  color: #5F00FF
+}
+
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+  background-color: #5F00FF
+}
+
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+  color: #5F5F00
+}
+
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+  background-color: #5F5F00
+}
+
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+  color: #5F5F5F
+}
+
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+  background-color: #5F5F5F
+}
+
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+  color: #5F5F87
+}
+
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+  background-color: #5F5F87
+}
+
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+  color: #5F5FAF
+}
+
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+  background-color: #5F5FAF
+}
+
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+  color: #5F5FD7
+}
+
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+  background-color: #5F5FD7
+}
+
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+  color: #5F5FFF
+}
+
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+  background-color: #5F5FFF
+}
+
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+  color: #5F8700
+}
+
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+}
+
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+  color: #5F875F
+}
+
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+  background-color: #5F875F
+}
+
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+  color: #5F8787
+}
+
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+  background-color: #5F8787
+}
+
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+  color: #5F87AF
+}
+
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+  background-color: #5F87AF
+}
+
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+  color: #5F87D7
+}
+
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+  background-color: #5F87D7
+}
+
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+  color: #5F87FF
+}
+
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+  background-color: #5F87FF
+}
+
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+  color: #5FAF00
+}
+
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+}
+
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+  color: #5FAF5F
+}
+
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+  background-color: #5FAF5F
+}
+
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+  color: #5FAF87
+}
+
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+  background-color: #5FAF87
+}
+
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+  color: #5FAFAF
+}
+
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+}
+
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+  color: #5FAFD7
+}
+
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+}
+
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+  color: #5FAFFF
+}
+
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+}
+
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+  color: #5FD700
+}
+
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+}
+
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+  color: #5FD75F
+}
+
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+}
+
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+}
+
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+  background-color: #5FD787
+}
+
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+  color: #5FD7AF
+}
+
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+}
+
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+  color: #5FD7D7
+}
+
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+}
+
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+  color: #5FD7FF
+}
+
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+}
+
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+}
+
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+}
+
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+}
+
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+}
+
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+  color: #5FFF87
+}
+
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+  background-color: #5FFF87
+}
+
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+  color: #5FFFAF
+}
+
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+  background-color: #5FFFAF
+}
+
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+  color: #5FFFD7
+}
+
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+  background-color: #5FFFD7
+}
+
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+}
+
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+}
+
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+  color: #870000
+}
+
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+  background-color: #870000
+}
+
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+  color: #87005F
+}
+
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+  background-color: #87005F
+}
+
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+  color: #870087
+}
+
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+  background-color: #870087
+}
+
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+  color: #8700AF
+}
+
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+  background-color: #8700AF
+}
+
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+  color: #8700D7
+}
+
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+  background-color: #8700D7
+}
+
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+  color: #8700FF
+}
+
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+  background-color: #8700FF
+}
+
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+}
+
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+  background-color: #875F00
+}
+
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+  color: #875F5F
+}
+
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+  background-color: #875F5F
+}
+
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+  color: #875F87
+}
+
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+}
+
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+  color: #875FAF
+}
+
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+}
+
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+}
+
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+  background-color: #875FD7
+}
+
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+  color: #875FFF
+}
+
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+  background-color: #875FFF
+}
+
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+  color: #878700
+}
+
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+  background-color: #878700
+}
+
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+  color: #87875F
+}
+
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+  background-color: #87875F
+}
+
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+  color: #878787
+}
+
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+  background-color: #878787
+}
+
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+  color: #8787AF
+}
+
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+}
+
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+  color: #8787D7
+}
+
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+  background-color: #8787D7
+}
+
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+}
+
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+  background-color: #8787FF
+}
+
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+}
+
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+}
+
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+}
+
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+}
+
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+  color: #87AF87
+}
+
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+}
+
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+}
+
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+}
+
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+  color: #87AFD7
+}
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+}
+
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+}
+
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+}
+
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+}
+
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+}
+
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+}
+
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+}
+
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+}
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+}
+
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+}
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+}
+
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+}
+
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+}
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+}
+
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+  background-color: #87D7FF
+}
+
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+}
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+}
+
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+}
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+}
+
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+}
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+}
+
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+}
+
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+}
+
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+  background-color: #AF0087
+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+  background-color: #767676
+}
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+  color: #808080
+}
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+div.highlight .-Color[class*=-BGC244] {
+  background-color: #808080
+}
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+div.highlight .-Color[class*=-C245] {
+  color: #8A8A8A
+}
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+  background-color: #8A8A8A
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+  color: #949494
+}
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+  background-color: #949494
+}
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+  color: #9E9E9E
+}
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+  background-color: #9E9E9E
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+  color: #A8A8A8
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+  background-color: #A8A8A8
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+  color: #B2B2B2
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+  background-color: #B2B2B2
+}
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+  color: #BCBCBC
+}
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+div.highlight .-Color[class*=-BGC250] {
+  background-color: #BCBCBC
+}
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+  color: #C6C6C6
+}
+
+div.highlight .-Color[class*=-BGC251] {
+  background-color: #C6C6C6
+}
+
+div.highlight .-Color[class*=-C252] {
+  color: #D0D0D0
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+  background-color: #D0D0D0
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+  background-color: #EEEEEE
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diff --git a/doc/_build/html/api.html b/doc/_build/html/api.html
index 15e032d..436140e 100644
--- a/doc/_build/html/api.html
+++ b/doc/_build/html/api.html
@@ -11,6 +11,7 @@
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
diff --git a/doc/_build/html/coming-from-r.html b/doc/_build/html/coming-from-r.html
index b46902c..1acd1a8 100644
--- a/doc/_build/html/coming-from-r.html
+++ b/doc/_build/html/coming-from-r.html
@@ -11,6 +11,7 @@
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -259,17 +260,17 @@ <h1>Coming from R<a class="headerlink" href="#coming-from-r" title="Link to this
 <section id="the-infer-pipeline">
 <h2>The infer pipeline<a class="headerlink" href="#the-infer-pipeline" title="Link to this heading">¶</a></h2>
 <div class="highlight-r notranslate"><div class="highlight"><pre><span></span><span class="c1"># R</span>
-<span class="n">mtcars</span><span class="w"> </span><span class="o">%&gt;%</span>
-<span class="w">  </span><span class="nf">specify</span><span class="p">(</span><span class="n">response</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mpg</span><span class="p">)</span><span class="w"> </span><span class="o">%&gt;%</span>
-<span class="w">  </span><span class="nf">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s">&quot;point&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">mu</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">20</span><span class="p">)</span><span class="w"> </span><span class="o">%&gt;%</span>
+<span class="n">pennies</span><span class="w"> </span><span class="o">%&gt;%</span>
+<span class="w">  </span><span class="nf">specify</span><span class="p">(</span><span class="n">response</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">year</span><span class="p">)</span><span class="w"> </span><span class="o">%&gt;%</span>
+<span class="w">  </span><span class="nf">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s">&quot;point&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">mu</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">1995</span><span class="p">)</span><span class="w"> </span><span class="o">%&gt;%</span>
 <span class="w">  </span><span class="nf">generate</span><span class="p">(</span><span class="n">reps</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">1000</span><span class="p">,</span><span class="w"> </span><span class="n">type</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s">&quot;bootstrap&quot;</span><span class="p">)</span><span class="w"> </span><span class="o">%&gt;%</span>
 <span class="w">  </span><span class="nf">calculate</span><span class="p">(</span><span class="n">stat</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s">&quot;mean&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Python — verbs are methods on the returned objects</span>
 <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;mpg&quot;</span><span class="p">)</span>
-    <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;point&quot;</span><span class="p">,</span> <span class="n">mu</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+    <span class="n">specify</span><span class="p">(</span><span class="n">md</span><span class="o">.</span><span class="n">load_pennies</span><span class="p">(),</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;year&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;point&quot;</span><span class="p">,</span> <span class="n">mu</span><span class="o">=</span><span class="mi">1995</span><span class="p">)</span>
     <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">)</span>
 <span class="p">)</span>
@@ -278,108 +279,71 @@ <h2>The infer pipeline<a class="headerlink" href="#the-infer-pipeline" title="Li
 <p><code class="docutils literal notranslate"><span class="pre">specify(formula=&quot;y</span> <span class="pre">~</span> <span class="pre">x&quot;)</span></code> works just like R’s formula interface; <code class="docutils literal notranslate"><span class="pre">success=</span></code> marks
 the success level for categorical responses.</p>
 </section>
-<section id="function-map">
-<h2>Function map<a class="headerlink" href="#function-map" title="Link to this heading">¶</a></h2>
+<section id="most-names-are-identical">
+<h2>Most names are identical<a class="headerlink" href="#most-names-are-identical" title="Link to this heading">¶</a></h2>
+<p>The overwhelming majority of functions keep <strong>the same name</strong> (including the
+British-spelling and short-form aliases). They’re called just as in R — as
+methods on the pipeline where applicable:</p>
+<blockquote>
+<div><p><code class="docutils literal notranslate"><span class="pre">specify</span></code>, <code class="docutils literal notranslate"><span class="pre">hypothesize</span></code>/<code class="docutils literal notranslate"><span class="pre">hypothesise</span></code>, <code class="docutils literal notranslate"><span class="pre">generate</span></code>, <code class="docutils literal notranslate"><span class="pre">calculate</span></code>, <code class="docutils literal notranslate"><span class="pre">fit</span></code>,
+<code class="docutils literal notranslate"><span class="pre">assume</span></code>, <code class="docutils literal notranslate"><span class="pre">observe</span></code>, <code class="docutils literal notranslate"><span class="pre">get_p_value</span></code>/<code class="docutils literal notranslate"><span class="pre">get_pvalue</span></code>,
+<code class="docutils literal notranslate"><span class="pre">get_confidence_interval</span></code>/<code class="docutils literal notranslate"><span class="pre">get_ci</span></code>, <code class="docutils literal notranslate"><span class="pre">visualize</span></code>/<code class="docutils literal notranslate"><span class="pre">visualise</span></code>,
+<code class="docutils literal notranslate"><span class="pre">shade_p_value</span></code>/<code class="docutils literal notranslate"><span class="pre">shade_pvalue</span></code>, <code class="docutils literal notranslate"><span class="pre">shade_confidence_interval</span></code>/<code class="docutils literal notranslate"><span class="pre">shade_ci</span></code>,
+<code class="docutils literal notranslate"><span class="pre">t_test</span></code>, <code class="docutils literal notranslate"><span class="pre">prop_test</span></code>, <code class="docutils literal notranslate"><span class="pre">chisq_test</span></code>, <code class="docutils literal notranslate"><span class="pre">t_stat</span></code>, <code class="docutils literal notranslate"><span class="pre">chisq_stat</span></code>,
+<code class="docutils literal notranslate"><span class="pre">rep_sample_n</span></code>/<code class="docutils literal notranslate"><span class="pre">rep_slice_sample</span></code>, <code class="docutils literal notranslate"><span class="pre">get_regression_table</span></code>,
+<code class="docutils literal notranslate"><span class="pre">get_regression_points</span></code>, <code class="docutils literal notranslate"><span class="pre">get_regression_summaries</span></code>, <code class="docutils literal notranslate"><span class="pre">get_correlation</span></code>,
+<code class="docutils literal notranslate"><span class="pre">pop_sd</span></code>, <code class="docutils literal notranslate"><span class="pre">tidy_summary</span></code>, <code class="docutils literal notranslate"><span class="pre">geom_parallel_slopes</span></code>.</p>
+</div></blockquote>
+</section>
+<section id="what-s-actually-different">
+<h2>What’s actually different<a class="headerlink" href="#what-s-actually-different" title="Link to this heading">¶</a></h2>
 <div class="table-wrapper colwidths-auto docutils container">
 <table class="docutils align-default">
 <thead>
-<tr class="row-odd"><th class="head"><p>R (<code class="docutils literal notranslate"><span class="pre">infer</span></code> / <code class="docutils literal notranslate"><span class="pre">moderndive</span></code>)</p></th>
-<th class="head"><p>Python (<code class="docutils literal notranslate"><span class="pre">moderndive</span></code>)</p></th>
+<tr class="row-odd"><th class="head"><p>R</p></th>
+<th class="head"><p>Python</p></th>
+<th class="head"><p>Why</p></th>
 </tr>
 </thead>
 <tbody>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">specify()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">specify()</span></code></p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">hypothesize()</span></code> / <code class="docutils literal notranslate"><span class="pre">hypothesise()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">.hypothesize()</span></code> / <code class="docutils literal notranslate"><span class="pre">.hypothesise()</span></code></p></td>
-</tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">generate()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">.generate()</span></code></p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">calculate()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">.calculate()</span></code></p></td>
-</tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">fit()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">.fit()</span></code></p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">assume()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">assume()</span></code></p></td>
-</tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">observe()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">observe()</span></code></p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">get_p_value()</span></code> / <code class="docutils literal notranslate"><span class="pre">get_pvalue()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">get_p_value()</span></code> / <code class="docutils literal notranslate"><span class="pre">get_pvalue()</span></code></p></td>
-</tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">get_confidence_interval()</span></code> / <code class="docutils literal notranslate"><span class="pre">get_ci()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">get_confidence_interval()</span></code> / <code class="docutils literal notranslate"><span class="pre">get_ci()</span></code></p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">visualize()</span></code> / <code class="docutils literal notranslate"><span class="pre">visualise()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">visualize()</span></code> / <code class="docutils literal notranslate"><span class="pre">visualise()</span></code></p></td>
-</tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">shade_p_value()</span></code> / <code class="docutils literal notranslate"><span class="pre">shade_pvalue()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">shade_p_value()</span></code> / <code class="docutils literal notranslate"><span class="pre">shade_pvalue()</span></code></p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">shade_confidence_interval()</span></code> / <code class="docutils literal notranslate"><span class="pre">shade_ci()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">shade_confidence_interval()</span></code> / <code class="docutils literal notranslate"><span class="pre">shade_ci()</span></code></p></td>
-</tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">t_test()</span></code>, <code class="docutils literal notranslate"><span class="pre">prop_test()</span></code>, <code class="docutils literal notranslate"><span class="pre">chisq_test()</span></code></p></td>
-<td><p>same names</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">t_stat()</span></code>, <code class="docutils literal notranslate"><span class="pre">chisq_stat()</span></code></p></td>
-<td><p>same names</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">rep_sample_n()</span></code> / <code class="docutils literal notranslate"><span class="pre">rep_slice_sample()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">rep_sample_n()</span></code> / <code class="docutils literal notranslate"><span class="pre">rep_slice_sample()</span></code></p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">get_regression_table()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">get_regression_table(model)</span></code></p></td>
-</tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">get_regression_points()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">get_regression_points(model)</span></code></p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">get_regression_summaries()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">get_regression_summaries(model)</span></code></p></td>
-</tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">get_correlation()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">get_correlation(data,</span> <span class="pre">&quot;y</span> <span class="pre">~</span> <span class="pre">x&quot;)</span></code></p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">pop_sd()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">pop_sd()</span></code></p></td>
-</tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">geom_parallel_slopes()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">geom_parallel_slopes()</span></code> (plotnine) / <code class="docutils literal notranslate"><span class="pre">gg_parallel_slopes(engine=...)</span></code></p></td>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">%&gt;%</span> <span class="pre">f(...)</span></code> / <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">|&gt;</span> <span class="pre">f(...)</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">x.f(...)</span></code> (method chaining)</p></td>
+<td><p>no pipe operator in Python</p></td>
 </tr>
 <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">geom_categorical_model()</span></code></p></td>
 <td><p><code class="docutils literal notranslate"><span class="pre">gg_categorical_model(engine=...)</span></code></p></td>
+<td><p>renamed to match <code class="docutils literal notranslate"><span class="pre">gg_parallel_slopes</span></code></p></td>
+</tr>
+<tr class="row-even"><td><p>ggplot2 <code class="docutils literal notranslate"><span class="pre">geom_*</span></code> layers</p></td>
+<td><p>plotly by default; <code class="docutils literal notranslate"><span class="pre">engine=&quot;plotnine&quot;</span></code> for ggplot-style</p></td>
+<td><p>dual-engine plotting</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">lm(y</span> <span class="pre">~</span> <span class="pre">x,</span> <span class="pre">data)</span></code> object</p></td>
+<td><p>a fitted <strong>statsmodels</strong> model: <code class="docutils literal notranslate"><span class="pre">smf.ols(&quot;y</span> <span class="pre">~</span> <span class="pre">x&quot;,</span> <span class="pre">data=df.to_pandas()).fit()</span></code></p></td>
+<td><p>regression backend</p></td>
 </tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">tidy_summary()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">tidy_summary()</span></code></p></td>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">get_correlation(df,</span> <span class="pre">y</span> <span class="pre">~</span> <span class="pre">x)</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">get_correlation(df,</span> <span class="pre">&quot;y</span> <span class="pre">~</span> <span class="pre">x&quot;)</span></code> <em>or</em> <code class="docutils literal notranslate"><span class="pre">get_correlation(df,</span> <span class="pre">x=&quot;x&quot;,</span> <span class="pre">y=&quot;y&quot;)</span></code></p></td>
+<td><p>formula passed as a string</p></td>
 </tr>
 </tbody>
 </table>
 </div>
 </section>
-<section id="key-differences">
-<h2>Key differences<a class="headerlink" href="#key-differences" title="Link to this heading">¶</a></h2>
+<section id="other-things-to-know">
+<h2>Other things to know<a class="headerlink" href="#other-things-to-know" title="Link to this heading">¶</a></h2>
 <ul class="simple">
-<li><p><strong>Pipe → methods.</strong> <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">%&gt;%</span> <span class="pre">hypothesize(...)</span></code> becomes <code class="docutils literal notranslate"><span class="pre">x.hypothesize(...)</span></code>.</p></li>
-<li><p><strong>Models.</strong> Where R passes an <code class="docutils literal notranslate"><span class="pre">lm()</span></code> object, Python passes a fitted
-<a class="reference external" href="https://www.statsmodels.org">statsmodels</a> model:
-<code class="docutils literal notranslate"><span class="pre">smf.ols(&quot;y</span> <span class="pre">~</span> <span class="pre">x&quot;,</span> <span class="pre">data=df.to_pandas()).fit()</span></code>.</p></li>
-<li><p><strong>Plotting engine.</strong> R returns ggplot2; Python defaults to <strong>plotly</strong>
-(interactive) with <code class="docutils literal notranslate"><span class="pre">engine=&quot;plotnine&quot;</span></code> available everywhere. Plots compose with
-<code class="docutils literal notranslate"><span class="pre">+</span></code> in both engines.</p></li>
-<li><p><strong>DataFrames.</strong> Inputs/outputs are polars; pass <code class="docutils literal notranslate"><span class="pre">.to_pandas()</span></code> when a downstream
-tool needs pandas.</p></li>
-<li><p><strong>Reproducibility.</strong> Pass <code class="docutils literal notranslate"><span class="pre">seed=</span></code> to <code class="docutils literal notranslate"><span class="pre">generate()</span></code> (R uses <code class="docutils literal notranslate"><span class="pre">set.seed()</span></code>).</p></li>
+<li><p><strong>Plots compose with <code class="docutils literal notranslate"><span class="pre">+</span></code></strong> in both engines, and default to <strong>plotly</strong>
+(interactive); pass <code class="docutils literal notranslate"><span class="pre">engine=&quot;plotnine&quot;</span></code> anywhere for ggplot-style output.</p></li>
+<li><p><strong>DataFrames</strong> are polars in and out; pass <code class="docutils literal notranslate"><span class="pre">.to_pandas()</span></code> when a downstream tool
+needs pandas.</p></li>
+<li><p><strong>Reproducibility:</strong> pass <code class="docutils literal notranslate"><span class="pre">seed=</span></code> to <code class="docutils literal notranslate"><span class="pre">generate()</span></code> (R uses <code class="docutils literal notranslate"><span class="pre">set.seed()</span></code>).</p></li>
 </ul>
 </section>
 <section id="same-datasets">
 <h2>Same datasets<a class="headerlink" href="#same-datasets" title="Link to this heading">¶</a></h2>
 <p>Most R <code class="docutils literal notranslate"><span class="pre">moderndive</span></code>/<code class="docutils literal notranslate"><span class="pre">infer</span></code> datasets are bundled here under the same name —
-<code class="docutils literal notranslate"><span class="pre">load_promotions()</span></code>, <code class="docutils literal notranslate"><span class="pre">load_pennies()</span></code>, <code class="docutils literal notranslate"><span class="pre">load_gss()</span></code>, etc. See <a class="reference internal" href="datasets.html"><span class="doc">Datasets</span></a>.</p>
+<code class="docutils literal notranslate"><span class="pre">load_pennies()</span></code>, <code class="docutils literal notranslate"><span class="pre">load_promotions()</span></code>, <code class="docutils literal notranslate"><span class="pre">load_gss()</span></code>, etc. See <a class="reference internal" href="datasets.html"><span class="doc">Datasets</span></a>.</p>
 </section>
 </section>
 
@@ -440,8 +404,9 @@ <h2>Same datasets<a class="headerlink" href="#same-datasets" title="Link to this
             <ul>
 <li><a class="reference internal" href="#">Coming from R</a><ul>
 <li><a class="reference internal" href="#the-infer-pipeline">The infer pipeline</a></li>
-<li><a class="reference internal" href="#function-map">Function map</a></li>
-<li><a class="reference internal" href="#key-differences">Key differences</a></li>
+<li><a class="reference internal" href="#most-names-are-identical">Most names are identical</a></li>
+<li><a class="reference internal" href="#what-s-actually-different">What’s actually different</a></li>
+<li><a class="reference internal" href="#other-things-to-know">Other things to know</a></li>
 <li><a class="reference internal" href="#same-datasets">Same datasets</a></li>
 </ul>
 </li>
diff --git a/doc/_build/html/datasets.html b/doc/_build/html/datasets.html
index ba1c982..320b3d8 100644
--- a/doc/_build/html/datasets.html
+++ b/doc/_build/html/datasets.html
@@ -11,6 +11,7 @@
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -251,17 +252,32 @@
           </label>
         </div>
         <article role="main" id="furo-main-content">
-          <section id="datasets">
+          <div class="cell tag_remove-input docutils container">
+</div>
+<section id="datasets">
 <h1>Datasets<a class="headerlink" href="#datasets" title="Link to this heading">¶</a></h1>
 <p><code class="docutils literal notranslate"><span class="pre">moderndive</span></code> bundles 58 datasets (the R <code class="docutils literal notranslate"><span class="pre">moderndive</span></code> + <code class="docutils literal notranslate"><span class="pre">infer</span></code> data, plus a few
 derived tables). Each loads with <code class="docutils literal notranslate"><span class="pre">load_&lt;name&gt;()</span></code> and returns a polars DataFrame.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
 
 <span class="n">md</span><span class="o">.</span><span class="n">available_datasets</span><span class="p">()</span>          <span class="c1"># sorted list of every dataset name</span>
 <span class="n">md</span><span class="o">.</span><span class="n">load_dataset</span><span class="p">(</span><span class="s2">&quot;promotions&quot;</span><span class="p">)</span>    <span class="c1"># load by name (string)</span>
 <span class="n">md</span><span class="o">.</span><span class="n">load_promotions</span><span class="p">()</span>             <span class="c1"># or via the generated loader</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (48, 3)</small><table border="1" class="dataframe"><thead><tr><th>id</th><th>decision</th><th>gender</th></tr><tr><td>i64</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>1</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>2</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>3</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>4</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>5</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>44</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr><tr><td>45</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr><tr><td>46</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr><tr><td>47</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr><tr><td>48</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr></tbody></table></div></div></div>
+</div>
 <section id="by-topic">
 <h2>By topic<a class="headerlink" href="#by-topic" title="Link to this heading">¶</a></h2>
 <dl class="simple myst">
diff --git a/doc/_build/html/genindex.html b/doc/_build/html/genindex.html
index 52ee06c..ae7af52 100644
--- a/doc/_build/html/genindex.html
+++ b/doc/_build/html/genindex.html
@@ -9,6 +9,7 @@
 <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
diff --git a/doc/_build/html/getting-started.html b/doc/_build/html/getting-started.html
index 877b041..95cf0b4 100644
--- a/doc/_build/html/getting-started.html
+++ b/doc/_build/html/getting-started.html
@@ -11,6 +11,7 @@
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -251,7 +252,9 @@
           </label>
         </div>
         <article role="main" id="furo-main-content">
-          <section id="getting-started">
+          <div class="cell tag_remove-input docutils container">
+</div>
+<section id="getting-started">
 <h1>Getting started<a class="headerlink" href="#getting-started" title="Link to this heading">¶</a></h1>
 <p>This page walks through a complete analysis end to end, then points you at the
 task guides for more depth.</p>
@@ -266,22 +269,24 @@ <h2>Install<a class="headerlink" href="#install" title="Link to this heading">¶
 <section id="load-a-dataset">
 <h2>Load a dataset<a class="headerlink" href="#load-a-dataset" title="Link to this heading">¶</a></h2>
 <p>All datasets ship with the package and load with <code class="docutils literal notranslate"><span class="pre">load_&lt;name&gt;()</span></code>:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
 
 <span class="n">promotions</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_promotions</span><span class="p">()</span>
 <span class="n">promotions</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
 </div>
-<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>shape: (5, 3)
-┌─────┬──────────┬────────┐
-│ id  ┆ decision ┆ gender │
-│ i64 ┆ str      ┆ str    │
-╞═════╪══════════╪════════╡
-│ 1   ┆ promoted ┆ male   │
-│ 2   ┆ promoted ┆ male   │
-│ …   ┆ …        ┆ …      │
-└─────┴──────────┴────────┘
-</pre></div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (5, 3)</small><table border="1" class="dataframe"><thead><tr><th>id</th><th>decision</th><th>gender</th></tr><tr><td>i64</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>1</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>2</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>3</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>4</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>5</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr></tbody></table></div></div></div>
 </div>
 <p>List everything that’s available with <code class="docutils literal notranslate"><span class="pre">md.available_datasets()</span></code> (58 datasets), and
 see <a class="reference internal" href="datasets.html"><span class="doc">Datasets</span></a> for a thematic tour.</p>
@@ -290,17 +295,32 @@ <h2>Load a dataset<a class="headerlink" href="#load-a-dataset" title="Link to th
 <h2>A first summary<a class="headerlink" href="#a-first-summary" title="Link to this heading">¶</a></h2>
 <p><code class="docutils literal notranslate"><span class="pre">tidy_summary</span></code> gives a per-variable five-number summary (numeric) or counts
 (categorical):</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">tidy_summary</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">tidy_summary</span>
 
 <span class="n">tidy_summary</span><span class="p">(</span><span class="n">md</span><span class="o">.</span><span class="n">load_almonds_sample_100</span><span class="p">(),</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;weight&quot;</span><span class="p">])</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (1, 11)</small><table border="1" class="dataframe"><thead><tr><th>column</th><th>n</th><th>group</th><th>type</th><th>min</th><th>Q1</th><th>mean</th><th>median</th><th>Q3</th><th>max</th><th>sd</th></tr><tr><td>str</td><td>i64</td><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;weight&quot;</td><td>100</td><td>null</td><td>&quot;numeric&quot;</td><td>2.9</td><td>3.4</td><td>3.682</td><td>3.7</td><td>3.9</td><td>4.5</td><td>0.362</td></tr></tbody></table></div></div></div>
+</div>
 </section>
 <section id="the-inference-pipeline">
 <h2>The inference pipeline<a class="headerlink" href="#the-inference-pipeline" title="Link to this heading">¶</a></h2>
 <p>The core grammar mirrors R <code class="docutils literal notranslate"><span class="pre">infer</span></code>. You build a pipeline and read it like a
 sentence:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span><span class="p">,</span> <span class="n">observe</span><span class="p">,</span> <span class="n">get_p_value</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span><span class="p">,</span> <span class="n">observe</span><span class="p">,</span> <span class="n">get_p_value</span>
 
 <span class="c1"># 1. The observed statistic</span>
 <span class="n">obs</span> <span class="o">=</span> <span class="n">observe</span><span class="p">(</span>
@@ -320,6 +340,17 @@ <h2>The inference pipeline<a class="headerlink" href="#the-inference-pipeline" t
 <span class="n">get_p_value</span><span class="p">(</span><span class="n">null</span><span class="p">,</span> <span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>   <span class="c1"># ≈ 0.025</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (1, 1)</small><table border="1" class="dataframe"><thead><tr><th>p_value</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.025</td></tr></tbody></table></div></div></div>
+</div>
 <p>Each verb has a focused guide: <a class="reference internal" href="guides/sampling.html"><span class="doc">Sampling</span></a>,
 <a class="reference internal" href="guides/confidence-intervals.html"><span class="doc">Bootstrapping &amp; confidence intervals</span></a>, and <a class="reference internal" href="guides/hypothesis-testing.html"><span class="doc">Hypothesis testing</span></a>.</p>
 </section>
@@ -327,7 +358,9 @@ <h2>The inference pipeline<a class="headerlink" href="#the-inference-pipeline" t
 <h2>Visualizing — choose your engine<a class="headerlink" href="#visualizing-choose-your-engine" title="Link to this heading">¶</a></h2>
 <p>Plots default to <strong>plotly</strong> (interactive). Pass <code class="docutils literal notranslate"><span class="pre">engine=&quot;plotnine&quot;</span></code> for
 grammar-of-graphics output. The composition syntax is identical:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">visualize</span><span class="p">,</span> <span class="n">shade_p_value</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">visualize</span><span class="p">,</span> <span class="n">shade_p_value</span>
 
 <span class="c1"># Interactive plotly figure</span>
 <span class="n">visualize</span><span class="p">(</span><span class="n">null</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_p_value</span><span class="p">(</span><span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>
@@ -336,12 +369,20 @@ <h2>Visualizing — choose your engine<a class="headerlink" href="#visualizing-c
 <span class="n">visualize</span><span class="p">(</span><span class="n">null</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s2">&quot;plotnine&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_p_value</span><span class="p">(</span><span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="_images/679346fce431777e0d221fb96d0d46c94c46266531703754ae8cf2b7aea89916.png"><img alt="_images/679346fce431777e0d221fb96d0d46c94c46266531703754ae8cf2b7aea89916.png" height="480" src="_images/679346fce431777e0d221fb96d0d46c94c46266531703754ae8cf2b7aea89916.png" width="640" />
+</a>
+</div>
+</div>
 <p>See <a class="reference internal" href="guides/plotting.html"><span class="doc">Plotting: plotly &amp; plotnine</span></a> for shading, confidence-interval overlays, theoretical
 overlays, and the regression-model plots.</p>
 </section>
 <section id="regression">
 <h2>Regression<a class="headerlink" href="#regression" title="Link to this heading">¶</a></h2>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">statsmodels.formula.api</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">smf</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">statsmodels.formula.api</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">smf</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">get_regression_table</span>
 
 <span class="n">houses</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_saratoga_houses</span><span class="p">()</span>
@@ -349,6 +390,17 @@ <h2>Regression<a class="headerlink" href="#regression" title="Link to this headi
 <span class="n">get_regression_table</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (3, 7)</small><table border="1" class="dataframe"><thead><tr><th>term</th><th>estimate</th><th>std_error</th><th>statistic</th><th>p_value</th><th>lower_ci</th><th>upper_ci</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;intercept&quot;</td><td>20986.094</td><td>6816.251</td><td>3.079</td><td>0.002</td><td>7611.128</td><td>34361.06</td></tr><tr><td>&quot;living_area&quot;</td><td>93.842</td><td>3.109</td><td>30.183</td><td>0.0</td><td>87.741</td><td>99.943</td></tr><tr><td>&quot;bedrooms&quot;</td><td>-7483.095</td><td>2783.531</td><td>-2.688</td><td>0.007</td><td>-12944.988</td><td>-2021.203</td></tr></tbody></table></div></div></div>
+</div>
 <p>Full details in <a class="reference internal" href="guides/regression.html"><span class="doc">Regression</span></a>.</p>
 </section>
 </section>
diff --git a/doc/_build/html/guides/confidence-intervals.html b/doc/_build/html/guides/confidence-intervals.html
index 3a2662d..69e2860 100644
--- a/doc/_build/html/guides/confidence-intervals.html
+++ b/doc/_build/html/guides/confidence-intervals.html
@@ -11,6 +11,7 @@
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -251,13 +252,17 @@
           </label>
         </div>
         <article role="main" id="furo-main-content">
-          <section id="bootstrapping-confidence-intervals">
+          <div class="cell tag_remove-input docutils container">
+</div>
+<section id="bootstrapping-confidence-intervals">
 <h1>Bootstrapping &amp; confidence intervals<a class="headerlink" href="#bootstrapping-confidence-intervals" title="Link to this heading">¶</a></h1>
 <p>Resample your data with replacement to build a <strong>bootstrap distribution</strong>, then
 read a confidence interval off it.</p>
 <section id="a-bootstrap-distribution-for-a-mean">
 <h2>A bootstrap distribution for a mean<a class="headerlink" href="#a-bootstrap-distribution-for-a-mean" title="Link to this heading">¶</a></h2>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span><span class="p">,</span> <span class="n">get_confidence_interval</span><span class="p">,</span> <span class="n">visualize</span><span class="p">,</span> <span class="n">shade_confidence_interval</span>
 
 <span class="n">almonds</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_almonds_sample_100</span><span class="p">()</span>
@@ -269,10 +274,14 @@ <h2>A bootstrap distribution for a mean<a class="headerlink" href="#a-bootstrap-
 <span class="p">)</span>
 </pre></div>
 </div>
+</div>
+</div>
 </section>
 <section id="confidence-intervals-three-methods">
 <h2>Confidence intervals — three methods<a class="headerlink" href="#confidence-intervals-three-methods" title="Link to this heading">¶</a></h2>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Percentile method (default)</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Percentile method (default)</span>
 <span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;percentile&quot;</span><span class="p">)</span>
 <span class="c1"># shape: (1, 2) → lower_ci ≈ 3.61, upper_ci ≈ 3.75</span>
 
@@ -284,14 +293,40 @@ <h2>Confidence intervals — three methods<a class="headerlink" href="#confidenc
 <span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bias-corrected&quot;</span><span class="p">,</span> <span class="n">point_estimate</span><span class="o">=</span><span class="n">point</span><span class="p">)</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (1, 2)</small><table border="1" class="dataframe"><thead><tr><th>lower_ci</th><th>upper_ci</th></tr><tr><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>3.613883</td><td>3.755272</td></tr></tbody></table></div></div></div>
+</div>
 <p>Distribution objects also expose the getter as a method:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">boot</span><span class="o">.</span><span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;percentile&quot;</span><span class="p">)</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">boot</span><span class="o">.</span><span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;percentile&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (1, 2)</small><table border="1" class="dataframe"><thead><tr><th>lower_ci</th><th>upper_ci</th></tr><tr><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>3.606975</td><td>3.75</td></tr></tbody></table></div></div></div>
+</div>
 </section>
 <section id="visualize-the-interval">
 <h2>Visualize the interval<a class="headerlink" href="#visualize-the-interval" title="Link to this heading">¶</a></h2>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">ci</span> <span class="o">=</span> <span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;percentile&quot;</span><span class="p">)</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ci</span> <span class="o">=</span> <span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;percentile&quot;</span><span class="p">)</span>
 
 <span class="c1"># plotly (default) — interactive</span>
 <span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_confidence_interval</span><span class="p">(</span><span class="n">ci</span><span class="p">)</span>
@@ -303,10 +338,17 @@ <h2>Visualize the interval<a class="headerlink" href="#visualize-the-interval" t
 <span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">shade_ci</span><span class="o">=</span><span class="n">ci</span><span class="p">)</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/ac66f688faad576ec00fb6dfa22ab9d34977edb8a0a5ff3cadb158ce22be94fd.png" src="../_images/ac66f688faad576ec00fb6dfa22ab9d34977edb8a0a5ff3cadb158ce22be94fd.png" />
+</div>
+</div>
 </section>
 <section id="a-confidence-interval-for-a-proportion">
 <h2>A confidence interval for a proportion<a class="headerlink" href="#a-confidence-interval-for-a-proportion" title="Link to this heading">¶</a></h2>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">polars</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pl</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">polars</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pl</span>
 
 <span class="n">mythbusters</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_mythbusters_yawn</span><span class="p">()</span>
 
@@ -318,11 +360,24 @@ <h2>A confidence interval for a proportion<a class="headerlink" href="#a-confide
 <span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">boot_prop</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;percentile&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (1, 2)</small><table border="1" class="dataframe"><thead><tr><th>lower_ci</th><th>upper_ci</th></tr><tr><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>0.1595</td><td>0.4</td></tr></tbody></table></div></div></div>
+</div>
 </section>
 <section id="a-confidence-interval-for-a-difference">
 <h2>A confidence interval for a difference<a class="headerlink" href="#a-confidence-interval-for-a-difference" title="Link to this heading">¶</a></h2>
 <p>Add an explanatory variable and an <code class="docutils literal notranslate"><span class="pre">order=</span></code> to compare two groups:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">boot_diff</span> <span class="o">=</span> <span class="p">(</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">boot_diff</span> <span class="o">=</span> <span class="p">(</span>
     <span class="n">specify</span><span class="p">(</span><span class="n">mythbusters</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;yawn ~ group&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;yes&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;seed&quot;</span><span class="p">,</span> <span class="s2">&quot;control&quot;</span><span class="p">))</span>
@@ -330,6 +385,17 @@ <h2>A confidence interval for a difference<a class="headerlink" href="#a-confide
 <span class="n">boot_diff</span><span class="o">.</span><span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">)</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (1, 2)</small><table border="1" class="dataframe"><thead><tr><th>lower_ci</th><th>upper_ci</th></tr><tr><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>-0.224934</td><td>0.316189</td></tr></tbody></table></div></div></div>
+</div>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
 <p>For <em>theory-based</em> intervals (the formula-based <code class="docutils literal notranslate"><span class="pre">t</span></code> interval) see
diff --git a/doc/_build/html/guides/hypothesis-testing.html b/doc/_build/html/guides/hypothesis-testing.html
index 5fc2f3b..d7fbcc3 100644
--- a/doc/_build/html/guides/hypothesis-testing.html
+++ b/doc/_build/html/guides/hypothesis-testing.html
@@ -11,6 +11,7 @@
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -251,7 +252,9 @@
           </label>
         </div>
         <article role="main" id="furo-main-content">
-          <section id="hypothesis-testing">
+          <div class="cell tag_remove-input docutils container">
+</div>
+<section id="hypothesis-testing">
 <h1>Hypothesis testing<a class="headerlink" href="#hypothesis-testing" title="Link to this heading">¶</a></h1>
 <p>Hypothesis tests follow the same grammar as confidence intervals, with an added
 <code class="docutils literal notranslate"><span class="pre">hypothesize()</span></code> step that defines the null world, and <code class="docutils literal notranslate"><span class="pre">generate()</span></code> that simulates
@@ -260,7 +263,9 @@ <h1>Hypothesis testing<a class="headerlink" href="#hypothesis-testing" title="Li
 <h2>Two groups: a permutation test<a class="headerlink" href="#two-groups-a-permutation-test" title="Link to this heading">¶</a></h2>
 <p>The classic “promotions” experiment — does the (perceived) gender on a résumé
 change the promotion rate?</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span><span class="p">,</span> <span class="n">observe</span><span class="p">,</span> <span class="n">get_p_value</span><span class="p">,</span> <span class="n">visualize</span><span class="p">,</span> <span class="n">shade_p_value</span>
 
 <span class="n">promotions</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_promotions</span><span class="p">()</span>
@@ -282,19 +287,39 @@ <h2>Two groups: a permutation test<a class="headerlink" href="#two-groups-a-perm
 <span class="n">get_p_value</span><span class="p">(</span><span class="n">null</span><span class="p">,</span> <span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>   <span class="c1"># ≈ 0.025</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (1, 1)</small><table border="1" class="dataframe"><thead><tr><th>p_value</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.025</td></tr></tbody></table></div></div></div>
+</div>
 </section>
 <section id="shade-the-p-value">
 <h2>Shade the p-value<a class="headerlink" href="#shade-the-p-value" title="Link to this heading">¶</a></h2>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">visualize</span><span class="p">(</span><span class="n">null</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_p_value</span><span class="p">(</span><span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">visualize</span><span class="p">(</span><span class="n">null</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_p_value</span><span class="p">(</span><span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/446856f7a7a4b3ae8beb128934fc4936f3afde3e478c71e657c8305dcee97764.png" src="../_images/446856f7a7a4b3ae8beb128934fc4936f3afde3e478c71e657c8305dcee97764.png" />
+</div>
+</div>
 <p><code class="docutils literal notranslate"><span class="pre">direction</span></code> is one of <code class="docutils literal notranslate"><span class="pre">&quot;right&quot;</span></code>/<code class="docutils literal notranslate"><span class="pre">&quot;greater&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;left&quot;</span></code>/<code class="docutils literal notranslate"><span class="pre">&quot;less&quot;</span></code>, or <code class="docutils literal notranslate"><span class="pre">&quot;two-sided&quot;</span></code>.
 The two-sided shading mirrors the observed statistic about 0.</p>
 </section>
 <section id="one-mean-one-proportion-point-null">
 <h2>One mean / one proportion (point null)<a class="headerlink" href="#one-mean-one-proportion-point-null" title="Link to this heading">¶</a></h2>
 <p>Use a <code class="docutils literal notranslate"><span class="pre">&quot;point&quot;</span></code> null with bootstrap resampling, supplying the hypothesized value:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">age</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_age_at_marriage</span><span class="p">()</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">age</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_age_at_marriage</span><span class="p">()</span>
 
 <span class="n">obs_t</span> <span class="o">=</span> <span class="n">observe</span><span class="p">(</span><span class="n">age</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">,</span> <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;t&quot;</span><span class="p">,</span> <span class="n">null</span><span class="o">=</span><span class="s2">&quot;point&quot;</span><span class="p">,</span> <span class="n">mu</span><span class="o">=</span><span class="mi">23</span><span class="p">)</span>
 
@@ -307,8 +332,21 @@ <h2>One mean / one proportion (point null)<a class="headerlink" href="#one-mean-
 <span class="n">get_p_value</span><span class="p">(</span><span class="n">null_t</span><span class="p">,</span> <span class="n">obs_stat</span><span class="o">=</span><span class="n">obs_t</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;two-sided&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (1, 1)</small><table border="1" class="dataframe"><thead><tr><th>p_value</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.0</td></tr></tbody></table></div></div></div>
+</div>
 <p>For a one-proportion test you can also <em>simulate</em> draws directly:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">polars</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pl</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">polars</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pl</span>
 
 <span class="n">coins</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;flip&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;heads&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">30</span> <span class="o">+</span> <span class="p">[</span><span class="s2">&quot;tails&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">70</span><span class="p">})</span>
 
@@ -320,6 +358,8 @@ <h2>One mean / one proportion (point null)<a class="headerlink" href="#one-mean-
 <span class="p">)</span>
 </pre></div>
 </div>
+</div>
+</div>
 </section>
 <section id="available-statistics">
 <h2>Available statistics<a class="headerlink" href="#available-statistics" title="Link to this heading">¶</a></h2>
diff --git a/doc/_build/html/guides/plotting.html b/doc/_build/html/guides/plotting.html
index 631b0d4..b759d8e 100644
--- a/doc/_build/html/guides/plotting.html
+++ b/doc/_build/html/guides/plotting.html
@@ -11,6 +11,7 @@
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -251,7 +252,9 @@
           </label>
         </div>
         <article role="main" id="furo-main-content">
-          <section id="plotting-plotly-plotnine">
+          <div class="cell tag_remove-input docutils container">
+</div>
+<section id="plotting-plotly-plotnine">
 <h1>Plotting: plotly &amp; plotnine<a class="headerlink" href="#plotting-plotly-plotnine" title="Link to this heading">¶</a></h1>
 <p>Every plotting function in <code class="docutils literal notranslate"><span class="pre">moderndive</span></code> takes an <code class="docutils literal notranslate"><span class="pre">engine=</span></code> argument:</p>
 <ul class="simple">
@@ -259,13 +262,15 @@ <h1>Plotting: plotly &amp; plotnine<a class="headerlink" href="#plotting-plotly-
 <li><p><code class="docutils literal notranslate"><span class="pre">engine=&quot;plotnine&quot;</span></code> — grammar-of-graphics figures (<code class="docutils literal notranslate"><span class="pre">plotnine.ggplot</span></code>).</p></li>
 </ul>
 <p>The composition syntax is identical across engines, so you can switch a whole
-analysis by changing one argument.</p>
-<section id="visualize-returns-an-inferplot">
-<h2>visualize() returns an InferPlot<a class="headerlink" href="#visualize-returns-an-inferplot" title="Link to this heading">¶</a></h2>
-<p><code class="docutils literal notranslate"><span class="pre">visualize()</span></code> returns a small <code class="docutils literal notranslate"><span class="pre">InferPlot</span></code> wrapper that you compose with shading via
-<code class="docutils literal notranslate"><span class="pre">+</span></code> in <strong>both</strong> engines:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span><span class="p">,</span> <span class="n">get_confidence_interval</span><span class="p">,</span> <span class="n">visualize</span><span class="p">,</span> <span class="n">shade_confidence_interval</span>
+analysis by changing one argument. (In this documentation, plotly figures are
+shown as static images; in a notebook they’re fully interactive.)</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
+    <span class="n">specify</span><span class="p">,</span> <span class="n">observe</span><span class="p">,</span> <span class="n">get_confidence_interval</span><span class="p">,</span>
+    <span class="n">visualize</span><span class="p">,</span> <span class="n">shade_confidence_interval</span><span class="p">,</span> <span class="n">shade_p_value</span><span class="p">,</span>
+<span class="p">)</span>
 
 <span class="n">boot</span> <span class="o">=</span> <span class="p">(</span>
     <span class="n">specify</span><span class="p">(</span><span class="n">md</span><span class="o">.</span><span class="n">load_almonds_sample_100</span><span class="p">(),</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span><span class="p">)</span>
@@ -273,77 +278,169 @@ <h2>visualize() returns an InferPlot<a class="headerlink" href="#visualize-retur
     <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">)</span>
 <span class="p">)</span>
 <span class="n">ci</span> <span class="o">=</span> <span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;percentile&quot;</span><span class="p">)</span>
-
-<span class="n">p</span> <span class="o">=</span> <span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_confidence_interval</span><span class="p">(</span><span class="n">ci</span><span class="p">)</span>   <span class="c1"># plotly InferPlot</span>
-<span class="n">p</span><span class="o">.</span><span class="n">figure</span>          <span class="c1"># the underlying plotly Figure</span>
-<span class="n">p</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>          <span class="c1"># display it</span>
+<span class="n">ci</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (1, 2)</small><table border="1" class="dataframe"><thead><tr><th>lower_ci</th><th>upper_ci</th></tr><tr><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>3.606975</td><td>3.75</td></tr></tbody></table></div></div></div>
+</div>
+<section id="visualize-returns-an-inferplot">
+<h2>visualize() returns an InferPlot<a class="headerlink" href="#visualize-returns-an-inferplot" title="Link to this heading">¶</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">visualize()</span></code> returns a small <code class="docutils literal notranslate"><span class="pre">InferPlot</span></code> wrapper that you compose with shading via
+<code class="docutils literal notranslate"><span class="pre">+</span></code> in <strong>both</strong> engines. The underlying figure is on <code class="docutils literal notranslate"><span class="pre">.figure</span></code> (and, for plotnine,
+the raw <code class="docutils literal notranslate"><span class="pre">ggplot</span></code> on <code class="docutils literal notranslate"><span class="pre">.gg</span></code>).</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_confidence_interval</span><span class="p">(</span><span class="n">ci</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>For the plotnine engine, the raw <code class="docutils literal notranslate"><span class="pre">ggplot</span></code> is available via <code class="docutils literal notranslate"><span class="pre">.gg</span></code>:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">g</span> <span class="o">=</span> <span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s2">&quot;plotnine&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_confidence_interval</span><span class="p">(</span><span class="n">ci</span><span class="p">)</span>
-<span class="n">g</span><span class="o">.</span><span class="n">gg</span>              <span class="c1"># the underlying plotnine ggplot</span>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/ac66f688faad576ec00fb6dfa22ab9d34977edb8a0a5ff3cadb158ce22be94fd.png" src="../_images/ac66f688faad576ec00fb6dfa22ab9d34977edb8a0a5ff3cadb158ce22be94fd.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># the same plot via the plotnine engine</span>
+<span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s2">&quot;plotnine&quot;</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_confidence_interval</span><span class="p">(</span><span class="n">ci</span><span class="p">)</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../_images/a1ca833e74d2d2988b8271748b2439c6db0fb6d52aa565c9c0cf8dfd523736eb.png"><img alt="../_images/a1ca833e74d2d2988b8271748b2439c6db0fb6d52aa565c9c0cf8dfd523736eb.png" height="480" src="../_images/a1ca833e74d2d2988b8271748b2439c6db0fb6d52aa565c9c0cf8dfd523736eb.png" width="640" />
+</a>
+</div>
+</div>
 <section id="keyword-form">
 <h3>Keyword form<a class="headerlink" href="#keyword-form" title="Link to this heading">¶</a></h3>
 <p>If you’d rather not use <code class="docutils literal notranslate"><span class="pre">+</span></code>, pass the shading inline (handy for plotly):</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">shade_ci</span><span class="o">=</span><span class="n">ci</span><span class="p">)</span>
-<span class="n">visualize</span><span class="p">(</span><span class="n">null</span><span class="p">,</span> <span class="n">shade_pvalue</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;obs_stat&quot;</span><span class="p">:</span> <span class="n">obs</span><span class="p">,</span> <span class="s2">&quot;direction&quot;</span><span class="p">:</span> <span class="s2">&quot;right&quot;</span><span class="p">})</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">shade_ci</span><span class="o">=</span><span class="n">ci</span><span class="p">)</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/ac66f688faad576ec00fb6dfa22ab9d34977edb8a0a5ff3cadb158ce22be94fd.png" src="../_images/ac66f688faad576ec00fb6dfa22ab9d34977edb8a0a5ff3cadb158ce22be94fd.png" />
+</div>
+</div>
 </section>
 </section>
 <section id="shading-p-values">
 <h2>Shading p-values<a class="headerlink" href="#shading-p-values" title="Link to this heading">¶</a></h2>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">observe</span><span class="p">,</span> <span class="n">shade_p_value</span>
-
-<span class="n">promotions</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_promotions</span><span class="p">()</span>
-<span class="n">obs</span> <span class="o">=</span> <span class="n">observe</span><span class="p">(</span><span class="n">promotions</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;decision ~ gender&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;promoted&quot;</span><span class="p">,</span>
-              <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;male&quot;</span><span class="p">,</span> <span class="s2">&quot;female&quot;</span><span class="p">))</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">spotify</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_spotify_metal_deephouse</span><span class="p">()</span>
+<span class="n">obs</span> <span class="o">=</span> <span class="n">observe</span><span class="p">(</span><span class="n">spotify</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;popular_or_not ~ track_genre&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;popular&quot;</span><span class="p">,</span>
+              <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;metal&quot;</span><span class="p">,</span> <span class="s2">&quot;deep-house&quot;</span><span class="p">))</span>
 <span class="n">null</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">promotions</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;decision ~ gender&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;promoted&quot;</span><span class="p">)</span>
+    <span class="n">specify</span><span class="p">(</span><span class="n">spotify</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;popular_or_not ~ track_genre&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;popular&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;independence&quot;</span><span class="p">)</span>
-    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;permute&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
-    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;male&quot;</span><span class="p">,</span> <span class="s2">&quot;female&quot;</span><span class="p">))</span>
+    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;permute&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">76</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;metal&quot;</span><span class="p">,</span> <span class="s2">&quot;deep-house&quot;</span><span class="p">))</span>
 <span class="p">)</span>
-
 <span class="n">visualize</span><span class="p">(</span><span class="n">null</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_p_value</span><span class="p">(</span><span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/57bcfc0de676a5294afac27836489f257436cfdc208aba5a2907c8b2aa95d9f8.png" src="../_images/57bcfc0de676a5294afac27836489f257436cfdc208aba5a2907c8b2aa95d9f8.png" />
+</div>
+</div>
 </section>
 <section id="simulation-vs-theory-overlays">
 <h2>Simulation vs. theory overlays<a class="headerlink" href="#simulation-vs-theory-overlays" title="Link to this heading">¶</a></h2>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;simulation&quot;</span><span class="p">)</span>    <span class="c1"># histogram (default)</span>
-<span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;theoretical&quot;</span><span class="p">)</span>   <span class="c1"># normal-approximation curve</span>
-<span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">)</span>          <span class="c1"># histogram + curve overlaid</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;theoretical&quot;</span><span class="p">)</span>   <span class="c1"># normal-approximation curve</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/b356fae18e59a76c6630e1f9a522d1b99306e56b3817f2c09a7547d10157d1df.png" src="../_images/b356fae18e59a76c6630e1f9a522d1b99306e56b3817f2c09a7547d10157d1df.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">)</span>          <span class="c1"># histogram + curve overlaid</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/146f21e1e2cfebef569cb551e24b0c5826d3fe84072e2197538258975eba8070.png" src="../_images/146f21e1e2cfebef569cb551e24b0c5826d3fe84072e2197538258975eba8070.png" />
+</div>
+</div>
 </section>
 <section id="faceted-regression-coefficient-plots">
 <h2>Faceted regression-coefficient plots<a class="headerlink" href="#faceted-regression-coefficient-plots" title="Link to this heading">¶</a></h2>
 <p><code class="docutils literal notranslate"><span class="pre">visualize_fit</span></code> shows one panel per term, and shading is <strong>per-facet</strong>:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive.infer.viz</span><span class="w"> </span><span class="kn">import</span> <span class="n">visualize_fit</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive.infer.viz</span><span class="w"> </span><span class="kn">import</span> <span class="n">visualize_fit</span>
 
-<span class="n">f</span> <span class="o">=</span> <span class="s2">&quot;price ~ living_area + bedrooms&quot;</span>
 <span class="n">houses</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_saratoga_houses</span><span class="p">()</span>
-<span class="n">boot_fit</span> <span class="o">=</span> <span class="n">specify</span><span class="p">(</span><span class="n">houses</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
-
+<span class="n">boot_fit</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">specify</span><span class="p">(</span><span class="n">houses</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;price ~ living_area + bedrooms&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+<span class="p">)</span>
 <span class="n">visualize_fit</span><span class="p">(</span><span class="n">boot_fit</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_confidence_interval</span><span class="p">(</span><span class="n">boot_fit</span><span class="o">.</span><span class="n">get_confidence_interval</span><span class="p">())</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/65edcbd2c398320bee92dd5442e64f14d1af22f756a002ca29e0d49a56241eaa.png" src="../_images/65edcbd2c398320bee92dd5442e64f14d1af22f756a002ca29e0d49a56241eaa.png" />
+</div>
+</div>
 </section>
 <section id="model-plots">
 <h2>Model plots<a class="headerlink" href="#model-plots" title="Link to this heading">¶</a></h2>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">gg_parallel_slopes</span><span class="p">,</span> <span class="n">gg_categorical_model</span><span class="p">,</span> <span class="n">pairplot</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">gg_parallel_slopes</span>
 
 <span class="n">evals</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_evals</span><span class="p">()</span>
 <span class="n">gg_parallel_slopes</span><span class="p">(</span><span class="n">evals</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">explanatory</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">,</span> <span class="n">by</span><span class="o">=</span><span class="s2">&quot;gender&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/1d823747e0f1ecebe3da106ec64b1ad73d4d55547d8814ff4c011efafc6354bc.png" src="../_images/1d823747e0f1ecebe3da106ec64b1ad73d4d55547d8814ff4c011efafc6354bc.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">gg_categorical_model</span>
+
 <span class="n">gg_categorical_model</span><span class="p">(</span><span class="n">evals</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">explanatory</span><span class="o">=</span><span class="s2">&quot;rank&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/c095fdc806a901609764f3a83013d31798e7b884fd7e4d4c51f4edb0142b30cf.png" src="../_images/c095fdc806a901609764f3a83013d31798e7b884fd7e4d4c51f4edb0142b30cf.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">pairplot</span>
 
 <span class="c1"># Scatterplot matrix (~ GGally::ggpairs)</span>
 <span class="n">pairplot</span><span class="p">(</span><span class="n">md</span><span class="o">.</span><span class="n">load_coffee_quality</span><span class="p">(),</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;total_cup_points&quot;</span><span class="p">,</span> <span class="s2">&quot;aroma&quot;</span><span class="p">,</span> <span class="s2">&quot;flavor&quot;</span><span class="p">])</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/ac9c0dd7643fbf405d9039110a0f70c17f154b4de622875f4f1404927f71b3a3.png" src="../_images/ac9c0dd7643fbf405d9039110a0f70c17f154b4de622875f4f1404927f71b3a3.png" />
+</div>
+</div>
 </section>
 <section id="saving-figures">
 <h2>Saving figures<a class="headerlink" href="#saving-figures" title="Link to this heading">¶</a></h2>
diff --git a/doc/_build/html/guides/regression.html b/doc/_build/html/guides/regression.html
index 1f82304..c3b3698 100644
--- a/doc/_build/html/guides/regression.html
+++ b/doc/_build/html/guides/regression.html
@@ -11,6 +11,7 @@
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -251,13 +252,17 @@
           </label>
         </div>
         <article role="main" id="furo-main-content">
-          <section id="regression">
+          <div class="cell tag_remove-input docutils container">
+</div>
+<section id="regression">
 <h1>Regression<a class="headerlink" href="#regression" title="Link to this heading">¶</a></h1>
 <p>The regression helpers turn a fitted <a class="reference external" href="https://www.statsmodels.org">statsmodels</a>
 model into tidy polars tables — the analog of R <code class="docutils literal notranslate"><span class="pre">moderndive</span></code>’s
 <code class="docutils literal notranslate"><span class="pre">get_regression_table()</span></code> / <code class="docutils literal notranslate"><span class="pre">get_regression_points()</span></code> / <code class="docutils literal notranslate"><span class="pre">get_regression_summaries()</span></code>.</p>
 <p>Fit models with statsmodels’ formula API, then tidy them:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">statsmodels.formula.api</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">smf</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">statsmodels.formula.api</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">smf</span>
 <span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
     <span class="n">get_regression_table</span><span class="p">,</span> <span class="n">get_regression_points</span><span class="p">,</span> <span class="n">get_regression_summaries</span><span class="p">,</span> <span class="n">get_correlation</span><span class="p">,</span>
@@ -267,48 +272,94 @@ <h1>Regression<a class="headerlink" href="#regression" title="Link to this headi
 <span class="n">model</span> <span class="o">=</span> <span class="n">smf</span><span class="o">.</span><span class="n">ols</span><span class="p">(</span><span class="s2">&quot;price ~ living_area + bedrooms&quot;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">houses</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">())</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
 </pre></div>
 </div>
+</div>
+</div>
 <section id="coefficient-table-with-confidence-intervals">
 <h2>Coefficient table (with confidence intervals)<a class="headerlink" href="#coefficient-table-with-confidence-intervals" title="Link to this heading">¶</a></h2>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">get_regression_table</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">get_regression_table</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>shape: (3, 7)
-┌─────────────┬───────────┬───────────┬───────────┬─────────┬────────────┬───────────┐
-│ term        ┆ estimate  ┆ std_error ┆ statistic ┆ p_value ┆ lower_ci   ┆ upper_ci  │
-╞═════════════╪═══════════╪═══════════╪═══════════╪═════════╪════════════╪═══════════╡
-│ intercept   ┆ 20986.094 ┆ 6816.251  ┆ 3.079     ┆ 0.002   ┆ 7611.128   ┆ 34361.06  │
-│ living_area ┆ 93.842    ┆ 3.109     ┆ 30.183    ┆ 0.0     ┆ 87.741     ┆ 99.943    │
-│ bedrooms    ┆ -7483.095 ┆ 2783.531  ┆ -2.688    ┆ 0.007   ┆ -12944.988 ┆ -2021.203 │
-└─────────────┴───────────┴───────────┴───────────┴─────────┴────────────┴───────────┘
-</pre></div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (3, 7)</small><table border="1" class="dataframe"><thead><tr><th>term</th><th>estimate</th><th>std_error</th><th>statistic</th><th>p_value</th><th>lower_ci</th><th>upper_ci</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;intercept&quot;</td><td>20986.094</td><td>6816.251</td><td>3.079</td><td>0.002</td><td>7611.128</td><td>34361.06</td></tr><tr><td>&quot;living_area&quot;</td><td>93.842</td><td>3.109</td><td>30.183</td><td>0.0</td><td>87.741</td><td>99.943</td></tr><tr><td>&quot;bedrooms&quot;</td><td>-7483.095</td><td>2783.531</td><td>-2.688</td><td>0.007</td><td>-12944.988</td><td>-2021.203</td></tr></tbody></table></div></div></div>
 </div>
 <p>Change the confidence level with <code class="docutils literal notranslate"><span class="pre">conf_level=</span></code> (e.g. <code class="docutils literal notranslate"><span class="pre">0.99</span></code>).</p>
 </section>
 <section id="fitted-values-residuals">
 <h2>Fitted values &amp; residuals<a class="headerlink" href="#fitted-values-residuals" title="Link to this heading">¶</a></h2>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">get_regression_points</span><span class="p">(</span><span class="n">model</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">get_regression_points</span><span class="p">(</span><span class="n">model</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 <span class="c1"># columns: ID, price, living_area, bedrooms, price_hat, residual</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (5, 6)</small><table border="1" class="dataframe"><thead><tr><th>ID</th><th>price</th><th>living_area</th><th>bedrooms</th><th>price_hat</th><th>residual</th></tr><tr><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>1</td><td>142212</td><td>1982</td><td>3</td><td>184531.942</td><td>-42319.942</td></tr><tr><td>2</td><td>134865</td><td>1676</td><td>3</td><td>155816.245</td><td>-20951.245</td></tr><tr><td>3</td><td>118007</td><td>1694</td><td>3</td><td>157505.404</td><td>-39498.404</td></tr><tr><td>4</td><td>138297</td><td>1800</td><td>2</td><td>174935.767</td><td>-36638.767</td></tr><tr><td>5</td><td>129470</td><td>2088</td><td>3</td><td>194479.21</td><td>-65009.21</td></tr></tbody></table></div></div></div>
+</div>
 </section>
 <section id="model-fit-summaries">
 <h2>Model-fit summaries<a class="headerlink" href="#model-fit-summaries" title="Link to this heading">¶</a></h2>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">get_regression_summaries</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">get_regression_summaries</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
 <span class="c1"># r_squared, adj_r_squared, mse, rmse, sigma, statistic (F), p_value, df, nobs</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (1, 9)</small><table border="1" class="dataframe"><thead><tr><th>r_squared</th><th>adj_r_squared</th><th>mse</th><th>rmse</th><th>sigma</th><th>statistic</th><th>p_value</th><th>df</th><th>nobs</th></tr><tr><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i64</td><td>i64</td></tr></thead><tbody><tr><td>0.578</td><td>0.578</td><td>2.5071e9</td><td>50071.187</td><td>50142.395</td><td>723.229</td><td>0.0</td><td>2</td><td>1057</td></tr></tbody></table></div></div></div>
+</div>
 </section>
 <section id="correlation">
 <h2>Correlation<a class="headerlink" href="#correlation" title="Link to this heading">¶</a></h2>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">get_correlation</span><span class="p">(</span><span class="n">houses</span><span class="p">,</span> <span class="s2">&quot;price ~ living_area&quot;</span><span class="p">)</span>   <span class="c1"># ≈ 0.759</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">get_correlation</span><span class="p">(</span><span class="n">houses</span><span class="p">,</span> <span class="s2">&quot;price ~ living_area&quot;</span><span class="p">)</span>   <span class="c1"># ≈ 0.759</span>
 <span class="c1"># or: get_correlation(houses, x=&quot;living_area&quot;, y=&quot;price&quot;)</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (1, 1)</small><table border="1" class="dataframe"><thead><tr><th>cor</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.758674</td></tr></tbody></table></div></div></div>
+</div>
 </section>
 <section id="visualizing-models">
 <h2>Visualizing models<a class="headerlink" href="#visualizing-models" title="Link to this heading">¶</a></h2>
 <p>Two ports of R <code class="docutils literal notranslate"><span class="pre">moderndive</span></code>’s ggplot helpers, both dual-engine:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">gg_parallel_slopes</span><span class="p">,</span> <span class="n">gg_categorical_model</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">gg_parallel_slopes</span><span class="p">,</span> <span class="n">gg_categorical_model</span>
 
 <span class="n">evals</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_evals</span><span class="p">()</span>
 
@@ -321,6 +372,11 @@ <h2>Visualizing models<a class="headerlink" href="#visualizing-models" title="Li
 <span class="n">gg_categorical_model</span><span class="p">(</span><span class="n">evals</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;score&quot;</span><span class="p">,</span> <span class="n">explanatory</span><span class="o">=</span><span class="s2">&quot;rank&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/c095fdc806a901609764f3a83013d31798e7b884fd7e4d4c51f4edb0142b30cf.png" src="../_images/c095fdc806a901609764f3a83013d31798e7b884fd7e4d4c51f4edb0142b30cf.png" />
+</div>
+</div>
 <p>For the plotnine engine you can also drop the parallel-slopes lines onto your own
 <code class="docutils literal notranslate"><span class="pre">ggplot</span></code> with <code class="docutils literal notranslate"><span class="pre">geom_parallel_slopes()</span></code>.</p>
 </section>
@@ -329,7 +385,9 @@ <h2>Inference for regression coefficients<a class="headerlink" href="#inference-
 <p><code class="docutils literal notranslate"><span class="pre">fit()</span></code> runs the regression on every bootstrap/permutation replicate, giving a
 distribution per coefficient. Pair it with <code class="docutils literal notranslate"><span class="pre">visualize_fit</span></code> and <strong>per-facet</strong>
 shading:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">moderndive.infer.viz</span><span class="w"> </span><span class="kn">import</span> <span class="n">visualize_fit</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">shade_confidence_interval</span><span class="p">,</span> <span class="n">shade_p_value</span>
 
@@ -350,6 +408,11 @@ <h2>Inference for regression coefficients<a class="headerlink" href="#inference-
 <span class="n">visualize_fit</span><span class="p">(</span><span class="n">null_fit</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_p_value</span><span class="p">(</span><span class="n">obs_stat</span><span class="o">=</span><span class="n">obs_fit</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;two-sided&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/4f6137e2efe6c6b33540a969fb71e0dc9370f9392a068c674921b84116e9ca18.png" src="../_images/4f6137e2efe6c6b33540a969fb71e0dc9370f9392a068c674921b84116e9ca18.png" />
+</div>
+</div>
 <p>Per-facet shading works in both engines: <code class="docutils literal notranslate"><span class="pre">shade_p_value</span></code> / <code class="docutils literal notranslate"><span class="pre">shade_confidence_interval</span></code>
 accept the observed <code class="docutils literal notranslate"><span class="pre">FitResult</span></code> or a term-keyed table, and each facet is shaded
 from its own value.</p>
diff --git a/doc/_build/html/guides/sampling.html b/doc/_build/html/guides/sampling.html
index 0a95e8f..8f3ae4c 100644
--- a/doc/_build/html/guides/sampling.html
+++ b/doc/_build/html/guides/sampling.html
@@ -11,6 +11,7 @@
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -251,7 +252,9 @@
           </label>
         </div>
         <article role="main" id="furo-main-content">
-          <section id="sampling">
+          <div class="cell tag_remove-input docutils container">
+</div>
+<section id="sampling">
 <h1>Sampling<a class="headerlink" href="#sampling" title="Link to this heading">¶</a></h1>
 <p>Sampling activities (the “bowl” of balls, tactile samples) are how ModernDive
 builds intuition for sampling variation. <code class="docutils literal notranslate"><span class="pre">rep_slice_sample</span></code> takes repeated samples
@@ -259,28 +262,56 @@ <h1>Sampling<a class="headerlink" href="#sampling" title="Link to this heading">
 <code class="docutils literal notranslate"><span class="pre">moderndive::rep_slice_sample()</span></code> / <code class="docutils literal notranslate"><span class="pre">infer::rep_sample_n()</span></code>.</p>
 <section id="the-bowl">
 <h2>The bowl<a class="headerlink" href="#the-bowl" title="Link to this heading">¶</a></h2>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
 <span class="kn">import</span><span class="w"> </span><span class="nn">polars</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pl</span>
 
 <span class="n">bowl</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_bowl</span><span class="p">()</span>      <span class="c1"># 2400 red/white balls</span>
 <span class="n">bowl</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (5, 2)</small><table border="1" class="dataframe"><thead><tr><th>ball_ID</th><th>color</th></tr><tr><td>i64</td><td>str</td></tr></thead><tbody><tr><td>1</td><td>&quot;white&quot;</td></tr><tr><td>2</td><td>&quot;white&quot;</td></tr><tr><td>3</td><td>&quot;white&quot;</td></tr><tr><td>4</td><td>&quot;red&quot;</td></tr><tr><td>5</td><td>&quot;white&quot;</td></tr></tbody></table></div></div></div>
+</div>
 </section>
 <section id="one-virtual-sample">
 <h2>One virtual sample<a class="headerlink" href="#one-virtual-sample" title="Link to this heading">¶</a></h2>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">rep_slice_sample</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">rep_slice_sample</span>
 
 <span class="n">sample</span> <span class="o">=</span> <span class="n">rep_slice_sample</span><span class="p">(</span><span class="n">bowl</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 <span class="c1"># proportion red in this sample</span>
 <span class="n">sample</span><span class="o">.</span><span class="n">select</span><span class="p">((</span><span class="n">pl</span><span class="o">.</span><span class="n">col</span><span class="p">(</span><span class="s2">&quot;color&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;red&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="s2">&quot;prop_red&quot;</span><span class="p">))</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (1, 1)</small><table border="1" class="dataframe"><thead><tr><th>prop_red</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.38</td></tr></tbody></table></div></div></div>
+</div>
 </section>
 <section id="many-samples-a-sampling-distribution">
 <h2>Many samples → a sampling distribution<a class="headerlink" href="#many-samples-a-sampling-distribution" title="Link to this heading">¶</a></h2>
 <p>Take 1000 samples of size 50 and compute the proportion red in each:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">samples</span> <span class="o">=</span> <span class="n">rep_slice_sample</span><span class="p">(</span><span class="n">bowl</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">samples</span> <span class="o">=</span> <span class="n">rep_slice_sample</span><span class="p">(</span><span class="n">bowl</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 
 <span class="n">prop_red</span> <span class="o">=</span> <span class="p">(</span>
     <span class="n">samples</span>
@@ -290,6 +321,17 @@ <h2>Many samples → a sampling distribution<a class="headerlink" href="#many-sa
 <span class="n">prop_red</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (5, 2)</small><table border="1" class="dataframe"><thead><tr><th>replicate</th><th>prop_red</th></tr><tr><td>i64</td><td>f64</td></tr></thead><tbody><tr><td>1</td><td>0.38</td></tr><tr><td>2</td><td>0.42</td></tr><tr><td>3</td><td>0.42</td></tr><tr><td>4</td><td>0.42</td></tr><tr><td>5</td><td>0.46</td></tr></tbody></table></div></div></div>
+</div>
 <p>That <code class="docutils literal notranslate"><span class="pre">prop_red</span></code> column is a <strong>sampling distribution</strong>. Visualize its spread the
 same way you would any distribution (see <a class="reference internal" href="confidence-intervals.html"><span class="doc">Bootstrapping &amp; confidence intervals</span></a> for building
 one with the inference pipeline instead).</p>
@@ -298,16 +340,42 @@ <h2>Many samples → a sampling distribution<a class="headerlink" href="#many-sa
 <h2>With vs. without replacement<a class="headerlink" href="#with-vs-without-replacement" title="Link to this heading">¶</a></h2>
 <p><code class="docutils literal notranslate"><span class="pre">rep_slice_sample</span></code> samples <strong>without</strong> replacement by default (like dealing from a
 deck). Pass <code class="docutils literal notranslate"><span class="pre">replace=True</span></code> for bootstrap-style resampling:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">rep_slice_sample</span><span class="p">(</span><span class="n">bowl</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">rep_slice_sample</span><span class="p">(</span><span class="n">bowl</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (50_000, 3)</small><table border="1" class="dataframe"><thead><tr><th>replicate</th><th>ball_ID</th><th>color</th></tr><tr><td>i64</td><td>i64</td><td>str</td></tr></thead><tbody><tr><td>1</td><td>1136</td><td>&quot;white&quot;</td></tr><tr><td>1</td><td>1229</td><td>&quot;red&quot;</td></tr><tr><td>1</td><td>1813</td><td>&quot;white&quot;</td></tr><tr><td>1</td><td>2282</td><td>&quot;red&quot;</td></tr><tr><td>1</td><td>84</td><td>&quot;white&quot;</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>1000</td><td>439</td><td>&quot;white&quot;</td></tr><tr><td>1000</td><td>1203</td><td>&quot;red&quot;</td></tr><tr><td>1000</td><td>252</td><td>&quot;white&quot;</td></tr><tr><td>1000</td><td>1113</td><td>&quot;white&quot;</td></tr><tr><td>1000</td><td>1277</td><td>&quot;red&quot;</td></tr></tbody></table></div></div></div>
+</div>
 </section>
 <section id="tactile-samples">
 <h2>Tactile samples<a class="headerlink" href="#tactile-samples" title="Link to this heading">¶</a></h2>
 <p>The hand-collected counterparts are bundled too:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">md</span><span class="o">.</span><span class="n">load_tactile_prop_red</span><span class="p">()</span>   <span class="c1"># 33 groups&#39; samples of 50 balls</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">md</span><span class="o">.</span><span class="n">load_tactile_prop_red</span><span class="p">()</span>   <span class="c1"># 33 groups&#39; samples of 50 balls</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (33, 4)</small><table border="1" class="dataframe"><thead><tr><th>group</th><th>replicate</th><th>red_balls</th><th>prop_red</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Ilyas, Yohan&quot;</td><td>1</td><td>21</td><td>0.42</td></tr><tr><td>&quot;Morgan, Terrance&quot;</td><td>2</td><td>17</td><td>0.34</td></tr><tr><td>&quot;Martin, Thomas&quot;</td><td>3</td><td>21</td><td>0.42</td></tr><tr><td>&quot;Clark, Frank&quot;</td><td>4</td><td>21</td><td>0.42</td></tr><tr><td>&quot;Riddhi, Karina&quot;</td><td>5</td><td>18</td><td>0.36</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;Julie, Hailin&quot;</td><td>29</td><td>15</td><td>0.3</td></tr><tr><td>&quot;Katie, Caroline&quot;</td><td>30</td><td>21</td><td>0.42</td></tr><tr><td>&quot;Mallory, Damani, Melissa&quot;</td><td>31</td><td>21</td><td>0.42</td></tr><tr><td>&quot;Katie&quot;</td><td>32</td><td>16</td><td>0.32</td></tr><tr><td>&quot;Francis, Vignesh&quot;</td><td>33</td><td>19</td><td>0.38</td></tr></tbody></table></div></div></div>
+</div>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
 <p><code class="docutils literal notranslate"><span class="pre">rep_sample_n</span></code> is an alias of <code class="docutils literal notranslate"><span class="pre">rep_slice_sample</span></code> (the older infer name). Both are
diff --git a/doc/_build/html/guides/theory-based.html b/doc/_build/html/guides/theory-based.html
index 1f60281..5360edf 100644
--- a/doc/_build/html/guides/theory-based.html
+++ b/doc/_build/html/guides/theory-based.html
@@ -11,6 +11,7 @@
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -251,14 +252,18 @@
           </label>
         </div>
         <article role="main" id="furo-main-content">
-          <section id="theory-based-inference">
+          <div class="cell tag_remove-input docutils container">
+</div>
+<section id="theory-based-inference">
 <h1>Theory-based inference<a class="headerlink" href="#theory-based-inference" title="Link to this heading">¶</a></h1>
 <p>Alongside the simulation grammar, <code class="docutils literal notranslate"><span class="pre">moderndive</span></code> ships tidy wrappers for the
 classical (formula-based) tests and a way to draw the theoretical distributions —
 mirroring R <code class="docutils literal notranslate"><span class="pre">infer</span></code>’s <code class="docutils literal notranslate"><span class="pre">t_test</span></code>, <code class="docutils literal notranslate"><span class="pre">prop_test</span></code>, <code class="docutils literal notranslate"><span class="pre">chisq_test</span></code>, and <code class="docutils literal notranslate"><span class="pre">assume()</span></code>.</p>
 <section id="one-line-tests">
 <h2>One-line tests<a class="headerlink" href="#one-line-tests" title="Link to this heading">¶</a></h2>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">t_test</span><span class="p">,</span> <span class="n">prop_test</span><span class="p">,</span> <span class="n">chisq_test</span>
 
 <span class="c1"># One-sample t-test</span>
@@ -279,13 +284,26 @@ <h2>One-line tests<a class="headerlink" href="#one-line-tests" title="Link to th
 <span class="c1"># → statistic, chisq_df, p_value</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (1, 3)</small><table border="1" class="dataframe"><thead><tr><th>statistic</th><th>chisq_df</th><th>p_value</th></tr><tr><td>f64</td><td>i64</td><td>f64</td></tr></thead><tbody><tr><td>0.105042</td><td>1</td><td>0.745861</td></tr></tbody></table></div></div></div>
+</div>
 <p><code class="docutils literal notranslate"><span class="pre">t_stat</span></code> and <code class="docutils literal notranslate"><span class="pre">chisq_stat</span></code> return just the test statistic if that’s all you need.</p>
 </section>
 <section id="theoretical-distributions-with-assume">
 <h2>Theoretical distributions with <code class="docutils literal notranslate"><span class="pre">assume()</span></code><a class="headerlink" href="#theoretical-distributions-with-assume" title="Link to this heading">¶</a></h2>
 <p><code class="docutils literal notranslate"><span class="pre">assume()</span></code> defines a theoretical sampling distribution you can visualize and use
 for p-values without simulating:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">assume</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">assume</span>
 
 <span class="c1"># t-distribution with 10 degrees of freedom</span>
 <span class="n">t_dist</span> <span class="o">=</span> <span class="n">assume</span><span class="p">(</span><span class="s2">&quot;t&quot;</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
@@ -296,6 +314,12 @@ <h2>Theoretical distributions with <code class="docutils literal notranslate"><s
 <span class="n">t_dist</span><span class="o">.</span><span class="n">visualize</span><span class="p">(</span><span class="n">engine</span><span class="o">=</span><span class="s2">&quot;plotnine&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<a class="reference internal image-reference" href="../_images/0cbf7366b5b2086def6aceec9bf8e8320703e5fcdb1857544f8d601b5ef8edd9.png"><img alt="../_images/0cbf7366b5b2086def6aceec9bf8e8320703e5fcdb1857544f8d601b5ef8edd9.png" height="480" src="../_images/0cbf7366b5b2086def6aceec9bf8e8320703e5fcdb1857544f8d601b5ef8edd9.png" width="640" />
+</a>
+</div>
+</div>
 <p>Supported distributions: <code class="docutils literal notranslate"><span class="pre">&quot;t&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;z&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;F&quot;</span></code> (pass <code class="docutils literal notranslate"><span class="pre">df=(df1,</span> <span class="pre">df2)</span></code>), and
 <code class="docutils literal notranslate"><span class="pre">&quot;Chisq&quot;</span></code>.</p>
 </section>
@@ -303,7 +327,9 @@ <h2>Theoretical distributions with <code class="docutils literal notranslate"><s
 <h2>Overlaying theory on a simulation<a class="headerlink" href="#overlaying-theory-on-a-simulation" title="Link to this heading">¶</a></h2>
 <p><code class="docutils literal notranslate"><span class="pre">visualize(...,</span> <span class="pre">method=...)</span></code> can show the simulation histogram, the theoretical
 curve, or both:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span><span class="p">,</span> <span class="n">visualize</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span><span class="p">,</span> <span class="n">visualize</span>
 
 <span class="n">boot</span> <span class="o">=</span> <span class="p">(</span>
     <span class="n">specify</span><span class="p">(</span><span class="n">age</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">)</span>
@@ -315,15 +341,29 @@ <h2>Overlaying theory on a simulation<a class="headerlink" href="#overlaying-the
 <span class="n">visualize</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;theoretical&quot;</span><span class="p">)</span>   <span class="c1"># just the curve</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../_images/b366a9ba76d2135e3480197df1efa63a52fcc6c3aade26c457542b5eae9607b8.png" src="../_images/b366a9ba76d2135e3480197df1efa63a52fcc6c3aade26c457542b5eae9607b8.png" />
+</div>
+</div>
 </section>
 <section id="population-standard-deviation">
 <h2>Population standard deviation<a class="headerlink" href="#population-standard-deviation" title="Link to this heading">¶</a></h2>
 <p>A small helper that divides by <code class="docutils literal notranslate"><span class="pre">n</span></code> (not <code class="docutils literal notranslate"><span class="pre">n</span> <span class="pre">−</span> <span class="pre">1</span></code>):</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">pop_sd</span>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">pop_sd</span>
 
 <span class="n">pop_sd</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>   <span class="c1"># → 1.414…</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1.4142135623730951
+</pre></div>
+</div>
+</div>
+</div>
 </section>
 </section>
 
diff --git a/doc/_build/html/index.html b/doc/_build/html/index.html
index dd5e83a..a25a3c6 100644
--- a/doc/_build/html/index.html
+++ b/doc/_build/html/index.html
@@ -11,6 +11,7 @@
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
@@ -251,7 +252,9 @@
           </label>
         </div>
         <article role="main" id="furo-main-content">
-          <section id="moderndive-python">
+          <div class="cell tag_remove-input docutils container">
+</div>
+<section id="moderndive-python">
 <h1>moderndive (Python)<a class="headerlink" href="#moderndive-python" title="Link to this heading">¶</a></h1>
 <p>The Python companion package for <strong>ModernDive: Statistical Inference via Data
 Science</strong> — a faithful port of the R <a class="reference external" href="https://moderndive.github.io/moderndive/"><code class="docutils literal notranslate"><span class="pre">moderndive</span></code></a>
@@ -286,33 +289,65 @@ <h2>Installation<a class="headerlink" href="#installation" title="Link to this h
 </section>
 <section id="second-example">
 <h2>30-second example<a class="headerlink" href="#second-example" title="Link to this heading">¶</a></h2>
-<p>Did promotion decisions depend on the applicant’s (perceived) gender? Shuffle the
-labels 1000 times to build a null distribution and read off a p-value:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
+<p>Are tracks more likely to be popular in <em>metal</em> than in <em>deep house</em>? Compute the
+observed difference in “popular” rates, then shuffle the genre labels 1000 times
+to build a null distribution and read off a p-value.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span><span class="p">,</span> <span class="n">observe</span><span class="p">,</span> <span class="n">get_p_value</span><span class="p">,</span> <span class="n">visualize</span><span class="p">,</span> <span class="n">shade_p_value</span>
 
-<span class="n">promotions</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_promotions</span><span class="p">()</span>
+<span class="n">spotify</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_spotify_metal_deephouse</span><span class="p">()</span>
 
-<span class="c1"># Observed difference in promotion rates (male − female)</span>
+<span class="c1"># Observed difference in &quot;popular&quot; proportions (metal − deep-house)</span>
 <span class="n">obs</span> <span class="o">=</span> <span class="n">observe</span><span class="p">(</span>
-    <span class="n">promotions</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;decision ~ gender&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;promoted&quot;</span><span class="p">,</span>
-    <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;male&quot;</span><span class="p">,</span> <span class="s2">&quot;female&quot;</span><span class="p">),</span>
+    <span class="n">spotify</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;popular_or_not ~ track_genre&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;popular&quot;</span><span class="p">,</span>
+    <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;metal&quot;</span><span class="p">,</span> <span class="s2">&quot;deep-house&quot;</span><span class="p">),</span>
 <span class="p">)</span>
-
-<span class="c1"># Null distribution under &quot;gender doesn&#39;t matter&quot; (permutation)</span>
+<span class="n">obs</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>ObservedStatistic(stat=&#39;diff in props&#39;, value=0.034)
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Null distribution under &quot;genre doesn&#39;t matter&quot; (permutation)</span>
 <span class="n">null</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">promotions</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;decision ~ gender&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;promoted&quot;</span><span class="p">)</span>
+    <span class="n">specify</span><span class="p">(</span><span class="n">spotify</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;popular_or_not ~ track_genre&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;popular&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;independence&quot;</span><span class="p">)</span>
-    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;permute&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
-    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;male&quot;</span><span class="p">,</span> <span class="s2">&quot;female&quot;</span><span class="p">))</span>
+    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;permute&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">76</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;metal&quot;</span><span class="p">,</span> <span class="s2">&quot;deep-house&quot;</span><span class="p">))</span>
 <span class="p">)</span>
-
-<span class="n">get_p_value</span><span class="p">(</span><span class="n">null</span><span class="p">,</span> <span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>   <span class="c1"># ≈ 0.025</span>
-
-<span class="c1"># Visualize it (interactive plotly by default; engine=&quot;plotnine&quot; also works)</span>
+<span class="n">get_p_value</span><span class="p">(</span><span class="n">null</span><span class="p">,</span> <span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (1, 1)</small><table border="1" class="dataframe"><thead><tr><th>p_value</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.075</td></tr></tbody></table></div></div></div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Visualize it (interactive plotly by default; engine=&quot;plotnine&quot; also works)</span>
 <span class="n">visualize</span><span class="p">(</span><span class="n">null</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_p_value</span><span class="p">(</span><span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</div>
+<div class="cell_output docutils container">
+<img alt="_images/57bcfc0de676a5294afac27836489f257436cfdc208aba5a2907c8b2aa95d9f8.png" src="_images/57bcfc0de676a5294afac27836489f257436cfdc208aba5a2907c8b2aa95d9f8.png" />
+</div>
+</div>
 </section>
 <section id="where-to-next">
 <h2>Where to next<a class="headerlink" href="#where-to-next" title="Link to this heading">¶</a></h2>
diff --git a/doc/_build/html/py-modindex.html b/doc/_build/html/py-modindex.html
index 066b705..85e5d33 100644
--- a/doc/_build/html/py-modindex.html
+++ b/doc/_build/html/py-modindex.html
@@ -9,6 +9,7 @@
 <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
diff --git a/doc/_build/html/search.html b/doc/_build/html/search.html
index b9f30b6..bd67357 100644
--- a/doc/_build/html/search.html
+++ b/doc/_build/html/search.html
@@ -11,6 +11,7 @@
 <title>Search - moderndive 0.1.0</title><link rel="stylesheet" type="text/css" href="_static/pygments.css?v=d111a655" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=7bdb33bb" />
     <link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css" />
     <link rel="stylesheet" type="text/css" href="_static/styles/furo-extensions.css?v=8dab3a3b" />
     
     
diff --git a/doc/_build/html/searchindex.js b/doc/_build/html/searchindex.js
index e2e1ed7..2c51e1e 100644
--- a/doc/_build/html/searchindex.js
+++ b/doc/_build/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles":{"30-second example":[[10,"second-example"]],"A bootstrap distribution for a mean":[[4,"a-bootstrap-distribution-for-a-mean"]],"A confidence interval for a difference":[[4,"a-confidence-interval-for-a-difference"]],"A confidence interval for a proportion":[[4,"a-confidence-interval-for-a-proportion"]],"A first summary":[[3,"a-first-summary"]],"API reference":[[0,null]],"Available statistics":[[5,"available-statistics"]],"Bootstrapping & confidence intervals":[[4,null]],"By topic":[[2,"by-topic"]],"Coefficient table (with confidence intervals)":[[7,"coefficient-table-with-confidence-intervals"]],"Coming from R":[[1,null]],"Confidence intervals \u2014 three methods":[[4,"confidence-intervals-three-methods"]],"Correlation":[[7,"correlation"]],"Datasets":[[0,"datasets"],[2,null]],"Faceted regression-coefficient plots":[[6,"faceted-regression-coefficient-plots"]],"Fitted values & residuals":[[7,"fitted-values-residuals"]],"Function map":[[1,"function-map"]],"Get started":[[10,null]],"Getters and visualization":[[0,"getters-and-visualization"]],"Getting started":[[3,null]],"Guides":[[10,null]],"Hypothesis testing":[[5,null]],"Inference for regression coefficients":[[7,"inference-for-regression-coefficients"]],"Inference grammar":[[0,"inference-grammar"]],"Install":[[3,"install"]],"Installation":[[10,"installation"]],"Key differences":[[1,"key-differences"]],"Keyword form":[[6,"keyword-form"]],"Load a dataset":[[3,"load-a-dataset"]],"Many samples \u2192 a sampling distribution":[[8,"many-samples-a-sampling-distribution"]],"Model plots":[[6,"model-plots"]],"Model-fit summaries":[[7,"model-fit-summaries"]],"One mean / one proportion (point null)":[[5,"one-mean-one-proportion-point-null"]],"One virtual sample":[[8,"one-virtual-sample"]],"One-line tests":[[9,"one-line-tests"]],"Overlaying theory on a simulation":[[9,"overlaying-theory-on-a-simulation"]],"Plotting: plotly & plotnine":[[6,null]],"Population standard 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diff --git a/doc/_build/jupyter_execute/datasets.ipynb b/doc/_build/jupyter_execute/datasets.ipynb
new file mode 100644
index 0000000..38b9855
--- /dev/null
+++ b/doc/_build/jupyter_execute/datasets.ipynb
@@ -0,0 +1,158 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "72ce050a",
+   "metadata": {
+    "tags": [
+     "remove-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.use(\"Agg\")\n",
+    "import plotly.io as pio\n",
+    "pio.renderers.default = \"png\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f97e9152",
+   "metadata": {},
+   "source": [
+    "# Datasets\n",
+    "\n",
+    "`moderndive` bundles 58 datasets (the R `moderndive` + `infer` data, plus a few\n",
+    "derived tables). Each loads with `load_<name>()` and returns a polars DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a67c08b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (48, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>id</th><th>decision</th><th>gender</th></tr><tr><td>i64</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>1</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>2</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>3</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>4</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>5</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>44</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr><tr><td>45</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr><tr><td>46</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr><tr><td>47</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr><tr><td>48</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (48, 3)\n",
+       "┌─────┬──────────┬────────┐\n",
+       "│ id  ┆ decision ┆ gender │\n",
+       "│ --- ┆ ---      ┆ ---    │\n",
+       "│ i64 ┆ str      ┆ str    │\n",
+       "╞═════╪══════════╪════════╡\n",
+       "│ 1   ┆ promoted ┆ male   │\n",
+       "│ 2   ┆ promoted ┆ male   │\n",
+       "│ 3   ┆ promoted ┆ male   │\n",
+       "│ 4   ┆ promoted ┆ male   │\n",
+       "│ 5   ┆ promoted ┆ male   │\n",
+       "│ …   ┆ …        ┆ …      │\n",
+       "│ 44  ┆ not      ┆ female │\n",
+       "│ 45  ┆ not      ┆ female │\n",
+       "│ 46  ┆ not      ┆ female │\n",
+       "│ 47  ┆ not      ┆ female │\n",
+       "│ 48  ┆ not      ┆ female │\n",
+       "└─────┴──────────┴────────┘"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import moderndive as md\n",
+    "\n",
+    "md.available_datasets()          # sorted list of every dataset name\n",
+    "md.load_dataset(\"promotions\")    # load by name (string)\n",
+    "md.load_promotions()             # or via the generated loader"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f315ab33",
+   "metadata": {},
+   "source": [
+    "## By topic\n",
+    "\n",
+    "**Sampling & simulation**\n",
+    ": `bowl`, `bowl_sample_1`, `bowl_samples`, `tactile_prop_red`,\n",
+    "  `almonds_bowl`, `almonds_sample`, `almonds_sample_100`,\n",
+    "  `pennies`, `pennies_sample`, `pennies_resamples`, `orig_pennies_sample`\n",
+    "\n",
+    "**Hypothesis testing & CIs**\n",
+    ": `promotions`, `promotions_shuffled`, `mythbusters_yawn`, `movies_sample`,\n",
+    "  `spotify_by_genre`, `spotify_52_original`, `spotify_52_shuffled`,\n",
+    "  `spotify_metal_deephouse`, `offshore`, `age_at_marriage`, `zinc_tidy`,\n",
+    "  `cle_sac`, `gss`\n",
+    "\n",
+    "**Regression**\n",
+    ": `evals`, `saratoga_houses`, `house_prices`, `coffee_quality`, `coffee_ratings`,\n",
+    "  `credit`, `MA_schools`, `un_member_states_2024`, `mario_kart_auction`,\n",
+    "  `amazon_books`, `old_faithful_2024`\n",
+    "\n",
+    "**Flights & weather (nycflights23 / 13)**\n",
+    ": `flights`, `weather`, `airlines`, `airports`, `planes`, `envoy_flights`,\n",
+    "  `alaska_flights`, `early_january_weather`, `early_january_2023_weather`\n",
+    "\n",
+    "**Other example data**\n",
+    ": `gapminder`, `gapminder_2007`, `drinks`, `airline_safety`, `dem_score`,\n",
+    "  `us_births_1994_2003`, `steves_episodes`, `avocados`, `babies`, `ipf_lifts`,\n",
+    "  `ev_charging`, `DD_vs_SB`, `ma_traffic_2020_vs_2019`, `mass_traffic_2020`\n",
+    "\n",
+    "## Tips\n",
+    "\n",
+    "- Every loader returns a polars DataFrame; call `.to_pandas()` if a function\n",
+    "  (e.g. statsmodels) expects pandas.\n",
+    "- Datetime columns (e.g. `time_hour` in the weather/flights data) are stored in\n",
+    "  UTC.\n",
+    "- `load_dataset(\"<name>\")` raises a helpful `ValueError` listing valid names if\n",
+    "  the dataset doesn't exist."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "text_representation": {
+    "extension": ".md",
+    "format_name": "myst"
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.3"
+  },
+  "source_map": [
+   11,
+   17,
+   24,
+   30
+  ]
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/doc/_build/jupyter_execute/getting-started.ipynb b/doc/_build/jupyter_execute/getting-started.ipynb
new file mode 100644
index 0000000..309968e
--- /dev/null
+++ b/doc/_build/jupyter_execute/getting-started.ipynb
@@ -0,0 +1,361 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3268861f",
+   "metadata": {
+    "tags": [
+     "remove-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.use(\"Agg\")\n",
+    "import plotly.io as pio\n",
+    "pio.renderers.default = \"png\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09601081",
+   "metadata": {},
+   "source": [
+    "# Getting started\n",
+    "\n",
+    "This page walks through a complete analysis end to end, then points you at the\n",
+    "task guides for more depth.\n",
+    "\n",
+    "## Install\n",
+    "\n",
+    "```bash\n",
+    "pip install moderndive\n",
+    "```\n",
+    "\n",
+    "`moderndive` returns [polars](https://pola.rs) DataFrames, but every function also\n",
+    "accepts pandas DataFrames as input.\n",
+    "\n",
+    "## Load a dataset\n",
+    "\n",
+    "All datasets ship with the package and load with `load_<name>()`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "edc1cccb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>id</th><th>decision</th><th>gender</th></tr><tr><td>i64</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>1</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>2</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>3</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>4</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>5</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 3)\n",
+       "┌─────┬──────────┬────────┐\n",
+       "│ id  ┆ decision ┆ gender │\n",
+       "│ --- ┆ ---      ┆ ---    │\n",
+       "│ i64 ┆ str      ┆ str    │\n",
+       "╞═════╪══════════╪════════╡\n",
+       "│ 1   ┆ promoted ┆ male   │\n",
+       "│ 2   ┆ promoted ┆ male   │\n",
+       "│ 3   ┆ promoted ┆ male   │\n",
+       "│ 4   ┆ promoted ┆ male   │\n",
+       "│ 5   ┆ promoted ┆ male   │\n",
+       "└─────┴──────────┴────────┘"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import moderndive as md\n",
+    "\n",
+    "promotions = md.load_promotions()\n",
+    "promotions.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ef99026",
+   "metadata": {},
+   "source": [
+    "List everything that's available with `md.available_datasets()` (58 datasets), and\n",
+    "see {doc}`datasets` for a thematic tour.\n",
+    "\n",
+    "## A first summary\n",
+    "\n",
+    "`tidy_summary` gives a per-variable five-number summary (numeric) or counts\n",
+    "(categorical):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f6c6ec04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (1, 11)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>column</th><th>n</th><th>group</th><th>type</th><th>min</th><th>Q1</th><th>mean</th><th>median</th><th>Q3</th><th>max</th><th>sd</th></tr><tr><td>str</td><td>i64</td><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;weight&quot;</td><td>100</td><td>null</td><td>&quot;numeric&quot;</td><td>2.9</td><td>3.4</td><td>3.682</td><td>3.7</td><td>3.9</td><td>4.5</td><td>0.362</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (1, 11)\n",
+       "┌────────┬─────┬───────┬─────────┬───┬────────┬─────┬─────┬───────┐\n",
+       "│ column ┆ n   ┆ group ┆ type    ┆ … ┆ median ┆ Q3  ┆ max ┆ sd    │\n",
+       "│ ---    ┆ --- ┆ ---   ┆ ---     ┆   ┆ ---    ┆ --- ┆ --- ┆ ---   │\n",
+       "│ str    ┆ i64 ┆ str   ┆ str     ┆   ┆ f64    ┆ f64 ┆ f64 ┆ f64   │\n",
+       "╞════════╪═════╪═══════╪═════════╪═══╪════════╪═════╪═════╪═══════╡\n",
+       "│ weight ┆ 100 ┆ null  ┆ numeric ┆ … ┆ 3.7    ┆ 3.9 ┆ 4.5 ┆ 0.362 │\n",
+       "└────────┴─────┴───────┴─────────┴───┴────────┴─────┴─────┴───────┘"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from moderndive import tidy_summary\n",
+    "\n",
+    "tidy_summary(md.load_almonds_sample_100(), columns=[\"weight\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "379fb4ad",
+   "metadata": {},
+   "source": [
+    "## The inference pipeline\n",
+    "\n",
+    "The core grammar mirrors R `infer`. You build a pipeline and read it like a\n",
+    "sentence:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e1f08c56",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (1, 1)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>p_value</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.025</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (1, 1)\n",
+       "┌─────────┐\n",
+       "│ p_value │\n",
+       "│ ---     │\n",
+       "│ f64     │\n",
+       "╞═════════╡\n",
+       "│ 0.025   │\n",
+       "└─────────┘"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from moderndive import specify, observe, get_p_value\n",
+    "\n",
+    "# 1. The observed statistic\n",
+    "obs = observe(\n",
+    "    promotions, formula=\"decision ~ gender\", success=\"promoted\",\n",
+    "    stat=\"diff in props\", order=(\"male\", \"female\"),\n",
+    ")\n",
+    "\n",
+    "# 2. A null distribution: specify → hypothesize → generate → calculate\n",
+    "null = (\n",
+    "    specify(promotions, formula=\"decision ~ gender\", success=\"promoted\")\n",
+    "    .hypothesize(null=\"independence\")\n",
+    "    .generate(reps=1000, type=\"permute\", seed=42)\n",
+    "    .calculate(stat=\"diff in props\", order=(\"male\", \"female\"))\n",
+    ")\n",
+    "\n",
+    "# 3. Summarize\n",
+    "get_p_value(null, obs_stat=obs, direction=\"right\")   # ≈ 0.025"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56377c2e",
+   "metadata": {},
+   "source": [
+    "Each verb has a focused guide: {doc}`guides/sampling`,\n",
+    "{doc}`guides/confidence-intervals`, and {doc}`guides/hypothesis-testing`.\n",
+    "\n",
+    "## Visualizing — choose your engine\n",
+    "\n",
+    "Plots default to **plotly** (interactive). Pass `engine=\"plotnine\"` for\n",
+    "grammar-of-graphics output. The composition syntax is identical:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8d18f028",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<plotnine.ggplot.ggplot object at 0x131a109d0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {
+      "image/png": {
+       "height": 480,
+       "width": 640
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from moderndive import visualize, shade_p_value\n",
+    "\n",
+    "# Interactive plotly figure\n",
+    "visualize(null) + shade_p_value(obs_stat=obs, direction=\"right\")\n",
+    "\n",
+    "# Same plot, plotnine\n",
+    "visualize(null, engine=\"plotnine\") + shade_p_value(obs_stat=obs, direction=\"right\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9abe800d",
+   "metadata": {},
+   "source": [
+    "See {doc}`guides/plotting` for shading, confidence-interval overlays, theoretical\n",
+    "overlays, and the regression-model plots.\n",
+    "\n",
+    "## Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "addcf1c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (3, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>term</th><th>estimate</th><th>std_error</th><th>statistic</th><th>p_value</th><th>lower_ci</th><th>upper_ci</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;intercept&quot;</td><td>20986.094</td><td>6816.251</td><td>3.079</td><td>0.002</td><td>7611.128</td><td>34361.06</td></tr><tr><td>&quot;living_area&quot;</td><td>93.842</td><td>3.109</td><td>30.183</td><td>0.0</td><td>87.741</td><td>99.943</td></tr><tr><td>&quot;bedrooms&quot;</td><td>-7483.095</td><td>2783.531</td><td>-2.688</td><td>0.007</td><td>-12944.988</td><td>-2021.203</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (3, 7)\n",
+       "┌─────────────┬───────────┬───────────┬───────────┬─────────┬────────────┬───────────┐\n",
+       "│ term        ┆ estimate  ┆ std_error ┆ statistic ┆ p_value ┆ lower_ci   ┆ upper_ci  │\n",
+       "│ ---         ┆ ---       ┆ ---       ┆ ---       ┆ ---     ┆ ---        ┆ ---       │\n",
+       "│ str         ┆ f64       ┆ f64       ┆ f64       ┆ f64     ┆ f64        ┆ f64       │\n",
+       "╞═════════════╪═══════════╪═══════════╪═══════════╪═════════╪════════════╪═══════════╡\n",
+       "│ intercept   ┆ 20986.094 ┆ 6816.251  ┆ 3.079     ┆ 0.002   ┆ 7611.128   ┆ 34361.06  │\n",
+       "│ living_area ┆ 93.842    ┆ 3.109     ┆ 30.183    ┆ 0.0     ┆ 87.741     ┆ 99.943    │\n",
+       "│ bedrooms    ┆ -7483.095 ┆ 2783.531  ┆ -2.688    ┆ 0.007   ┆ -12944.988 ┆ -2021.203 │\n",
+       "└─────────────┴───────────┴───────────┴───────────┴─────────┴────────────┴───────────┘"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import statsmodels.formula.api as smf\n",
+    "from moderndive import get_regression_table\n",
+    "\n",
+    "houses = md.load_saratoga_houses()\n",
+    "model = smf.ols(\"price ~ living_area + bedrooms\", data=houses.to_pandas()).fit()\n",
+    "get_regression_table(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bf74dcd",
+   "metadata": {},
+   "source": [
+    "Full details in {doc}`guides/regression`."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "text_representation": {
+    "extension": ".md",
+    "format_name": "myst"
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.3"
+  },
+  "source_map": [
+   11,
+   17,
+   37,
+   42,
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+   57,
+   64,
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+   93,
+   101,
+   108,
+   115
+  ]
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/doc/_build/jupyter_execute/guides/confidence-intervals.ipynb b/doc/_build/jupyter_execute/guides/confidence-intervals.ipynb
new file mode 100644
index 0000000..6305a27
--- /dev/null
+++ b/doc/_build/jupyter_execute/guides/confidence-intervals.ipynb
@@ -0,0 +1,396 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "31357d1d",
+   "metadata": {
+    "tags": [
+     "remove-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.use(\"Agg\")\n",
+    "import plotly.io as pio\n",
+    "pio.renderers.default = \"png\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff28f610",
+   "metadata": {},
+   "source": [
+    "# Bootstrapping & confidence intervals\n",
+    "\n",
+    "Resample your data with replacement to build a **bootstrap distribution**, then\n",
+    "read a confidence interval off it.\n",
+    "\n",
+    "## A bootstrap distribution for a mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "acd56aac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import moderndive as md\n",
+    "from moderndive import specify, get_confidence_interval, visualize, shade_confidence_interval\n",
+    "\n",
+    "almonds = md.load_almonds_sample_100()\n",
+    "\n",
+    "boot = (\n",
+    "    specify(almonds, response=\"weight\")\n",
+    "    .generate(reps=1000, type=\"bootstrap\", seed=1)\n",
+    "    .calculate(stat=\"mean\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37281e53",
+   "metadata": {},
+   "source": [
+    "## Confidence intervals — three methods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2b337ec9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (1, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>lower_ci</th><th>upper_ci</th></tr><tr><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>3.613883</td><td>3.755272</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (1, 2)\n",
+       "┌──────────┬──────────┐\n",
+       "│ lower_ci ┆ upper_ci │\n",
+       "│ ---      ┆ ---      │\n",
+       "│ f64      ┆ f64      │\n",
+       "╞══════════╪══════════╡\n",
+       "│ 3.613883 ┆ 3.755272 │\n",
+       "└──────────┴──────────┘"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Percentile method (default)\n",
+    "get_confidence_interval(boot, level=0.95, type=\"percentile\")\n",
+    "# shape: (1, 2) → lower_ci ≈ 3.61, upper_ci ≈ 3.75\n",
+    "\n",
+    "# Standard-error method (needs the point estimate)\n",
+    "point = float(specify(almonds, response=\"weight\").calculate(stat=\"mean\"))\n",
+    "get_confidence_interval(boot, level=0.95, type=\"se\", point_estimate=point)\n",
+    "\n",
+    "# Bias-corrected\n",
+    "get_confidence_interval(boot, level=0.95, type=\"bias-corrected\", point_estimate=point)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abccc242",
+   "metadata": {},
+   "source": [
+    "Distribution objects also expose the getter as a method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2c770122",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (1, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>lower_ci</th><th>upper_ci</th></tr><tr><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>3.606975</td><td>3.75</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (1, 2)\n",
+       "┌──────────┬──────────┐\n",
+       "│ lower_ci ┆ upper_ci │\n",
+       "│ ---      ┆ ---      │\n",
+       "│ f64      ┆ f64      │\n",
+       "╞══════════╪══════════╡\n",
+       "│ 3.606975 ┆ 3.75     │\n",
+       "└──────────┴──────────┘"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boot.get_confidence_interval(level=0.95, type=\"percentile\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "565034ef",
+   "metadata": {},
+   "source": [
+    "## Visualize the interval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e4036868",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      ],
+      "text/plain": [
+       "Figure({\n",
+       "    'data': [{'marker': {'color': '#7f7f7f', 'line': {'color': 'white', 'width': 1}},\n",
+       "              'nbinsx': 20,\n",
+       "              'type': 'histogram',\n",
+       "              'x': {'bdata': ('Q4ts5/upDUBI4XoUrkcNQCqHFtnO9w' ... '6F61ENQDEzMzMzMw1ARLbz/dR4DUA='),\n",
+       "                    'dtype': 'f8'}}],\n",
+       "    'layout': {'bargap': 0.02,\n",
+       "               'shapes': [{'fillcolor': '#1f77b4',\n",
+       "                           'line': {'width': 0},\n",
+       "                           'opacity': 0.3,\n",
+       "                           'type': 'rect',\n",
+       "                           'x0': 3.606975,\n",
+       "                           'x1': 3.7499999999999996,\n",
+       "                           'xref': 'x',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y domain'},\n",
+       "                          {'line': {'color': '#1f77b4', 'width': 2},\n",
+       "                           'type': 'line',\n",
+       "                           'x0': 3.606975,\n",
+       "                           'x1': 3.606975,\n",
+       "                           'xref': 'x',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y domain'},\n",
+       "                          {'line': {'color': '#1f77b4', 'width': 2},\n",
+       "                           'type': 'line',\n",
+       "                           'x0': 3.7499999999999996,\n",
+       "                           'x1': 3.7499999999999996,\n",
+       "                           'xref': 'x',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y domain'}],\n",
+       "               'showlegend': False,\n",
+       "               'template': '...',\n",
+       "               'title': {'text': 'Simulation-Based Bootstrap Distribution'},\n",
+       "               'xaxis': {'title': {'text': 'mean'}},\n",
+       "               'yaxis': {'title': {'text': 'count'}}}\n",
+       "})"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ci = get_confidence_interval(boot, level=0.95, type=\"percentile\")\n",
+    "\n",
+    "# plotly (default) — interactive\n",
+    "visualize(boot) + shade_confidence_interval(ci)\n",
+    "\n",
+    "# plotnine\n",
+    "visualize(boot, engine=\"plotnine\") + shade_confidence_interval(ci)\n",
+    "\n",
+    "# one-call keyword form\n",
+    "visualize(boot, shade_ci=ci)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3446664d",
+   "metadata": {},
+   "source": [
+    "## A confidence interval for a proportion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fefa30e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (1, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>lower_ci</th><th>upper_ci</th></tr><tr><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>0.1595</td><td>0.4</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (1, 2)\n",
+       "┌──────────┬──────────┐\n",
+       "│ lower_ci ┆ upper_ci │\n",
+       "│ ---      ┆ ---      │\n",
+       "│ f64      ┆ f64      │\n",
+       "╞══════════╪══════════╡\n",
+       "│ 0.1595   ┆ 0.4      │\n",
+       "└──────────┴──────────┘"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import polars as pl\n",
+    "\n",
+    "mythbusters = md.load_mythbusters_yawn()\n",
+    "\n",
+    "boot_prop = (\n",
+    "    specify(mythbusters, response=\"yawn\", success=\"yes\")\n",
+    "    .generate(reps=1000, type=\"bootstrap\", seed=1)\n",
+    "    .calculate(stat=\"prop\")\n",
+    ")\n",
+    "get_confidence_interval(boot_prop, level=0.95, type=\"percentile\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5531efd",
+   "metadata": {},
+   "source": [
+    "## A confidence interval for a difference\n",
+    "\n",
+    "Add an explanatory variable and an `order=` to compare two groups:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "21ac7056",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (1, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>lower_ci</th><th>upper_ci</th></tr><tr><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>-0.224934</td><td>0.316189</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (1, 2)\n",
+       "┌───────────┬──────────┐\n",
+       "│ lower_ci  ┆ upper_ci │\n",
+       "│ ---       ┆ ---      │\n",
+       "│ f64       ┆ f64      │\n",
+       "╞═══════════╪══════════╡\n",
+       "│ -0.224934 ┆ 0.316189 │\n",
+       "└───────────┴──────────┘"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boot_diff = (\n",
+    "    specify(mythbusters, formula=\"yawn ~ group\", success=\"yes\")\n",
+    "    .generate(reps=1000, type=\"bootstrap\", seed=1)\n",
+    "    .calculate(stat=\"diff in props\", order=(\"seed\", \"control\"))\n",
+    ")\n",
+    "boot_diff.get_confidence_interval(level=0.95)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "656b6bf3",
+   "metadata": {},
+   "source": [
+    "```{seealso}\n",
+    "For *theory-based* intervals (the formula-based `t` interval) see\n",
+    "{doc}`theory-based`.\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "text_representation": {
+    "extension": ".md",
+    "format_name": "myst"
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.3"
+  },
+  "source_map": [
+   11,
+   17,
+   26,
+   37,
+   41,
+   52,
+   56,
+   58,
+   62,
+   73,
+   77,
+   88,
+   94,
+   101
+  ]
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/doc/_build/jupyter_execute/guides/hypothesis-testing.ipynb b/doc/_build/jupyter_execute/guides/hypothesis-testing.ipynb
new file mode 100644
index 0000000..8e425c0
--- /dev/null
+++ b/doc/_build/jupyter_execute/guides/hypothesis-testing.ipynb
@@ -0,0 +1,304 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a8d4de14",
+   "metadata": {
+    "tags": [
+     "remove-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.use(\"Agg\")\n",
+    "import plotly.io as pio\n",
+    "pio.renderers.default = \"png\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45bd0d3d",
+   "metadata": {},
+   "source": [
+    "# Hypothesis testing\n",
+    "\n",
+    "Hypothesis tests follow the same grammar as confidence intervals, with an added\n",
+    "`hypothesize()` step that defines the null world, and `generate()` that simulates\n",
+    "from it.\n",
+    "\n",
+    "## Two groups: a permutation test\n",
+    "\n",
+    "The classic \"promotions\" experiment — does the (perceived) gender on a résumé\n",
+    "change the promotion rate?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2e44c72b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (1, 1)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>p_value</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.025</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (1, 1)\n",
+       "┌─────────┐\n",
+       "│ p_value │\n",
+       "│ ---     │\n",
+       "│ f64     │\n",
+       "╞═════════╡\n",
+       "│ 0.025   │\n",
+       "└─────────┘"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import moderndive as md\n",
+    "from moderndive import specify, observe, get_p_value, visualize, shade_p_value\n",
+    "\n",
+    "promotions = md.load_promotions()\n",
+    "\n",
+    "# Observed difference in promotion proportions, male − female\n",
+    "obs = observe(\n",
+    "    promotions, formula=\"decision ~ gender\", success=\"promoted\",\n",
+    "    stat=\"diff in props\", order=(\"male\", \"female\"),\n",
+    ")   # ≈ 0.292\n",
+    "\n",
+    "# Null: gender is independent of the decision → permute the labels\n",
+    "null = (\n",
+    "    specify(promotions, formula=\"decision ~ gender\", success=\"promoted\")\n",
+    "    .hypothesize(null=\"independence\")\n",
+    "    .generate(reps=1000, type=\"permute\", seed=42)\n",
+    "    .calculate(stat=\"diff in props\", order=(\"male\", \"female\"))\n",
+    ")\n",
+    "\n",
+    "get_p_value(null, obs_stat=obs, direction=\"right\")   # ≈ 0.025"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6725639d",
+   "metadata": {},
+   "source": [
+    "## Shade the p-value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f8df35f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      ],
+      "text/plain": [
+       "Figure({\n",
+       "    'data': [{'marker': {'color': '#7f7f7f', 'line': {'color': 'white', 'width': 1}},\n",
+       "              'nbinsx': 20,\n",
+       "              'type': 'histogram',\n",
+       "              'x': {'bdata': ('qqqqqqqq0r9QVVVVVVWlP1BVVVVVVa' ... 'AAAADAv1BVVVVVVaU/AAAAAAAAwL8='),\n",
+       "                    'dtype': 'f8'}}],\n",
+       "    'layout': {'bargap': 0.02,\n",
+       "               'shapes': [{'line': {'color': '#d62728', 'dash': 'solid', 'width': 2},\n",
+       "                           'type': 'line',\n",
+       "                           'x0': 0.29166666666666663,\n",
+       "                           'x1': 0.29166666666666663,\n",
+       "                           'xref': 'x',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y domain'},\n",
+       "                          {'fillcolor': '#d62728',\n",
+       "                           'line': {'width': 0},\n",
+       "                           'opacity': 0.3,\n",
+       "                           'type': 'rect',\n",
+       "                           'x0': 0.29166666666666663,\n",
+       "                           'x1': 0.4166666666666667,\n",
+       "                           'xref': 'x',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y domain'}],\n",
+       "               'showlegend': False,\n",
+       "               'template': '...',\n",
+       "               'title': {'text': 'Simulation-Based Null Distribution'},\n",
+       "               'xaxis': {'title': {'text': 'diff in props'}},\n",
+       "               'yaxis': {'title': {'text': 'count'}}}\n",
+       "})"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "visualize(null) + shade_p_value(obs_stat=obs, direction=\"right\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "322039dd",
+   "metadata": {},
+   "source": [
+    "`direction` is one of `\"right\"`/`\"greater\"`, `\"left\"`/`\"less\"`, or `\"two-sided\"`.\n",
+    "The two-sided shading mirrors the observed statistic about 0.\n",
+    "\n",
+    "## One mean / one proportion (point null)\n",
+    "\n",
+    "Use a `\"point\"` null with bootstrap resampling, supplying the hypothesized value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d2b210f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (1, 1)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>p_value</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.0</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (1, 1)\n",
+       "┌─────────┐\n",
+       "│ p_value │\n",
+       "│ ---     │\n",
+       "│ f64     │\n",
+       "╞═════════╡\n",
+       "│ 0.0     │\n",
+       "└─────────┘"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age = md.load_age_at_marriage()\n",
+    "\n",
+    "obs_t = observe(age, response=\"age\", stat=\"t\", null=\"point\", mu=23)\n",
+    "\n",
+    "null_t = (\n",
+    "    specify(age, response=\"age\")\n",
+    "    .hypothesize(null=\"point\", mu=23)\n",
+    "    .generate(reps=1000, type=\"bootstrap\", seed=1)\n",
+    "    .calculate(stat=\"t\")\n",
+    ")\n",
+    "get_p_value(null_t, obs_stat=obs_t, direction=\"two-sided\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b49eef5",
+   "metadata": {},
+   "source": [
+    "For a one-proportion test you can also *simulate* draws directly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4fc3dbfd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import polars as pl\n",
+    "\n",
+    "coins = pl.DataFrame({\"flip\": [\"heads\"] * 30 + [\"tails\"] * 70})\n",
+    "\n",
+    "null_p = (\n",
+    "    specify(coins, response=\"flip\", success=\"heads\")\n",
+    "    .hypothesize(null=\"point\", p=0.5)\n",
+    "    .generate(reps=1000, type=\"draw\", seed=1)   # \"simulate\" is an alias\n",
+    "    .calculate(stat=\"prop\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a25388b",
+   "metadata": {},
+   "source": [
+    "## Available statistics\n",
+    "\n",
+    "`calculate(stat=...)` supports the full infer vocabulary: `\"mean\"`, `\"median\"`,\n",
+    "`\"sum\"`, `\"sd\"`, `\"prop\"`, `\"count\"`, `\"diff in means\"`, `\"diff in medians\"`,\n",
+    "`\"diff in props\"`, `\"ratio of means\"`, `\"ratio of props\"`, `\"odds ratio\"`,\n",
+    "`\"slope\"`, `\"correlation\"`, `\"t\"`, `\"z\"`, `\"F\"`, `\"Chisq\"`, plus any custom\n",
+    "callable `stat(response, explanatory) -> float`.\n",
+    "\n",
+    "```{seealso}\n",
+    "Prefer a one-line classical test? See the tidy wrappers in {doc}`theory-based`\n",
+    "(`t_test`, `prop_test`, `chisq_test`).\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "text_representation": {
+    "extension": ".md",
+    "format_name": "myst"
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.3"
+  },
+  "source_map": [
+   11,
+   17,
+   30,
+   51,
+   55,
+   57,
+   66,
+   78,
+   82,
+   93
+  ]
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/doc/_build/jupyter_execute/guides/plotting.ipynb b/doc/_build/jupyter_execute/guides/plotting.ipynb
new file mode 100644
index 0000000..7dfd2d9
--- /dev/null
+++ b/doc/_build/jupyter_execute/guides/plotting.ipynb
@@ -0,0 +1,752 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4a57ea3b",
+   "metadata": {
+    "tags": [
+     "remove-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.use(\"Agg\")\n",
+    "import plotly.io as pio\n",
+    "pio.renderers.default = \"png\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3feb4bb4",
+   "metadata": {},
+   "source": [
+    "# Plotting: plotly & plotnine\n",
+    "\n",
+    "Every plotting function in `moderndive` takes an `engine=` argument:\n",
+    "\n",
+    "- `engine=\"plotly\"` (**default**) — interactive figures (`plotly.graph_objects.Figure`).\n",
+    "- `engine=\"plotnine\"` — grammar-of-graphics figures (`plotnine.ggplot`).\n",
+    "\n",
+    "The composition syntax is identical across engines, so you can switch a whole\n",
+    "analysis by changing one argument. (In this documentation, plotly figures are\n",
+    "shown as static images; in a notebook they're fully interactive.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "828ca414",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (1, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>lower_ci</th><th>upper_ci</th></tr><tr><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>3.606975</td><td>3.75</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (1, 2)\n",
+       "┌──────────┬──────────┐\n",
+       "│ lower_ci ┆ upper_ci │\n",
+       "│ ---      ┆ ---      │\n",
+       "│ f64      ┆ f64      │\n",
+       "╞══════════╪══════════╡\n",
+       "│ 3.606975 ┆ 3.75     │\n",
+       "└──────────┴──────────┘"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import moderndive as md\n",
+    "from moderndive import (\n",
+    "    specify, observe, get_confidence_interval,\n",
+    "    visualize, shade_confidence_interval, shade_p_value,\n",
+    ")\n",
+    "\n",
+    "boot = (\n",
+    "    specify(md.load_almonds_sample_100(), response=\"weight\")\n",
+    "    .generate(reps=1000, type=\"bootstrap\", seed=1)\n",
+    "    .calculate(stat=\"mean\")\n",
+    ")\n",
+    "ci = get_confidence_interval(boot, level=0.95, type=\"percentile\")\n",
+    "ci"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a846f4e4",
+   "metadata": {},
+   "source": [
+    "## visualize() returns an InferPlot\n",
+    "\n",
+    "`visualize()` returns a small `InferPlot` wrapper that you compose with shading via\n",
+    "`+` in **both** engines. The underlying figure is on `.figure` (and, for plotnine,\n",
+    "the raw `ggplot` on `.gg`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8a6a4af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      ],
+      "text/plain": [
+       "Figure({\n",
+       "    'data': [{'marker': {'color': '#7f7f7f', 'line': {'color': 'white', 'width': 1}},\n",
+       "              'nbinsx': 20,\n",
+       "              'type': 'histogram',\n",
+       "              'x': {'bdata': ('Q4ts5/upDUBI4XoUrkcNQCqHFtnO9w' ... '6F61ENQDEzMzMzMw1ARLbz/dR4DUA='),\n",
+       "                    'dtype': 'f8'}}],\n",
+       "    'layout': {'bargap': 0.02,\n",
+       "               'shapes': [{'fillcolor': '#1f77b4',\n",
+       "                           'line': {'width': 0},\n",
+       "                           'opacity': 0.3,\n",
+       "                           'type': 'rect',\n",
+       "                           'x0': 3.606975,\n",
+       "                           'x1': 3.7499999999999996,\n",
+       "                           'xref': 'x',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y domain'},\n",
+       "                          {'line': {'color': '#1f77b4', 'width': 2},\n",
+       "                           'type': 'line',\n",
+       "                           'x0': 3.606975,\n",
+       "                           'x1': 3.606975,\n",
+       "                           'xref': 'x',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y domain'},\n",
+       "                          {'line': {'color': '#1f77b4', 'width': 2},\n",
+       "                           'type': 'line',\n",
+       "                           'x0': 3.7499999999999996,\n",
+       "                           'x1': 3.7499999999999996,\n",
+       "                           'xref': 'x',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y domain'}],\n",
+       "               'showlegend': False,\n",
+       "               'template': '...',\n",
+       "               'title': {'text': 'Simulation-Based Bootstrap Distribution'},\n",
+       "               'xaxis': {'title': {'text': 'mean'}},\n",
+       "               'yaxis': {'title': {'text': 'count'}}}\n",
+       "})"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "visualize(boot) + shade_confidence_interval(ci)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e70f8f0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<plotnine.ggplot.ggplot object at 0x13145f950>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {
+      "image/png": {
+       "height": 480,
+       "width": 640
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# the same plot via the plotnine engine\n",
+    "visualize(boot, engine=\"plotnine\") + shade_confidence_interval(ci)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35d68302",
+   "metadata": {},
+   "source": [
+    "### Keyword form\n",
+    "\n",
+    "If you'd rather not use `+`, pass the shading inline (handy for plotly):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "eaf6ea20",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Bootstrap Distribution\"},\"xaxis\":{\"title\":{\"text\":\"mean\"}},\"yaxis\":{\"title\":{\"text\":\"count\"}},\"shapes\":[{\"fillcolor\":\"#1f77b4\",\"line\":{\"width\":0},\"opacity\":0.3,\"type\":\"rect\",\"x0\":3.606975,\"x1\":3.7499999999999996,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"},{\"line\":{\"color\":\"#1f77b4\",\"width\":2},\"type\":\"line\",\"x0\":3.606975,\"x1\":3.606975,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"},{\"line\":{\"color\":\"#1f77b4\",\"width\":2},\"type\":\"line\",\"x0\":3.7499999999999996,\"x1\":3.7499999999999996,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"}]},                        {\"responsive\": true}                    )                };            </script>        </div>"
+      ],
+      "text/plain": [
+       "Figure({\n",
+       "    'data': [{'marker': {'color': '#7f7f7f', 'line': {'color': 'white', 'width': 1}},\n",
+       "              'nbinsx': 20,\n",
+       "              'type': 'histogram',\n",
+       "              'x': {'bdata': ('Q4ts5/upDUBI4XoUrkcNQCqHFtnO9w' ... '6F61ENQDEzMzMzMw1ARLbz/dR4DUA='),\n",
+       "                    'dtype': 'f8'}}],\n",
+       "    'layout': {'bargap': 0.02,\n",
+       "               'shapes': [{'fillcolor': '#1f77b4',\n",
+       "                           'line': {'width': 0},\n",
+       "                           'opacity': 0.3,\n",
+       "                           'type': 'rect',\n",
+       "                           'x0': 3.606975,\n",
+       "                           'x1': 3.7499999999999996,\n",
+       "                           'xref': 'x',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y domain'},\n",
+       "                          {'line': {'color': '#1f77b4', 'width': 2},\n",
+       "                           'type': 'line',\n",
+       "                           'x0': 3.606975,\n",
+       "                           'x1': 3.606975,\n",
+       "                           'xref': 'x',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y domain'},\n",
+       "                          {'line': {'color': '#1f77b4', 'width': 2},\n",
+       "                           'type': 'line',\n",
+       "                           'x0': 3.7499999999999996,\n",
+       "                           'x1': 3.7499999999999996,\n",
+       "                           'xref': 'x',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y domain'}],\n",
+       "               'showlegend': False,\n",
+       "               'template': '...',\n",
+       "               'title': {'text': 'Simulation-Based Bootstrap Distribution'},\n",
+       "               'xaxis': {'title': {'text': 'mean'}},\n",
+       "               'yaxis': {'title': {'text': 'count'}}}\n",
+       "})"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "visualize(boot, shade_ci=ci)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "351fb1c4",
+   "metadata": {},
+   "source": [
+    "## Shading p-values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d94931ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      ],
+      "text/plain": [
+       "Figure({\n",
+       "    'data': [{'marker': {'color': '#7f7f7f', 'line': {'color': 'white', 'width': 1}},\n",
+       "              'nbinsx': 20,\n",
+       "              'type': 'histogram',\n",
+       "              'x': {'bdata': ('QLTIdr6fmj+AFK5H4XqEvwCq8dJNYm' ... 'YxCKyMPwCq8dJNYmA/AH9qvHSTeD8='),\n",
+       "                    'dtype': 'f8'}}],\n",
+       "    'layout': {'bargap': 0.02,\n",
+       "               'shapes': [{'line': {'color': '#d62728', 'dash': 'solid', 'width': 2},\n",
+       "                           'type': 'line',\n",
+       "                           'x0': 0.03399999999999992,\n",
+       "                           'x1': 0.03399999999999992,\n",
+       "                           'xref': 'x',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y domain'},\n",
+       "                          {'fillcolor': '#d62728',\n",
+       "                           'line': {'width': 0},\n",
+       "                           'opacity': 0.3,\n",
+       "                           'type': 'rect',\n",
+       "                           'x0': 0.03399999999999992,\n",
+       "                           'x1': 0.07509999999999994,\n",
+       "                           'xref': 'x',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y domain'}],\n",
+       "               'showlegend': False,\n",
+       "               'template': '...',\n",
+       "               'title': {'text': 'Simulation-Based Null Distribution'},\n",
+       "               'xaxis': {'title': {'text': 'diff in props'}},\n",
+       "               'yaxis': {'title': {'text': 'count'}}}\n",
+       "})"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spotify = md.load_spotify_metal_deephouse()\n",
+    "obs = observe(spotify, formula=\"popular_or_not ~ track_genre\", success=\"popular\",\n",
+    "              stat=\"diff in props\", order=(\"metal\", \"deep-house\"))\n",
+    "null = (\n",
+    "    specify(spotify, formula=\"popular_or_not ~ track_genre\", success=\"popular\")\n",
+    "    .hypothesize(null=\"independence\")\n",
+    "    .generate(reps=1000, type=\"permute\", seed=76)\n",
+    "    .calculate(stat=\"diff in props\", order=(\"metal\", \"deep-house\"))\n",
+    ")\n",
+    "visualize(null) + shade_p_value(obs_stat=obs, direction=\"right\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "950146a4",
+   "metadata": {},
+   "source": [
+    "## Simulation vs. theory overlays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a1ea5456",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
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"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"white\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"#C8D4E3\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"white\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\"},\"bgcolor\":\"white\",\"radialaxis\":{\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"},\"yaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"},\"zaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"},\"bgcolor\":\"white\",\"caxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#EBF0F8\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#EBF0F8\",\"zerolinewidth\":2}}},\"title\":{\"text\":\"Theoretical Distribution\"},\"xaxis\":{\"title\":{\"text\":\"mean\"}},\"yaxis\":{\"title\":{\"text\":\"density\"}},\"bargap\":0.02,\"showlegend\":false},                        {\"responsive\": true}                    )                };            </script>        </div>"
+      ],
+      "text/plain": [
+       "Figure({\n",
+       "    'data': [{'line': {'color': '#d62728'},\n",
+       "              'mode': 'lines',\n",
+       "              'type': 'scatter',\n",
+       "              'x': {'bdata': ('2M73U+OlDECESJWMEacMQDHCMsU/qA' ... '0gkXoOQAb5yli/ew5AsnJoke18DkA='),\n",
+       "                    'dtype': 'f8'},\n",
+       "              'y': {'bdata': ('VP8zg1F+zz+rvkcTbHXQP7XFcBzKMt' ... 'lHLG2QPyPj9qGQ/o4/X5aAjMQ7jT8='),\n",
+       "                    'dtype': 'f8'}}],\n",
+       "    'layout': {'bargap': 0.02,\n",
+       "               'showlegend': False,\n",
+       "               'template': '...',\n",
+       "               'title': {'text': 'Theoretical Distribution'},\n",
+       "               'xaxis': {'title': {'text': 'mean'}},\n",
+       "               'yaxis': {'title': {'text': 'density'}}}\n",
+       "})"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "visualize(boot, method=\"theoretical\")   # normal-approximation curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cd09e3c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Bootstrap Distribution\"},\"xaxis\":{\"title\":{\"text\":\"mean\"}},\"yaxis\":{\"title\":{\"text\":\"density\"}},\"bargap\":0.02,\"showlegend\":false},                        {\"responsive\": true}                    )                };            </script>        </div>"
+      ],
+      "text/plain": [
+       "Figure({\n",
+       "    'data': [{'histnorm': 'probability density',\n",
+       "              'marker': {'color': '#7f7f7f', 'line': {'color': 'white', 'width': 1}},\n",
+       "              'nbinsx': 20,\n",
+       "              'type': 'histogram',\n",
+       "              'x': {'bdata': ('Q4ts5/upDUBI4XoUrkcNQCqHFtnO9w' ... '6F61ENQDEzMzMzMw1ARLbz/dR4DUA='),\n",
+       "                    'dtype': 'f8'}},\n",
+       "             {'line': {'color': '#d62728'},\n",
+       "              'mode': 'lines',\n",
+       "              'type': 'scatter',\n",
+       "              'x': {'bdata': ('2M73U+OlDECESJWMEacMQDHCMsU/qA' ... '0gkXoOQAb5yli/ew5AsnJoke18DkA='),\n",
+       "                    'dtype': 'f8'},\n",
+       "              'y': {'bdata': ('VP8zg1F+zz+rvkcTbHXQP7XFcBzKMt' ... 'lHLG2QPyPj9qGQ/o4/X5aAjMQ7jT8='),\n",
+       "                    'dtype': 'f8'}}],\n",
+       "    'layout': {'bargap': 0.02,\n",
+       "               'showlegend': False,\n",
+       "               'template': '...',\n",
+       "               'title': {'text': 'Simulation-Based Bootstrap Distribution'},\n",
+       "               'xaxis': {'title': {'text': 'mean'}},\n",
+       "               'yaxis': {'title': {'text': 'density'}}}\n",
+       "})"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "visualize(boot, method=\"both\")          # histogram + curve overlaid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1720c08f",
+   "metadata": {},
+   "source": [
+    "## Faceted regression-coefficient plots\n",
+    "\n",
+    "`visualize_fit` shows one panel per term, and shading is **per-facet**:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8750acfc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TDtusXdrrAoBOjYB8BwcDX1a2hbEHDwDj9FLShVcHAqbTmgVU4zcCbPNe2dLShwN6QPe5gy7rAD14saEdzw8Csn7hVe5GlwE8Aou3TTMHAqjaHSEzGs8AdH5u7ZxS8wKnw2XdH9rvAddzez81TtMCClr+wyYHAwBYHKHig3MPAvkcDE\\u002fDcwsDiZxRU\\u002fzW9wDoroaYeE7fAG\\u002flwoe8Vo8ChSeYn6vPKwJ8TszbZvMDAupmlCw+1sMAevQHXjsO7wI0bi8QoTMLAEoZPCEvqtMBq9yiLpw6YQGC8Pt6iD7TAzttoueYcvcD4Ij6HPti+wFMcm69r28fAWuzOx26ms8DAOSRz8Z6nwKZQ9RUbVrjAmCRkrSvTwMCk16s6KBPCwLx9r8VWdr3ANrhzTPuRtcA+mGRuVmu7wPJDW204XITA4psiyHQIucCb+0qkHRPAwAy1IxBKdrvA51I6JE1kw8DGejnOq13HwF+fXR6hpq3A+Az2Ga5WwsAsKsQ6QXXHwHzZ5W5QjrPA+zBRwMyIwsBt8eWW6g+lwILamDwj57LAL2vI3PDVyMDkEdQ6VRSxwE\\u002fSjqWQvcXANWVJ20BbuMDeNhwW8wHAwMooqDnuk8TAGE931o+8pcDB0MLHGum3wHk10PJ6ibTAUhXyFmP9wsD7r+Fggdi0wAfJakXF0MfA0RBwMxSVw8Aryj0FdxPIwFIfTU6zHsrAeMosMmVYuMASW2S6adipwN7UoZJntL\\u002fAlBfJtnumwcA8BmJPUbrMwPjD1M1BS8bAvywNR+crvcC7krb6ZrG9wAc5qD7l7MbAoVWA5cdlwcC+hC541au4wK7XwMW9kq\\u002fAFMzZZIixysBIK8J1TKm1wP3378fMc7nApywKli7JwMDPg+kCH2m2wHjDXk6QcKfAy42di2Kbq8DIjxxaYWbGwF7I+OBscrzACH6aBr\\u002fducDGRJFjLjXGwF6XsnHabpXAG5L1XMr8ssBwJSE6C3e8wDSX3YwqmsbAQc4bUBs4vsBO0udWEwuxwC5uLMvd\\u002fMHAK+mO6dyIt8A6rf5KFh7DwBYPnmnWA8HAHipKAfIguMDtAXa\\u002fj8++wDOJiNJ2w8nASZkKe54KsMC6OhLzXcm\\u002fwJKFTWc0psLAN876HuYexsAiidiQ7y7CwIEeb0u8IsTAi\\u002fWqJ1xRtMBLy3WiBtvJwN4AQLlyGsPAwL1fkdVwvsAeYOJWHz+zwN+oparJJ7jAX9lzoW\\u002f7usBuM1sDR7C\\u002fwAKUwBFAw6nAWQtNNjg7uMDjPSoI3Qa4wHAjBuiC1bbAyNRhNnM0w8D2DfnA3EykwAbVjAaF3rvAOAXMrSlwucD9eDAm82nDwFMOiY9737jAOI32yHFZusD+zHxsUdS\\u002fwLx5AV94N8bAxdKcD+cOuMCUqas8PbvFwBMMDTYf5avAuQmb+OQZxMAA1A+FM8S8wEA8uLkAVMLAkuj0L1dNvcAsVf5W5H3JwIMDIK0Tr77Awo12llZ7wcDcTLKRHETGwBKNiRaNuLTAXPta7n62ysAXkX6MX2yWQLi5NWV4FL7Au6FhACCsssCeOyBw2ffBwCDek7FPJL3AgOL4OCX0tcAOBrOaHE\\u002fDwBv9OLUdg8bAYeb7mWn0w8ACF0FEVs7EwNodizbiwMfAz\\u002fOh3m2fwsCYypvU18a9wL4EU0xOe7PAnUoZBNkRu8Cu2h0NyGi4wDTnoZXQrcPAYOr6N8E4tMBp+K4mVPDFwPhYosLy+J3A3bkiME+qwMAwN9dgC0OqwDr74p8P08LAmj6UScMbwMB+AqYK5RfBwF6tv3V+BcrAJ15oi\\u002fnyxMD4uxZFZue4wGoqQBYjY8DA+a26no3VwsDvJDHGf4\\u002fGwJj5hg\\u002frMcnACNp354XsnsBu5DTmz8u8wCa3y6DEt8LABWkwBIb9w8BWhEl7mD6SwO0EnwkiybLAkJW\\u002ftSSBwcD6jlr8coy\\u002fwBaOU7ZyrLjAEZt5E5OEtsDrkPPAVl\\u002fAwCEu75ity8bAxPfBhcJGuMA\\u002f9wI2+BSwwFG5Cc1RUa3AtGjtH306wMBu+u+rSD3CwAKjE+J7arrAA8TY8hKkusCKlK1EC3S+wPDzMVRo973AVVzxgJJhxMAswgnd9Zy5wMqd29c+fsXALzgVlXiCo8C6KbcBPTLHwGxnrxia5cLAjK\\u002fBH8QtwcBF3mI9wxzKwE3JDSslAMDAmjkqAvJPxcAt\\u002fX1Jqd3LwIYNyI1DS73ATB3+l0TosMD0VZB8CS\\u002fHwKsp3Hm42K7Ax+80R5vMwsDakpxUln\\u002fCwE4lEJZ9O8LAqAUlECXGfsADAosZScfBwMNqGmDjdMzAygxRm0aCucCigCA0WsutwBTNyF4hvMrAsFTPBodrucBpb90Ow3G1wEIMwDoEoMTAeUPHF+PYz8CPO3Lie8e5wI8L8Np7E7zAbh5BjmROoMAnWDL7W1WzwP7ygY0dz8PAlKkFXlBIuMD2z2eICie\\u002fwCwsXjiU1r7ABoifjpbHtMBeYXky5Fe+wAqwbF9nQ7PAfmkSe0Gut8BKGmXAmACzwAn5vUKWAMjAC7B9T8OjlsBZMwmCapS0wMO0xfezw77ANDFF7mhGz8AHLwv8AzHBwG9V0wJsybjAOTPFA7Hux8AINe4wtDO4wOhJ7+jtwGnAUtOK7+pvsMCkWjaavw7EwKYR1RJDtcHAHMyV9ShewMAzQ6C4s1fFwMFVFQwav7bANMQxVJsIucB57Nv6cFvCwM61jhox0YPALba\\u002faGUxw8CxbwMsb5jDwKVqD0xyhsLA3EtG8XfntsBl464Ir5S4wBOC+QgzycHA6MGmoIF4isDCDMnOyQq3wHCzXnIv9sDADNB7rVuFusDae5CMikG\\u002fwENTW+guAsHA2p533m6VmMAoonUN8nbDwF\\u002fdungTLMTACY\\u002fgzL2R0cBldla3aEbBwFgaVk8gD37ASx\\u002fy\\u002feluxMAUoVC4mIaywCepU2+pEMXAh2NfJkamtsAFRBEb75q5wPBTn8Chy7bABZ2p3yrwssDDIPk4M8y4wIsw5Wr1BsbAHmJlLhfMmcA2FpSsNyi5wCPaRb0fY8LAfnLWvh7IpsBKFkzVRku8wLhsIvUJ28jAZcoXfbvswMBWKumlWfe\\u002fwDBvvFoOQMzA1BQftskSxcCK6nbMU0i1wAckb0fyYMHAwHR9H6jpwsDK4cVLiNnBwOxKtWpyWMHAzhX0GtUBy8BC5L7PfFDDwOD9A\\u002fA6drfA8GL+2w06ucDKQGC1ra2ywIDYZFk8WcHAgqmJTvnat8AYlq5MqOCswHZHQ+kdNLbAgcBxBUk0tcCMhTJJJuS+wFvNdi3Fcb\\u002fASIFBQB2KxMDPPKxWqMuywPpPAYtObr3AosmW5gJPqMDPTa9BaG69wJA0daF0MLnAPNZKoVUdtsCoJFBgzXazwByE\\u002f7PiKL7AjABsxOCrycBpwqoGX4+3wBytuPa9G7nAVwngzLNtu8AxAdy8AkPEwDHQ4+Q\\u002fesPAgWHFlcRdqcBZZpMoGYShwCLde+deD7PAbjIHC0Y+vcCqnqyYyTK8wK7r60xFDMDAS207pLpRwcDyh2QxlXW4wKJdJ6zagMHAxvfSbKHksMDXp0S7vGulwBWmEAjURcjApoUw12mHvsCVm0d969+zwFZbBCcOSq\\u002fAHwExm0NywsBRg6ZTAXyuwB4EedHDTcTANNx6xk6OpMAv0t38bWDIwL+GxK4Vo77As+Szeyp2vsDqFQaaUPXFwJ8sWbwB6arARrsdmgtrsMBylsx\\u002fJZjBwGCIQsJ09K7AuCLZwrMPxsBxkPkwZC26wK2S74TRsMbA1yZFeJGNvcAWWi3lqHi7wE+8SEW458nAbi\\u002fdSZoIw8A5AgRzx8S5wJLgdnC9VsPA\\u002fuZMuxA0ssDQUpY5O\\u002fGkwPCjO2L03L\\u002fAG9I\\u002f5kReqsCv9oyJnp\\u002fEwNdw4RspYMHAIPsiU680wsCtwtDBp9iiwKNEyXew+cjAvf1pInt+wcCE7xFQAcO5wMh7s3dUsbDA\\u002fEEPj4xvrsDaBpcEftC8wERvpnVCtcLAFH+Tu4HEuMBUwwIYgKGUwECBUIxXgsLAyFQkFqkQtMB3Xl0qek7AwB8WMWgpY8DAsfbBNSCspsCM1JTKrQTBwIzIGHvyIrjAVPFM5\\u002foimsA2zlAxlgK7wJINhyihbLnAMdfkWmD4tsDaxha8GDa9wO6gQRHDobDAN3JCf2cSqsBR\\u002fjPieh65wA5j5O+6eMLA8NedKe9Fq8AcHa8iywejwI4\\u002fqB6SirDAx8yIDA7twsAIXd+Q0cy\\u002fwFTGJDRkj7bAHBXH7iz4wMDetdiT5Ri0wDZKNak6mb\\u002fAp0f+SavTusCnJXgYbTuywG7WgwnSO7fAVIWBBGmRuMAwCKRtoFzDwAYomp\\u002fPG7bAgIcVGuCavcARbn3rInq6wJy11J8q6bTAYlb1ajCYtMAZMR7rkALLwJmtKDhUHrjABK\\u002fe4plKtsBmW2gm50+8wIB\\u002fz\\u002fnO+qrAAF6mCR3vxsAbwAMugEGzwKZZkw4XscLA6XjU+So5wsBi7v+tCMCcwFexwWn9P7\\u002fA1wLPEZB2wcB1YegbDxumwAw1MvQIZLrAXHfO5XuotcAoQvJxKsLAwOgdNP9YV7TADJYHiD9cu8BNbrCVxwvGwGm3nfGLVsbAQ3ChnTtyxMCY+Lx7BFKzwNCk6a\\u002f3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+       "Figure({\n",
+       "    'data': [{'marker': {'color': '#7f7f7f', 'line': {'color': 'white', 'width': 1}},\n",
+       "              'nbinsx': 20,\n",
+       "              'type': 'histogram',\n",
+       "              'x': {'bdata': ('VvXKvrSg10Ag0JHqEtPaQKGvHizX8b' ... 'nHqjHEQLDc2GAZAthApyMOIR+Ry0A='),\n",
+       "                    'dtype': 'f8'},\n",
+       "              'xaxis': 'x',\n",
+       "              'yaxis': 'y'},\n",
+       "             {'marker': {'color': '#7f7f7f', 'line': {'color': 'white', 'width': 1}},\n",
+       "              'nbinsx': 20,\n",
+       "              'type': 'histogram',\n",
+       "              'x': {'bdata': ('zJ99T5rQV0DINYORzONXQBbJtdMrlV' ... '8OgONXQJn1wZt56VdAf1Tir1cgV0A='),\n",
+       "                    'dtype': 'f8'},\n",
+       "              'xaxis': 'x2',\n",
+       "              'yaxis': 'y2'},\n",
+       "             {'marker': {'color': '#7f7f7f', 'line': {'color': 'white', 'width': 1}},\n",
+       "              'nbinsx': 20,\n",
+       "              'type': 'histogram',\n",
+       "              'x': {'bdata': ('mBuhcxAgwsD0dP8UfQfBwGzN3uiFfr' ... 'rCMj21wJeJqTx/fMPATDdm2oG4rsA='),\n",
+       "                    'dtype': 'f8'},\n",
+       "              'xaxis': 'x3',\n",
+       "              'yaxis': 'y3'}],\n",
+       "    'layout': {'annotations': [{'font': {'size': 16},\n",
+       "                                'showarrow': False,\n",
+       "                                'text': 'intercept',\n",
+       "                                'x': 0.14444444444444446,\n",
+       "                                'xanchor': 'center',\n",
+       "                                'xref': 'paper',\n",
+       "                                'y': 1.0,\n",
+       "                                'yanchor': 'bottom',\n",
+       "                                'yref': 'paper'},\n",
+       "                               {'font': {'size': 16},\n",
+       "                                'showarrow': False,\n",
+       "                                'text': 'living_area',\n",
+       "                                'x': 0.5,\n",
+       "                                'xanchor': 'center',\n",
+       "                                'xref': 'paper',\n",
+       "                                'y': 1.0,\n",
+       "                                'yanchor': 'bottom',\n",
+       "                                'yref': 'paper'},\n",
+       "                               {'font': {'size': 16},\n",
+       "                                'showarrow': False,\n",
+       "                                'text': 'bedrooms',\n",
+       "                                'x': 0.8555555555555556,\n",
+       "                                'xanchor': 'center',\n",
+       "                                'xref': 'paper',\n",
+       "                                'y': 1.0,\n",
+       "                                'yanchor': 'bottom',\n",
+       "                                'yref': 'paper'}],\n",
+       "               'bargap': 0.02,\n",
+       "               'shapes': [{'fillcolor': '#1f77b4',\n",
+       "                           'line': {'width': 0},\n",
+       "                           'opacity': 0.3,\n",
+       "                           'type': 'rect',\n",
+       "                           'x0': -13263.439614383647,\n",
+       "                           'x1': -1320.3750915939063,\n",
+       "                           'xref': 'x3',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y3 domain'},\n",
+       "                          {'line': {'color': '#1f77b4', 'width': 2},\n",
+       "                           'type': 'line',\n",
+       "                           'x0': -13263.439614383647,\n",
+       "                           'x1': -13263.439614383647,\n",
+       "                           'xref': 'x3',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y3 domain'},\n",
+       "                          {'line': {'color': '#1f77b4', 'width': 2},\n",
+       "                           'type': 'line',\n",
+       "                           'x0': -1320.3750915939063,\n",
+       "                           'x1': -1320.3750915939063,\n",
+       "                           'xref': 'x3',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y3 domain'},\n",
+       "                          {'fillcolor': '#1f77b4',\n",
+       "                           'line': {'width': 0},\n",
+       "                           'opacity': 0.3,\n",
+       "                           'type': 'rect',\n",
+       "                           'x0': 6242.7628615098165,\n",
+       "                           'x1': 36149.32553478348,\n",
+       "                           'xref': 'x',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y domain'},\n",
+       "                          {'line': {'color': '#1f77b4', 'width': 2},\n",
+       "                           'type': 'line',\n",
+       "                           'x0': 6242.7628615098165,\n",
+       "                           'x1': 6242.7628615098165,\n",
+       "                           'xref': 'x',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y domain'},\n",
+       "                          {'line': {'color': '#1f77b4', 'width': 2},\n",
+       "                           'type': 'line',\n",
+       "                           'x0': 36149.32553478348,\n",
+       "                           'x1': 36149.32553478348,\n",
+       "                           'xref': 'x',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y domain'},\n",
+       "                          {'fillcolor': '#1f77b4',\n",
+       "                           'line': {'width': 0},\n",
+       "                           'opacity': 0.3,\n",
+       "                           'type': 'rect',\n",
+       "                           'x0': 85.28312133869836,\n",
+       "                           'x1': 101.50677381488379,\n",
+       "                           'xref': 'x2',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y2 domain'},\n",
+       "                          {'line': {'color': '#1f77b4', 'width': 2},\n",
+       "                           'type': 'line',\n",
+       "                           'x0': 85.28312133869836,\n",
+       "                           'x1': 85.28312133869836,\n",
+       "                           'xref': 'x2',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y2 domain'},\n",
+       "                          {'line': {'color': '#1f77b4', 'width': 2},\n",
+       "                           'type': 'line',\n",
+       "                           'x0': 101.50677381488379,\n",
+       "                           'x1': 101.50677381488379,\n",
+       "                           'xref': 'x2',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y2 domain'}],\n",
+       "               'showlegend': False,\n",
+       "               'template': '...',\n",
+       "               'title': {'text': 'Simulation-Based Bootstrap Distribution'},\n",
+       "               'xaxis': {'anchor': 'y', 'domain': [0.0, 0.2888888888888889]},\n",
+       "               'xaxis2': {'anchor': 'y2', 'domain': [0.35555555555555557, 0.6444444444444445]},\n",
+       "               'xaxis3': {'anchor': 'y3', 'domain': [0.7111111111111111, 1.0]},\n",
+       "               'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0]},\n",
+       "               'yaxis2': {'anchor': 'x2', 'domain': [0.0, 1.0]},\n",
+       "               'yaxis3': {'anchor': 'x3', 'domain': [0.0, 1.0]}}\n",
+       "})"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from moderndive.infer.viz import visualize_fit\n",
+    "\n",
+    "houses = md.load_saratoga_houses()\n",
+    "boot_fit = (\n",
+    "    specify(houses, formula=\"price ~ living_area + bedrooms\")\n",
+    "    .generate(reps=1000, type=\"bootstrap\", seed=1)\n",
+    "    .fit()\n",
+    ")\n",
+    "visualize_fit(boot_fit) + shade_confidence_interval(boot_fit.get_confidence_interval())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33b65260",
+   "metadata": {},
+   "source": [
+    "## Model plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e12a1dcf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from moderndive import gg_parallel_slopes\n",
+    "\n",
+    "evals = md.load_evals()\n",
+    "gg_parallel_slopes(evals, response=\"score\", explanatory=\"age\", by=\"gender\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1a9fb5fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from moderndive import gg_categorical_model\n",
+    "\n",
+    "gg_categorical_model(evals, response=\"score\", explanatory=\"rank\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "11d0277e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from moderndive import pairplot\n",
+    "\n",
+    "# Scatterplot matrix (~ GGally::ggpairs)\n",
+    "pairplot(md.load_coffee_quality(), columns=[\"total_cup_points\", \"aroma\", \"flavor\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dca282a4",
+   "metadata": {},
+   "source": [
+    "## Saving figures\n",
+    "\n",
+    "```python\n",
+    "p = visualize(boot)            # plotly\n",
+    "p.save(\"dist.html\")            # interactive HTML (no extra deps)\n",
+    "p.save(\"dist.png\")             # static image — needs: pip install \"moderndive[image]\"\n",
+    "\n",
+    "g = visualize(boot, engine=\"plotnine\")\n",
+    "g.save(\"dist.png\", width=6, height=4, dpi=150)\n",
+    "```\n",
+    "\n",
+    "`pairplot(..., engine=\"seaborn\")` (alias `\"plotnine\"`) returns a Matplotlib figure\n",
+    "if you prefer the seaborn-backed scatterplot matrix."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "text_representation": {
+    "extension": ".md",
+    "format_name": "myst"
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.3"
+  },
+  "source_map": [
+   11,
+   17,
+   30,
+   44,
+   52,
+   56,
+   59,
+   65,
+   67,
+   71,
+   82,
+   86,
+   90,
+   92,
+   98,
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+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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diff --git a/doc/_build/jupyter_execute/guides/regression.ipynb b/doc/_build/jupyter_execute/guides/regression.ipynb
new file mode 100644
index 0000000..6fcc6a3
--- /dev/null
+++ b/doc/_build/jupyter_execute/guides/regression.ipynb
@@ -0,0 +1,565 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c2670bc0",
+   "metadata": {
+    "tags": [
+     "remove-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.use(\"Agg\")\n",
+    "import plotly.io as pio\n",
+    "pio.renderers.default = \"png\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3a66eec",
+   "metadata": {},
+   "source": [
+    "# Regression\n",
+    "\n",
+    "The regression helpers turn a fitted [statsmodels](https://www.statsmodels.org)\n",
+    "model into tidy polars tables — the analog of R `moderndive`'s\n",
+    "`get_regression_table()` / `get_regression_points()` / `get_regression_summaries()`.\n",
+    "\n",
+    "Fit models with statsmodels' formula API, then tidy them:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5ad5e15d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import statsmodels.formula.api as smf\n",
+    "import moderndive as md\n",
+    "from moderndive import (\n",
+    "    get_regression_table, get_regression_points, get_regression_summaries, get_correlation,\n",
+    ")\n",
+    "\n",
+    "houses = md.load_saratoga_houses()\n",
+    "model = smf.ols(\"price ~ living_area + bedrooms\", data=houses.to_pandas()).fit()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a50af0cd",
+   "metadata": {},
+   "source": [
+    "## Coefficient table (with confidence intervals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0a8d464b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (3, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>term</th><th>estimate</th><th>std_error</th><th>statistic</th><th>p_value</th><th>lower_ci</th><th>upper_ci</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;intercept&quot;</td><td>20986.094</td><td>6816.251</td><td>3.079</td><td>0.002</td><td>7611.128</td><td>34361.06</td></tr><tr><td>&quot;living_area&quot;</td><td>93.842</td><td>3.109</td><td>30.183</td><td>0.0</td><td>87.741</td><td>99.943</td></tr><tr><td>&quot;bedrooms&quot;</td><td>-7483.095</td><td>2783.531</td><td>-2.688</td><td>0.007</td><td>-12944.988</td><td>-2021.203</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (3, 7)\n",
+       "┌─────────────┬───────────┬───────────┬───────────┬─────────┬────────────┬───────────┐\n",
+       "│ term        ┆ estimate  ┆ std_error ┆ statistic ┆ p_value ┆ lower_ci   ┆ upper_ci  │\n",
+       "│ ---         ┆ ---       ┆ ---       ┆ ---       ┆ ---     ┆ ---        ┆ ---       │\n",
+       "│ str         ┆ f64       ┆ f64       ┆ f64       ┆ f64     ┆ f64        ┆ f64       │\n",
+       "╞═════════════╪═══════════╪═══════════╪═══════════╪═════════╪════════════╪═══════════╡\n",
+       "│ intercept   ┆ 20986.094 ┆ 6816.251  ┆ 3.079     ┆ 0.002   ┆ 7611.128   ┆ 34361.06  │\n",
+       "│ living_area ┆ 93.842    ┆ 3.109     ┆ 30.183    ┆ 0.0     ┆ 87.741     ┆ 99.943    │\n",
+       "│ bedrooms    ┆ -7483.095 ┆ 2783.531  ┆ -2.688    ┆ 0.007   ┆ -12944.988 ┆ -2021.203 │\n",
+       "└─────────────┴───────────┴───────────┴───────────┴─────────┴────────────┴───────────┘"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_regression_table(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "057b8f6d",
+   "metadata": {},
+   "source": [
+    "Change the confidence level with `conf_level=` (e.g. `0.99`).\n",
+    "\n",
+    "## Fitted values & residuals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3857437b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>ID</th><th>price</th><th>living_area</th><th>bedrooms</th><th>price_hat</th><th>residual</th></tr><tr><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>1</td><td>142212</td><td>1982</td><td>3</td><td>184531.942</td><td>-42319.942</td></tr><tr><td>2</td><td>134865</td><td>1676</td><td>3</td><td>155816.245</td><td>-20951.245</td></tr><tr><td>3</td><td>118007</td><td>1694</td><td>3</td><td>157505.404</td><td>-39498.404</td></tr><tr><td>4</td><td>138297</td><td>1800</td><td>2</td><td>174935.767</td><td>-36638.767</td></tr><tr><td>5</td><td>129470</td><td>2088</td><td>3</td><td>194479.21</td><td>-65009.21</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 6)\n",
+       "┌─────┬────────┬─────────────┬──────────┬────────────┬────────────┐\n",
+       "│ ID  ┆ price  ┆ living_area ┆ bedrooms ┆ price_hat  ┆ residual   │\n",
+       "│ --- ┆ ---    ┆ ---         ┆ ---      ┆ ---        ┆ ---        │\n",
+       "│ i64 ┆ i64    ┆ i64         ┆ i64      ┆ f64        ┆ f64        │\n",
+       "╞═════╪════════╪═════════════╪══════════╪════════════╪════════════╡\n",
+       "│ 1   ┆ 142212 ┆ 1982        ┆ 3        ┆ 184531.942 ┆ -42319.942 │\n",
+       "│ 2   ┆ 134865 ┆ 1676        ┆ 3        ┆ 155816.245 ┆ -20951.245 │\n",
+       "│ 3   ┆ 118007 ┆ 1694        ┆ 3        ┆ 157505.404 ┆ -39498.404 │\n",
+       "│ 4   ┆ 138297 ┆ 1800        ┆ 2        ┆ 174935.767 ┆ -36638.767 │\n",
+       "│ 5   ┆ 129470 ┆ 2088        ┆ 3        ┆ 194479.21  ┆ -65009.21  │\n",
+       "└─────┴────────┴─────────────┴──────────┴────────────┴────────────┘"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_regression_points(model).head()\n",
+    "# columns: ID, price, living_area, bedrooms, price_hat, residual"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5102446",
+   "metadata": {},
+   "source": [
+    "## Model-fit summaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8263ee73",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (1, 9)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>r_squared</th><th>adj_r_squared</th><th>mse</th><th>rmse</th><th>sigma</th><th>statistic</th><th>p_value</th><th>df</th><th>nobs</th></tr><tr><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i64</td><td>i64</td></tr></thead><tbody><tr><td>0.578</td><td>0.578</td><td>2.5071e9</td><td>50071.187</td><td>50142.395</td><td>723.229</td><td>0.0</td><td>2</td><td>1057</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (1, 9)\n",
+       "┌───────────┬───────────────┬──────────┬───────────┬───┬───────────┬─────────┬─────┬──────┐\n",
+       "│ r_squared ┆ adj_r_squared ┆ mse      ┆ rmse      ┆ … ┆ statistic ┆ p_value ┆ df  ┆ nobs │\n",
+       "│ ---       ┆ ---           ┆ ---      ┆ ---       ┆   ┆ ---       ┆ ---     ┆ --- ┆ ---  │\n",
+       "│ f64       ┆ f64           ┆ f64      ┆ f64       ┆   ┆ f64       ┆ f64     ┆ i64 ┆ i64  │\n",
+       "╞═══════════╪═══════════════╪══════════╪═══════════╪═══╪═══════════╪═════════╪═════╪══════╡\n",
+       "│ 0.578     ┆ 0.578         ┆ 2.5071e9 ┆ 50071.187 ┆ … ┆ 723.229   ┆ 0.0     ┆ 2   ┆ 1057 │\n",
+       "└───────────┴───────────────┴──────────┴───────────┴───┴───────────┴─────────┴─────┴──────┘"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_regression_summaries(model)\n",
+    "# r_squared, adj_r_squared, mse, rmse, sigma, statistic (F), p_value, df, nobs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c73d51b8",
+   "metadata": {},
+   "source": [
+    "## Correlation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e7d9a0c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (1, 1)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>cor</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.758674</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (1, 1)\n",
+       "┌──────────┐\n",
+       "│ cor      │\n",
+       "│ ---      │\n",
+       "│ f64      │\n",
+       "╞══════════╡\n",
+       "│ 0.758674 │\n",
+       "└──────────┘"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_correlation(houses, \"price ~ living_area\")   # ≈ 0.759\n",
+    "# or: get_correlation(houses, x=\"living_area\", y=\"price\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa993617",
+   "metadata": {},
+   "source": [
+    "## Visualizing models\n",
+    "\n",
+    "Two ports of R `moderndive`'s ggplot helpers, both dual-engine:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "16afab96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from moderndive import gg_parallel_slopes, gg_categorical_model\n",
+    "\n",
+    "evals = md.load_evals()\n",
+    "\n",
+    "# Parallel-slopes model: one common slope, a separate intercept per group\n",
+    "gg_parallel_slopes(evals, response=\"score\", explanatory=\"age\", by=\"gender\")            # plotly\n",
+    "gg_parallel_slopes(evals, response=\"score\", explanatory=\"age\", by=\"gender\",\n",
+    "                   engine=\"plotnine\")\n",
+    "\n",
+    "# Regression with a single categorical predictor\n",
+    "gg_categorical_model(evals, response=\"score\", explanatory=\"rank\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04e30907",
+   "metadata": {},
+   "source": [
+    "For the plotnine engine you can also drop the parallel-slopes lines onto your own\n",
+    "`ggplot` with `geom_parallel_slopes()`.\n",
+    "\n",
+    "## Inference for regression coefficients\n",
+    "\n",
+    "`fit()` runs the regression on every bootstrap/permutation replicate, giving a\n",
+    "distribution per coefficient. Pair it with `visualize_fit` and **per-facet**\n",
+    "shading:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "08dc9c71",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
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a88LSalAG1ZBGF3YncCUGEdLPp2lQAKcS2Zlp8BAIO+dWpSNWUCmpkPozg+WQG4+n\\u002flAx6XAWC9\\u002fJrLZisBQRacfIIpwQGwauLZEtrNAeMUaI2h3hUDcMDzp4VC5wLC78aMpOXNAzXSZ8pwOk0CK0kzT6Le8QOSFUmrcs6nALP\\u002f51c4ns0CsNNHtsbqzQBAlOllrssBAcICtiUYDhkAX0t5iX0qhwEbuYbQnirPAlciSNVvgp8CoEmJ70zWSQNoREH7usLfA6MoucDpakUDsnTwHKkqUQJpmQ4f90aXAOHrdP9gWgUBiBGzb092gwCAUTGXc\\u002fmpAiBnTpNytlEAUNges+9uvQCSX+Q6GLHrAAItTT91Ku8AZfjhlY0GUwAD6z4C8ZJRAHiudadJMtMALEjn6H6yfwH+dH2tV0bDANAXRZ6WEnUCoAjuJ8zWUwG66ibjjQJnAX1Cn04gxtcAyRdLuL06mwALzWlY8DbDAvSmz8i5rtEAYmjmN5EamQDpaWDx7wsHAaG7ZAI5susBgvXH7UKl8QJB\\u002fsY5VtbPAwD2y2ypOcECo4o6+7i2KwEjuvu+SLYhA\\u002fC\\u002f63tZth0CwpeceXNKmQGDoQXsd3LbA5B7vK3HHtkBIAotss66EwNIw6uqmG61AVh5KqXBcr8AavbMJmKauwPRC33FEfrJA0iJ+2i5tpEAkYV++YWieQLQykDISgYVASmjzzQwYxUCUoSgd7niqQPlSIaOoUrVAZsYGHsnNmEDPj4eD8pO+wEa9JTIcrKdAUNNhZ1lQgkCc2RMLy26qQMuRQYESz7ZAKaXQMkdbwcA4E2\\u002fbzY+8QBtCms3nGKBAF3tvHc9Fg0DOP43J4uTBQCjWYIvyHZ\\u002fAOFDV7CiqrsDDIEDvcvqgQA8eU1u7S69ApSG66jZXsUAfk0hCE8KqQKARjSQqhKNAKvRBJZ0sh8BFpeioLgWkwJw8BhDItZbAgdZmuXozokBft6KNH8SNQH8VDgljuZBAZZnznffvlMAq+GA+3ySVQORH1jdP66dA9v7FPcWRksAhKFp153G+wI5NtgLewrlA0l\\u002fbVvyevcC+N6rRLUGSwIrdv\\u002fBmnbZArmccw+aSksDwueD0MMtvwPRGn35T2XHAECOhLHiEssBErlm5PXKaQFxwvdppZInAiKIA3FIpkcCO6MVC1GicQHsiIRPrDrzA9lacmmmMp8Dixtqjr+KnQMy1AF4+4JJAklLc67hPvEBYTamSEGC0QO7AQV8PEpbAbAuuVdn4uMDCjmGGHjekQLQxZJFApKBAqIG+EUhXnEBwUqzQR528QBCkWaafnsXAKK7XMGxyu0DYrqOIUUi2QMmTbv16KKXAVjEIoYnZrkB4\\u002fJJ1wsa8wJAWcH+QBqVA9QI85ljIskBJjX6VOYehQLQof1+41aVAXZZ3F9cstMBpJX6j9mmswMoanm8OkKTAWsEYFPHsmUCSnb+BNAaHwCKgkhj\\u002feaVAtItY2kSAfMAPslXp6Z2zQIaIdpWba7BA4fV8h2n+o8A+X8lZH2y1QKAPWgRUHFZAMOpQs6oRacCQxcZfHgl8QJIg6fRFkZ3AEgyVaOXNp8AI+iT2kNyzwBKRrVZHHKxARO5Q5YmhlMAmYHhi9n7DwHoOIFjurpJAGCwZenWZcsCmOJIUhxaywAWIrcto4sFAKFYP1blNgcCchcUOJX2fwNaXSqOnxpHAdtCC8f+QmMAovLZe5AizwGT8sh5PiqfAkPhSqaK0tcCfY61bxLa2wMx8nm4N4ppAcC7PaLu8fkABg5LcnVu5QBBKYWLed2lAHQUqP91sosAIHj8o76qwwC4iOP2cdbhAYOY9LHxje8AkCICsyU+GwEVSaYQJHKtAestORw76pkBNQYlOFLu6QMhOdqKUzJXAhkLP+k4SdMDNKbVWBMSiQDBeB9MaLrBA\\u002fIzETTgYlMCFj3yGSIaVwAoILed19bJA16Oqcv6TscC4hBOoLruFwDzm443DEbpAdq0e2t9MpMD5vH0\\u002fppmowCKUcB6ms41Am9lxekwepUDZrN6ObuygQHOmxIQsQq9AANS3aL3rlMBCZiXM\\u002ffCpQADi\\u002fJxtERHAHO433X5QmcDpdPoZfaSywA5iLlHkSKrACpgSxM\\u002fntsDWzM5QsUClwGkIaHOzn6tAygtbPw38kMCGNnr2aN23QNNRFBiXir1AKF4ACWWDrMA26uq+jvhzQPgW0\\u002fERFbhAB0TjhZ5PtUAbGIbRMN2wwES0+jW3TbXAbEpnBXFXuUAw8u25DPKeQFwnAendNZRAYSTLKKMmwkDWkI7DrWOiwAATq0AueprAwXWefCVCs0CQmALiVnqEwNofISTZmI\\u002fACvz4xdkMpUANOZq7VZTBwGYmWWdzH7nA7KqG6TQ+gsAAxRP\\u002figJcQCAveWlBNlFAkJ7eE2zjsEAebUa48tavwA67GZAw+qbACLZTTkaqm8DseOLKSFipwEATQJFNA0rA++o93WgNnkA8iqU+EuSjQBEB+OrJSr7ArIADtIR5n0C9x3aC\\u002fQ+iQDLqjK8ZR5dANpkXr9havUAkAA806IqaQBHui6CXaopAyCOL8q0erEBYVpq98MuFQFSNjUySsY1AtpXS2ktvokDaaDCYNUucwLrvuomJurRAvZsbt70oo0AsjwrantRyQFe6OQFFMqzA4DpeMyKyZUDwJchRlZ5zwJ3VyqOPqp5ApMZHxXNQtsBAu0aF3rWAQAk9\\u002fmMrgZNAgxFpFDhmrEBADKyJgkORQEk23FZoI7fAbCEP8VvtiMBtEkVsLQS+QLAYT9XJGYLAcIQGMHxNoMCSvldSTJevwG4SpIT7RqHAYbUdQQhZy8AjbTEZXN26QOColyMXqsnAuIMGkLfhfcC48cdYB1CtwENWMUG5MppAPl8r81YQlsBzjkBbpmqrQHUygPBFV6\\u002fAHGCPwZ5ksMBKlPtLOgOywBpBnR7purbAuEuMDbSrg8DzKUwf532iQA5iZZuguLnAAgZqUiLKmEDJH4VI\\u002fia6wDQ1W0DsfoLA+u7UGItUrUDwxlsdDGnCQP98+CimHrXAAJdVzC+kr0BcpC6MJWrAQEBDBrYYnaNAY\\u002f\\u002fXqpdSr0AYtsSd4u6zQPHcYam3sqDAvqNe7q5+scB87FbgSXOMQNuxc6Z8urdA1BsCNH0vvcA9oUW0scmbQJCoNJHbHGjAcCeyUvzTsECY2syanbCWwHvjIRdueZdAX1tU\\u002fbqDsEAI+PzymmKtQJF83t9T8ZjAsq64iRgKlcC7G0ZXlie8wLVFGcwXyahA5LZdQReimcDMZB9DCNqfQOB4M2CmgX1AgXzS8hJLuMCQddy5ZytxQMqzVCXxDrPAht1dAdeswsD0C0xgNs+hQAnWv\\u002fymgZRAAaA+NnI\\u002fs8D5ktNoBKO\\u002fwBYrtAJoUbhA4hUKRNY8xkAamlz8slKwQNbLc4VoC57A+i8facjiuMB0GMdr38G8wEpEd\\u002fvJk7JAVmtlm3cgnkDd1HecgwO2QKiSs5faYZDAu5zGRQ17tEDqIPNh4pSpQLWdDYpkIqjAcp09duoYo0AW5\\u002fnVwna8QGZ1e8yK86PATmrYJxKArsAuBLtj+7C4QPRJvtLXoabAMN7XkX\\u002f8fMCS32a18cfDQADWKkInsx1AvhTKnb8VtcDZGDtrhzOcQMIvlhFv9YNAJH8\\u002fCcS\\u002fckDCXOdJs9SUQEAvDTNYIqdA\\u002ffdRCqswkUAa1WvTxd+8wDg1783uzbPAwWVCxm+klMDK+cOE6weqwJglOv1bM1vAABNDUZ0AgUCpv7DOyE+cwFi9J+ede6fAWuOwjirPkkDRb6KKYpa5wEJ55udpcqNAg5AecB6Iw0Dm1dPIHXWzQB8TfL8b37nAqplpSwY3wUDjQfzjNvKtQGzaO+57sqJAbEjqmNWNtsCrBoCS50LCQLi0lnC4XrfA9aZmSoLnp0BozvYWjBG3QJ2Zd0lBUa1AVYXuJlo3oEBOEofrovSgwBsyOGTEfqjAAsZoOTeLucDGj+8jYsSTQPf+1DyClMHAuBediS+8r0A\\u002fVkuaGULCwGbAFr9rUJdATh6pV1dKnEDWN8n4Qx+sQPisl9eo9qXAPBNo20yUt0A1c27MjWKjwADj\\u002fc+OZoPACBSdfCOHrkBDH5Qu+pqwQOjg1sUXk6pAABVCY8ietkCpS846R32XwOYIez8scqdAjA\\u002fZqn31eUCARY2lyAg9wPR+3hLqTorAr9uZluBtpsDUhCHt8RKUwD9asLeQyrTAYAVfxoBAuUDWIGYpxf+zwE133MwPvqTA7KKNxSMcrsCU9LBArLR9wMlPPLIwu6pAflyVeiKlrsDIR0XZJOG1wJx9GWXAs6BAPufjWBBdmkAexGhluNCowC86QLf5LbtACh8jr+NbuMCbozJwQWejQJ7EDynhB7NA0i4Hmd0RkcB3Kx9PbOuuwOjKyJK3763A4FKD5ywDaUCkQgS4fqGIQOMQLoJqGp3A4CVZ0h4akkAiMKZn+QijQEBsyk1ObLpA8zbG9YUKo8AM6aI0OH2kwLA7njsZvbFAk9Vf1GpiocD4Xa21v8aFwLyJIxyxd6BA5Ea5W+oHkMDYvY7RrmyGQIZCHXbsmqTAYuSzeLE7msBwUVvnzUyBQEn+N3XHnLXAubbHhJIxkcDRdfUOT3qoQNi\\u002ft5diIppAJxEGOt\\u002fspUDwmZ5v0BhywAT3AqjUFbHAnFGFyXDsqkBton+5Q7mRwI7yWjpP4KFA6Jg+DW3It8C+SbGiKFyIQBjjrp18B6jAAujI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+       "                           'xref': 'x2',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y2 domain'}],\n",
+       "               'showlegend': False,\n",
+       "               'template': '...',\n",
+       "               'title': {'text': 'Simulation-Based Null Distribution'},\n",
+       "               'xaxis': {'anchor': 'y', 'domain': [0.0, 0.2888888888888889]},\n",
+       "               'xaxis2': {'anchor': 'y2', 'domain': [0.35555555555555557, 0.6444444444444445]},\n",
+       "               'xaxis3': {'anchor': 'y3', 'domain': [0.7111111111111111, 1.0]},\n",
+       "               'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0]},\n",
+       "               'yaxis2': {'anchor': 'x2', 'domain': [0.0, 1.0]},\n",
+       "               'yaxis3': {'anchor': 'x3', 'domain': [0.0, 1.0]}}\n",
+       "})"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from moderndive import specify\n",
+    "from moderndive.infer.viz import visualize_fit\n",
+    "from moderndive import shade_confidence_interval, shade_p_value\n",
+    "\n",
+    "f = \"price ~ living_area + bedrooms\"\n",
+    "obs_fit = specify(houses, formula=f).fit()\n",
+    "\n",
+    "# Bootstrap distribution of each coefficient → per-term CIs\n",
+    "boot_fit = specify(houses, formula=f).generate(reps=1000, type=\"bootstrap\", seed=1).fit()\n",
+    "boot_fit.get_confidence_interval(level=0.95)          # one row per term\n",
+    "visualize_fit(boot_fit) + shade_confidence_interval(boot_fit.get_confidence_interval())\n",
+    "\n",
+    "# Null distribution → per-term p-values, each facet shaded at its own estimate\n",
+    "null_fit = (\n",
+    "    specify(houses, formula=f).hypothesize(null=\"independence\")\n",
+    "    .generate(reps=1000, type=\"permute\", seed=1).fit()\n",
+    ")\n",
+    "null_fit.get_p_value(obs_stat=obs_fit, direction=\"two-sided\")\n",
+    "visualize_fit(null_fit) + shade_p_value(obs_stat=obs_fit, direction=\"two-sided\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6477edc9",
+   "metadata": {},
+   "source": [
+    "Per-facet shading works in both engines: `shade_p_value` / `shade_confidence_interval`\n",
+    "accept the observed `FitResult` or a term-keyed table, and each facet is shaded\n",
+    "from its own value."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "text_representation": {
+    "extension": ".md",
+    "format_name": "myst"
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.3"
+  },
+  "source_map": [
+   11,
+   17,
+   27,
+   36,
+   40,
+   42,
+   49,
+   52,
+   56,
+   59,
+   63,
+   66,
+   72,
+   84,
+   95,
+   115
+  ]
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/doc/_build/jupyter_execute/guides/sampling.ipynb b/doc/_build/jupyter_execute/guides/sampling.ipynb
new file mode 100644
index 0000000..d86d578
--- /dev/null
+++ b/doc/_build/jupyter_execute/guides/sampling.ipynb
@@ -0,0 +1,363 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "16cf0433",
+   "metadata": {
+    "tags": [
+     "remove-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.use(\"Agg\")\n",
+    "import plotly.io as pio\n",
+    "pio.renderers.default = \"png\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe865f3b",
+   "metadata": {},
+   "source": [
+    "# Sampling\n",
+    "\n",
+    "Sampling activities (the \"bowl\" of balls, tactile samples) are how ModernDive\n",
+    "builds intuition for sampling variation. `rep_slice_sample` takes repeated samples\n",
+    "and stacks them with a `replicate` column — the analog of R\n",
+    "`moderndive::rep_slice_sample()` / `infer::rep_sample_n()`.\n",
+    "\n",
+    "## The bowl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9c71d6ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>ball_ID</th><th>color</th></tr><tr><td>i64</td><td>str</td></tr></thead><tbody><tr><td>1</td><td>&quot;white&quot;</td></tr><tr><td>2</td><td>&quot;white&quot;</td></tr><tr><td>3</td><td>&quot;white&quot;</td></tr><tr><td>4</td><td>&quot;red&quot;</td></tr><tr><td>5</td><td>&quot;white&quot;</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 2)\n",
+       "┌─────────┬───────┐\n",
+       "│ ball_ID ┆ color │\n",
+       "│ ---     ┆ ---   │\n",
+       "│ i64     ┆ str   │\n",
+       "╞═════════╪═══════╡\n",
+       "│ 1       ┆ white │\n",
+       "│ 2       ┆ white │\n",
+       "│ 3       ┆ white │\n",
+       "│ 4       ┆ red   │\n",
+       "│ 5       ┆ white │\n",
+       "└─────────┴───────┘"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import moderndive as md\n",
+    "import polars as pl\n",
+    "\n",
+    "bowl = md.load_bowl()      # 2400 red/white balls\n",
+    "bowl.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1329399",
+   "metadata": {},
+   "source": [
+    "## One virtual sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "79bbe22f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (1, 1)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>prop_red</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.38</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (1, 1)\n",
+       "┌──────────┐\n",
+       "│ prop_red │\n",
+       "│ ---      │\n",
+       "│ f64      │\n",
+       "╞══════════╡\n",
+       "│ 0.38     │\n",
+       "└──────────┘"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from moderndive import rep_slice_sample\n",
+    "\n",
+    "sample = rep_slice_sample(bowl, n=50, seed=1)\n",
+    "# proportion red in this sample\n",
+    "sample.select((pl.col(\"color\") == \"red\").mean().alias(\"prop_red\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "785876de",
+   "metadata": {},
+   "source": [
+    "## Many samples → a sampling distribution\n",
+    "\n",
+    "Take 1000 samples of size 50 and compute the proportion red in each:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8394ffd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>replicate</th><th>prop_red</th></tr><tr><td>i64</td><td>f64</td></tr></thead><tbody><tr><td>1</td><td>0.38</td></tr><tr><td>2</td><td>0.42</td></tr><tr><td>3</td><td>0.42</td></tr><tr><td>4</td><td>0.42</td></tr><tr><td>5</td><td>0.46</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 2)\n",
+       "┌───────────┬──────────┐\n",
+       "│ replicate ┆ prop_red │\n",
+       "│ ---       ┆ ---      │\n",
+       "│ i64       ┆ f64      │\n",
+       "╞═══════════╪══════════╡\n",
+       "│ 1         ┆ 0.38     │\n",
+       "│ 2         ┆ 0.42     │\n",
+       "│ 3         ┆ 0.42     │\n",
+       "│ 4         ┆ 0.42     │\n",
+       "│ 5         ┆ 0.46     │\n",
+       "└───────────┴──────────┘"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "samples = rep_slice_sample(bowl, n=50, reps=1000, seed=1)\n",
+    "\n",
+    "prop_red = (\n",
+    "    samples\n",
+    "    .group_by(\"replicate\")\n",
+    "    .agg((pl.col(\"color\") == \"red\").mean().alias(\"prop_red\"))\n",
+    ")\n",
+    "prop_red.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06e11137",
+   "metadata": {},
+   "source": [
+    "That `prop_red` column is a **sampling distribution**. Visualize its spread the\n",
+    "same way you would any distribution (see {doc}`confidence-intervals` for building\n",
+    "one with the inference pipeline instead).\n",
+    "\n",
+    "## With vs. without replacement\n",
+    "\n",
+    "`rep_slice_sample` samples **without** replacement by default (like dealing from a\n",
+    "deck). Pass `replace=True` for bootstrap-style resampling:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "81143aaa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (50_000, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>replicate</th><th>ball_ID</th><th>color</th></tr><tr><td>i64</td><td>i64</td><td>str</td></tr></thead><tbody><tr><td>1</td><td>1136</td><td>&quot;white&quot;</td></tr><tr><td>1</td><td>1229</td><td>&quot;red&quot;</td></tr><tr><td>1</td><td>1813</td><td>&quot;white&quot;</td></tr><tr><td>1</td><td>2282</td><td>&quot;red&quot;</td></tr><tr><td>1</td><td>84</td><td>&quot;white&quot;</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>1000</td><td>439</td><td>&quot;white&quot;</td></tr><tr><td>1000</td><td>1203</td><td>&quot;red&quot;</td></tr><tr><td>1000</td><td>252</td><td>&quot;white&quot;</td></tr><tr><td>1000</td><td>1113</td><td>&quot;white&quot;</td></tr><tr><td>1000</td><td>1277</td><td>&quot;red&quot;</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (50_000, 3)\n",
+       "┌───────────┬─────────┬───────┐\n",
+       "│ replicate ┆ ball_ID ┆ color │\n",
+       "│ ---       ┆ ---     ┆ ---   │\n",
+       "│ i64       ┆ i64     ┆ str   │\n",
+       "╞═══════════╪═════════╪═══════╡\n",
+       "│ 1         ┆ 1136    ┆ white │\n",
+       "│ 1         ┆ 1229    ┆ red   │\n",
+       "│ 1         ┆ 1813    ┆ white │\n",
+       "│ 1         ┆ 2282    ┆ red   │\n",
+       "│ 1         ┆ 84      ┆ white │\n",
+       "│ …         ┆ …       ┆ …     │\n",
+       "│ 1000      ┆ 439     ┆ white │\n",
+       "│ 1000      ┆ 1203    ┆ red   │\n",
+       "│ 1000      ┆ 252     ┆ white │\n",
+       "│ 1000      ┆ 1113    ┆ white │\n",
+       "│ 1000      ┆ 1277    ┆ red   │\n",
+       "└───────────┴─────────┴───────┘"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rep_slice_sample(bowl, n=50, reps=1000, replace=True, seed=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebb972ee",
+   "metadata": {},
+   "source": [
+    "## Tactile samples\n",
+    "\n",
+    "The hand-collected counterparts are bundled too:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d3f3a45a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (33, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>group</th><th>replicate</th><th>red_balls</th><th>prop_red</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Ilyas, Yohan&quot;</td><td>1</td><td>21</td><td>0.42</td></tr><tr><td>&quot;Morgan, Terrance&quot;</td><td>2</td><td>17</td><td>0.34</td></tr><tr><td>&quot;Martin, Thomas&quot;</td><td>3</td><td>21</td><td>0.42</td></tr><tr><td>&quot;Clark, Frank&quot;</td><td>4</td><td>21</td><td>0.42</td></tr><tr><td>&quot;Riddhi, Karina&quot;</td><td>5</td><td>18</td><td>0.36</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;Julie, Hailin&quot;</td><td>29</td><td>15</td><td>0.3</td></tr><tr><td>&quot;Katie, Caroline&quot;</td><td>30</td><td>21</td><td>0.42</td></tr><tr><td>&quot;Mallory, Damani, Melissa&quot;</td><td>31</td><td>21</td><td>0.42</td></tr><tr><td>&quot;Katie&quot;</td><td>32</td><td>16</td><td>0.32</td></tr><tr><td>&quot;Francis, Vignesh&quot;</td><td>33</td><td>19</td><td>0.38</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (33, 4)\n",
+       "┌──────────────────────────┬───────────┬───────────┬──────────┐\n",
+       "│ group                    ┆ replicate ┆ red_balls ┆ prop_red │\n",
+       "│ ---                      ┆ ---       ┆ ---       ┆ ---      │\n",
+       "│ str                      ┆ i64       ┆ i64       ┆ f64      │\n",
+       "╞══════════════════════════╪═══════════╪═══════════╪══════════╡\n",
+       "│ Ilyas, Yohan             ┆ 1         ┆ 21        ┆ 0.42     │\n",
+       "│ Morgan, Terrance         ┆ 2         ┆ 17        ┆ 0.34     │\n",
+       "│ Martin, Thomas           ┆ 3         ┆ 21        ┆ 0.42     │\n",
+       "│ Clark, Frank             ┆ 4         ┆ 21        ┆ 0.42     │\n",
+       "│ Riddhi, Karina           ┆ 5         ┆ 18        ┆ 0.36     │\n",
+       "│ …                        ┆ …         ┆ …         ┆ …        │\n",
+       "│ Julie, Hailin            ┆ 29        ┆ 15        ┆ 0.3      │\n",
+       "│ Katie, Caroline          ┆ 30        ┆ 21        ┆ 0.42     │\n",
+       "│ Mallory, Damani, Melissa ┆ 31        ┆ 21        ┆ 0.42     │\n",
+       "│ Katie                    ┆ 32        ┆ 16        ┆ 0.32     │\n",
+       "│ Francis, Vignesh         ┆ 33        ┆ 19        ┆ 0.38     │\n",
+       "└──────────────────────────┴───────────┴───────────┴──────────┘"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "md.load_tactile_prop_red()   # 33 groups' samples of 50 balls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e42c9a2",
+   "metadata": {},
+   "source": [
+    "```{seealso}\n",
+    "`rep_sample_n` is an alias of `rep_slice_sample` (the older infer name). Both are\n",
+    "documented in the {doc}`../api`.\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "text_representation": {
+    "extension": ".md",
+    "format_name": "myst"
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.3"
+  },
+  "source_map": [
+   11,
+   17,
+   28,
+   34,
+   38,
+   44,
+   50,
+   59,
+   70,
+   72,
+   78,
+   80
+  ]
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/doc/_build/jupyter_execute/guides/theory-based.ipynb b/doc/_build/jupyter_execute/guides/theory-based.ipynb
new file mode 100644
index 0000000..c7f488d
--- /dev/null
+++ b/doc/_build/jupyter_execute/guides/theory-based.ipynb
@@ -0,0 +1,272 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5606ac5b",
+   "metadata": {
+    "tags": [
+     "remove-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.use(\"Agg\")\n",
+    "import plotly.io as pio\n",
+    "pio.renderers.default = \"png\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "337d8d6c",
+   "metadata": {},
+   "source": [
+    "# Theory-based inference\n",
+    "\n",
+    "Alongside the simulation grammar, `moderndive` ships tidy wrappers for the\n",
+    "classical (formula-based) tests and a way to draw the theoretical distributions —\n",
+    "mirroring R `infer`'s `t_test`, `prop_test`, `chisq_test`, and `assume()`.\n",
+    "\n",
+    "## One-line tests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5ef17e1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (1, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>statistic</th><th>chisq_df</th><th>p_value</th></tr><tr><td>f64</td><td>i64</td><td>f64</td></tr></thead><tbody><tr><td>0.105042</td><td>1</td><td>0.745861</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (1, 3)\n",
+       "┌───────────┬──────────┬──────────┐\n",
+       "│ statistic ┆ chisq_df ┆ p_value  │\n",
+       "│ ---       ┆ ---      ┆ ---      │\n",
+       "│ f64       ┆ i64      ┆ f64      │\n",
+       "╞═══════════╪══════════╪══════════╡\n",
+       "│ 0.105042  ┆ 1        ┆ 0.745861 │\n",
+       "└───────────┴──────────┴──────────┘"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import moderndive as md\n",
+    "from moderndive import t_test, prop_test, chisq_test\n",
+    "\n",
+    "# One-sample t-test\n",
+    "age = md.load_age_at_marriage()\n",
+    "t_test(age, response=\"age\", mu=23)\n",
+    "# → statistic, t_df, p_value, alternative, estimate, lower_ci, upper_ci\n",
+    "\n",
+    "# Two-sample (Welch) t-test\n",
+    "movies = md.load_movies_sample()\n",
+    "t_test(movies, formula=\"rating ~ genre\", order=(\"Action\", \"Romance\"))\n",
+    "\n",
+    "# Two-proportion z-test\n",
+    "yawn = md.load_mythbusters_yawn()\n",
+    "prop_test(yawn, formula=\"yawn ~ group\", success=\"yes\", order=(\"seed\", \"control\"))\n",
+    "\n",
+    "# Chi-squared test of independence\n",
+    "chisq_test(yawn, formula=\"yawn ~ group\")\n",
+    "# → statistic, chisq_df, p_value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca3ea1c8",
+   "metadata": {},
+   "source": [
+    "`t_stat` and `chisq_stat` return just the test statistic if that's all you need.\n",
+    "\n",
+    "## Theoretical distributions with `assume()`\n",
+    "\n",
+    "`assume()` defines a theoretical sampling distribution you can visualize and use\n",
+    "for p-values without simulating:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "24220223",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<plotnine.ggplot.ggplot object at 0x1391fe060>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {
+      "image/png": {
+       "height": 480,
+       "width": 640
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from moderndive import assume\n",
+    "\n",
+    "# t-distribution with 10 degrees of freedom\n",
+    "t_dist = assume(\"t\", df=10)\n",
+    "t_dist.get_p_value(2.0, direction=\"right\")     # one-sided p-value\n",
+    "\n",
+    "# plotly by default; engine=\"plotnine\" for ggplot output\n",
+    "t_dist.visualize()\n",
+    "t_dist.visualize(engine=\"plotnine\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7bd7c71",
+   "metadata": {},
+   "source": [
+    "Supported distributions: `\"t\"`, `\"z\"`, `\"F\"` (pass `df=(df1, df2)`), and\n",
+    "`\"Chisq\"`.\n",
+    "\n",
+    "## Overlaying theory on a simulation\n",
+    "\n",
+    "`visualize(..., method=...)` can show the simulation histogram, the theoretical\n",
+    "curve, or both:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "565a48f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
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Distribution\"},\"xaxis\":{\"title\":{\"text\":\"mean\"}},\"yaxis\":{\"title\":{\"text\":\"density\"}},\"bargap\":0.02,\"showlegend\":false},                        {\"responsive\": true}                    )                };            </script>        </div>"
+      ],
+      "text/plain": [
+       "Figure({\n",
+       "    'data': [{'line': {'color': '#d62728'},\n",
+       "              'mode': 'lines',\n",
+       "              'type': 'scatter',\n",
+       "              'x': {'bdata': ('YOHEULlDN0CznptF/0M3QAdccjpFRD' ... 'gBNrA3QN44n/Z7sDdAMfZ168GwN0A='),\n",
+       "                    'dtype': 'f8'},\n",
+       "              'y': {'bdata': ('+AOSpGyltz9ZuYZi7OK4P3sG9QgCL7' ... '2UyF5dP8AepGpxWVs/WUE+X7B1WT8='),\n",
+       "                    'dtype': 'f8'}}],\n",
+       "    'layout': {'bargap': 0.02,\n",
+       "               'showlegend': False,\n",
+       "               'template': '...',\n",
+       "               'title': {'text': 'Theoretical Distribution'},\n",
+       "               'xaxis': {'title': {'text': 'mean'}},\n",
+       "               'yaxis': {'title': {'text': 'density'}}}\n",
+       "})"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from moderndive import specify, visualize\n",
+    "\n",
+    "boot = (\n",
+    "    specify(age, response=\"age\")\n",
+    "    .generate(reps=1000, type=\"bootstrap\", seed=1)\n",
+    "    .calculate(stat=\"mean\")\n",
+    ")\n",
+    "\n",
+    "visualize(boot, method=\"both\")          # histogram + normal-approximation curve\n",
+    "visualize(boot, method=\"theoretical\")   # just the curve"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2190be2c",
+   "metadata": {},
+   "source": [
+    "## Population standard deviation\n",
+    "\n",
+    "A small helper that divides by `n` (not `n − 1`):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "48c35537",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.4142135623730951"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from moderndive import pop_sd\n",
+    "\n",
+    "pop_sd([1, 2, 3, 4, 5])   # → 1.414…"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "text_representation": {
+    "extension": ".md",
+    "format_name": "myst"
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.3"
+  },
+  "source_map": [
+   11,
+   17,
+   27,
+   47,
+   56,
+   66,
+   76,
+   87,
+   93
+  ]
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/doc/_build/jupyter_execute/index.ipynb b/doc/_build/jupyter_execute/index.ipynb
new file mode 100644
index 0000000..54812e3
--- /dev/null
+++ b/doc/_build/jupyter_execute/index.ipynb
@@ -0,0 +1,275 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "75e555df",
+   "metadata": {
+    "tags": [
+     "remove-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.use(\"Agg\")\n",
+    "import plotly.io as pio\n",
+    "pio.renderers.default = \"png\"  # render plotly figures as static images in the docs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc4f7b71",
+   "metadata": {},
+   "source": [
+    "# moderndive (Python)\n",
+    "\n",
+    "The Python companion package for **ModernDive: Statistical Inference via Data\n",
+    "Science** — a faithful port of the R [`moderndive`](https://moderndive.github.io/moderndive/)\n",
+    "and [`infer`](https://infer.tidymodels.org) packages to a modern Python\n",
+    "data-science stack ([polars](https://pola.rs), [plotly](https://plotly.com/python/),\n",
+    "[plotnine](https://plotnine.org), [statsmodels](https://www.statsmodels.org)).\n",
+    "\n",
+    "If you teach or learn statistical inference the *tidy* way — `specify` →\n",
+    "`hypothesize` → `generate` → `calculate` → `visualize` — this package gives you\n",
+    "the same grammar in Python, plus regression helpers and the book's datasets.\n",
+    "\n",
+    "## Why moderndive?\n",
+    "\n",
+    "- **A tidy inference grammar.** Bootstrap confidence intervals and\n",
+    "  permutation/​simulation hypothesis tests read like sentences, mirroring R `infer`.\n",
+    "- **Regression helpers** that return tidy tables: `get_regression_table`,\n",
+    "  `get_regression_points`, `get_regression_summaries`, `get_correlation`.\n",
+    "- **Dual-engine plots.** Every plot takes `engine=\"plotly\"` (default, interactive)\n",
+    "  or `engine=\"plotnine\"` (grammar-of-graphics) — same code, your choice of output.\n",
+    "- **58 bundled datasets** via `load_*()` loaders returning polars DataFrames.\n",
+    "- **polars-first**, works with pandas too.\n",
+    "\n",
+    "## Installation\n",
+    "\n",
+    "```bash\n",
+    "pip install moderndive\n",
+    "```\n",
+    "\n",
+    "Optional extra for saving plotly figures as static images (PNG/SVG):\n",
+    "\n",
+    "```bash\n",
+    "pip install \"moderndive[image]\"   # adds kaleido\n",
+    "```\n",
+    "\n",
+    "## 30-second example\n",
+    "\n",
+    "Are tracks more likely to be popular in *metal* than in *deep house*? Compute the\n",
+    "observed difference in \"popular\" rates, then shuffle the genre labels 1000 times\n",
+    "to build a null distribution and read off a p-value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aede4c55",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ObservedStatistic(stat='diff in props', value=0.034)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import moderndive as md\n",
+    "from moderndive import specify, observe, get_p_value, visualize, shade_p_value\n",
+    "\n",
+    "spotify = md.load_spotify_metal_deephouse()\n",
+    "\n",
+    "# Observed difference in \"popular\" proportions (metal − deep-house)\n",
+    "obs = observe(\n",
+    "    spotify, formula=\"popular_or_not ~ track_genre\", success=\"popular\",\n",
+    "    stat=\"diff in props\", order=(\"metal\", \"deep-house\"),\n",
+    ")\n",
+    "obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7d76deeb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (1, 1)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>p_value</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.075</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (1, 1)\n",
+       "┌─────────┐\n",
+       "│ p_value │\n",
+       "│ ---     │\n",
+       "│ f64     │\n",
+       "╞═════════╡\n",
+       "│ 0.075   │\n",
+       "└─────────┘"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Null distribution under \"genre doesn't matter\" (permutation)\n",
+    "null = (\n",
+    "    specify(spotify, formula=\"popular_or_not ~ track_genre\", success=\"popular\")\n",
+    "    .hypothesize(null=\"independence\")\n",
+    "    .generate(reps=1000, type=\"permute\", seed=76)\n",
+    "    .calculate(stat=\"diff in props\", order=(\"metal\", \"deep-house\"))\n",
+    ")\n",
+    "get_p_value(null, obs_stat=obs, direction=\"right\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d749da0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      ],
+      "text/plain": [
+       "Figure({\n",
+       "    'data': [{'marker': {'color': '#7f7f7f', 'line': {'color': 'white', 'width': 1}},\n",
+       "              'nbinsx': 20,\n",
+       "              'type': 'histogram',\n",
+       "              'x': {'bdata': ('QLTIdr6fmj+AFK5H4XqEvwCq8dJNYm' ... 'YxCKyMPwCq8dJNYmA/AH9qvHSTeD8='),\n",
+       "                    'dtype': 'f8'}}],\n",
+       "    'layout': {'bargap': 0.02,\n",
+       "               'shapes': [{'line': {'color': '#d62728', 'dash': 'solid', 'width': 2},\n",
+       "                           'type': 'line',\n",
+       "                           'x0': 0.03399999999999992,\n",
+       "                           'x1': 0.03399999999999992,\n",
+       "                           'xref': 'x',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y domain'},\n",
+       "                          {'fillcolor': '#d62728',\n",
+       "                           'line': {'width': 0},\n",
+       "                           'opacity': 0.3,\n",
+       "                           'type': 'rect',\n",
+       "                           'x0': 0.03399999999999992,\n",
+       "                           'x1': 0.07509999999999994,\n",
+       "                           'xref': 'x',\n",
+       "                           'y0': 0,\n",
+       "                           'y1': 1,\n",
+       "                           'yref': 'y domain'}],\n",
+       "               'showlegend': False,\n",
+       "               'template': '...',\n",
+       "               'title': {'text': 'Simulation-Based Null Distribution'},\n",
+       "               'xaxis': {'title': {'text': 'diff in props'}},\n",
+       "               'yaxis': {'title': {'text': 'count'}}}\n",
+       "})"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Visualize it (interactive plotly by default; engine=\"plotnine\" also works)\n",
+    "visualize(null) + shade_p_value(obs_stat=obs, direction=\"right\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c5df1ee",
+   "metadata": {},
+   "source": [
+    "## Where to next\n",
+    "\n",
+    "- New here? Start with {doc}`getting-started`.\n",
+    "- Coming from R? See {doc}`coming-from-r` for a function-by-function map.\n",
+    "- Browse the task guides below, or jump to the {doc}`api`.\n",
+    "\n",
+    "```{toctree}\n",
+    ":maxdepth: 1\n",
+    ":caption: Get started\n",
+    "\n",
+    "getting-started\n",
+    "coming-from-r\n",
+    "datasets\n",
+    "```\n",
+    "\n",
+    "```{toctree}\n",
+    ":maxdepth: 1\n",
+    ":caption: Guides\n",
+    "\n",
+    "guides/sampling\n",
+    "guides/confidence-intervals\n",
+    "guides/hypothesis-testing\n",
+    "guides/regression\n",
+    "guides/theory-based\n",
+    "guides/plotting\n",
+    "```\n",
+    "\n",
+    "```{toctree}\n",
+    ":maxdepth: 2\n",
+    ":caption: Reference\n",
+    "\n",
+    "api\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "text_representation": {
+    "extension": ".md",
+    "format_name": "myst"
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.3"
+  },
+  "source_map": [
+   11,
+   17,
+   60,
+   74,
+   85,
+   88
+  ]
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/doc/coming-from-r.md b/doc/coming-from-r.md
index fca9f7f..510cc69 100644
--- a/doc/coming-from-r.md
+++ b/doc/coming-from-r.md
@@ -8,9 +8,9 @@ Python. The grammar is the same; the main differences are Python method-chaining
 
 ```r
 # R
-mtcars %>%
-  specify(response = mpg) %>%
-  hypothesize(null = "point", mu = 20) %>%
+pennies %>%
+  specify(response = year) %>%
+  hypothesize(null = "point", mu = 1995) %>%
   generate(reps = 1000, type = "bootstrap") %>%
   calculate(stat = "mean")
 ```
@@ -18,8 +18,8 @@ mtcars %>%
 ```python
 # Python — verbs are methods on the returned objects
 (
-    specify(data, response="mpg")
-    .hypothesize(null="point", mu=20)
+    specify(md.load_pennies(), response="year")
+    .hypothesize(null="point", mu=1995)
     .generate(reps=1000, type="bootstrap", seed=1)
     .calculate(stat="mean")
 )
@@ -28,48 +28,40 @@ mtcars %>%
 `specify(formula="y ~ x")` works just like R's formula interface; `success=` marks
 the success level for categorical responses.
 
-## Function map
+## Most names are identical
 
-| R (`infer` / `moderndive`)        | Python (`moderndive`)                                  |
-| --------------------------------- | ------------------------------------------------------ |
-| `specify()`                       | `specify()`                                            |
-| `hypothesize()` / `hypothesise()` | `.hypothesize()` / `.hypothesise()`                    |
-| `generate()`                      | `.generate()`                                          |
-| `calculate()`                     | `.calculate()`                                         |
-| `fit()`                           | `.fit()`                                               |
-| `assume()`                        | `assume()`                                             |
-| `observe()`                       | `observe()`                                            |
-| `get_p_value()` / `get_pvalue()`  | `get_p_value()` / `get_pvalue()`                       |
-| `get_confidence_interval()` / `get_ci()` | `get_confidence_interval()` / `get_ci()`        |
-| `visualize()` / `visualise()`     | `visualize()` / `visualise()`                          |
-| `shade_p_value()` / `shade_pvalue()` | `shade_p_value()` / `shade_pvalue()`                |
-| `shade_confidence_interval()` / `shade_ci()` | `shade_confidence_interval()` / `shade_ci()` |
-| `t_test()`, `prop_test()`, `chisq_test()` | same names                                     |
-| `t_stat()`, `chisq_stat()`        | same names                                             |
-| `rep_sample_n()` / `rep_slice_sample()` | `rep_sample_n()` / `rep_slice_sample()`          |
-| `get_regression_table()`          | `get_regression_table(model)`                          |
-| `get_regression_points()`         | `get_regression_points(model)`                         |
-| `get_regression_summaries()`      | `get_regression_summaries(model)`                      |
-| `get_correlation()`               | `get_correlation(data, "y ~ x")`                       |
-| `pop_sd()`                        | `pop_sd()`                                             |
-| `geom_parallel_slopes()`          | `geom_parallel_slopes()` (plotnine) / `gg_parallel_slopes(engine=...)` |
-| `geom_categorical_model()`        | `gg_categorical_model(engine=...)`                     |
-| `tidy_summary()`                  | `tidy_summary()`                                       |
+The overwhelming majority of functions keep **the same name** (including the
+British-spelling and short-form aliases). They're called just as in R — as
+methods on the pipeline where applicable:
 
-## Key differences
+> `specify`, `hypothesize`/`hypothesise`, `generate`, `calculate`, `fit`,
+> `assume`, `observe`, `get_p_value`/`get_pvalue`,
+> `get_confidence_interval`/`get_ci`, `visualize`/`visualise`,
+> `shade_p_value`/`shade_pvalue`, `shade_confidence_interval`/`shade_ci`,
+> `t_test`, `prop_test`, `chisq_test`, `t_stat`, `chisq_stat`,
+> `rep_sample_n`/`rep_slice_sample`, `get_regression_table`,
+> `get_regression_points`, `get_regression_summaries`, `get_correlation`,
+> `pop_sd`, `tidy_summary`, `geom_parallel_slopes`.
 
-- **Pipe → methods.** `x %>% hypothesize(...)` becomes `x.hypothesize(...)`.
-- **Models.** Where R passes an `lm()` object, Python passes a fitted
-  [statsmodels](https://www.statsmodels.org) model:
-  `smf.ols("y ~ x", data=df.to_pandas()).fit()`.
-- **Plotting engine.** R returns ggplot2; Python defaults to **plotly**
-  (interactive) with `engine="plotnine"` available everywhere. Plots compose with
-  `+` in both engines.
-- **DataFrames.** Inputs/outputs are polars; pass `.to_pandas()` when a downstream
-  tool needs pandas.
-- **Reproducibility.** Pass `seed=` to `generate()` (R uses `set.seed()`).
+## What's actually different
+
+| R | Python | Why |
+| --- | --- | --- |
+| `x %>% f(...)` / `x \|> f(...)` | `x.f(...)` (method chaining) | no pipe operator in Python |
+| `geom_categorical_model()` | `gg_categorical_model(engine=...)` | renamed to match `gg_parallel_slopes` |
+| ggplot2 `geom_*` layers | plotly by default; `engine="plotnine"` for ggplot-style | dual-engine plotting |
+| `lm(y ~ x, data)` object | a fitted **statsmodels** model: `smf.ols("y ~ x", data=df.to_pandas()).fit()` | regression backend |
+| `get_correlation(df, y ~ x)` | `get_correlation(df, "y ~ x")` *or* `get_correlation(df, x="x", y="y")` | formula passed as a string |
+
+## Other things to know
+
+- **Plots compose with `+`** in both engines, and default to **plotly**
+  (interactive); pass `engine="plotnine"` anywhere for ggplot-style output.
+- **DataFrames** are polars in and out; pass `.to_pandas()` when a downstream tool
+  needs pandas.
+- **Reproducibility:** pass `seed=` to `generate()` (R uses `set.seed()`).
 
 ## Same datasets
 
 Most R `moderndive`/`infer` datasets are bundled here under the same name —
-`load_promotions()`, `load_pennies()`, `load_gss()`, etc. See {doc}`datasets`.
+`load_pennies()`, `load_promotions()`, `load_gss()`, etc. See {doc}`datasets`.
diff --git a/doc/conf.py b/doc/conf.py
index 1df0fc2..26c20ff 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -19,7 +19,7 @@
     "sphinx.ext.viewcode",
     "sphinx_autodoc_typehints",
     "sphinx_copybutton",
-    "myst_parser",
+    "myst_nb",  # MyST markdown + execution of {code-cell} blocks (supersedes myst_parser)
 ]
 
 autosummary_generate = True
@@ -28,6 +28,18 @@
 napoleon_google_docstring = True
 napoleon_numpy_docstring = True
 myst_enable_extensions = ["colon_fence", "deflist"]
+
+# --- myst-nb execution -------------------------------------------------------
+# Pages carrying a Jupytext frontmatter header run their {code-cell} blocks at
+# build time so real outputs (and plots) appear on the site. Plots are rendered
+# as static PNGs: plotnine via matplotlib, plotly via kaleido (the renderer is
+# set to "png" in each page's hidden setup cell). We tell myst-nb to prefer the
+# PNG over plotly's heavy interactive text/html so pages stay small.
+nb_execution_mode = "auto"
+nb_execution_raise_on_error = True
+nb_execution_timeout = 120
+nb_output_stderr = "remove"
+nb_mime_priority_overrides = [("html", "image/png", 0)]
 # Heavy scientific deps are installed in the RTD build; nothing to mock.
 
 intersphinx_mapping = {
@@ -38,7 +50,7 @@
 
 templates_path = ["_templates"]
 exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
-source_suffix = {".md": "markdown", ".rst": "restructuredtext"}
+source_suffix = {".rst": "restructuredtext", ".md": "myst-nb", ".ipynb": "myst-nb"}
 
 html_theme = "furo"
 html_title = f"moderndive {version}"
diff --git a/doc/datasets.md b/doc/datasets.md
index 5b8c693..ed19957 100644
--- a/doc/datasets.md
+++ b/doc/datasets.md
@@ -1,9 +1,27 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  name: python3
+---
+
+```{code-cell} python
+:tags: [remove-input]
+import matplotlib
+matplotlib.use("Agg")
+import plotly.io as pio
+pio.renderers.default = "png"
+```
+
 # Datasets
 
 `moderndive` bundles 58 datasets (the R `moderndive` + `infer` data, plus a few
 derived tables). Each loads with `load_<name>()` and returns a polars DataFrame.
 
-```python
+```{code-cell} python
 import moderndive as md
 
 md.available_datasets()          # sorted list of every dataset name
diff --git a/doc/getting-started.md b/doc/getting-started.md
index 2453b27..2a3e65f 100644
--- a/doc/getting-started.md
+++ b/doc/getting-started.md
@@ -1,3 +1,21 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  name: python3
+---
+
+```{code-cell} python
+:tags: [remove-input]
+import matplotlib
+matplotlib.use("Agg")
+import plotly.io as pio
+pio.renderers.default = "png"
+```
+
 # Getting started
 
 This page walks through a complete analysis end to end, then points you at the
@@ -16,24 +34,13 @@ accepts pandas DataFrames as input.
 
 All datasets ship with the package and load with `load_<name>()`:
 
-```python
+```{code-cell} python
 import moderndive as md
 
 promotions = md.load_promotions()
 promotions.head()
 ```
 
-```text
-shape: (5, 3)
-┌─────┬──────────┬────────┐
-│ id  ┆ decision ┆ gender │
-│ i64 ┆ str      ┆ str    │
-╞═════╪══════════╪════════╡
-│ 1   ┆ promoted ┆ male   │
-│ 2   ┆ promoted ┆ male   │
-│ …   ┆ …        ┆ …      │
-└─────┴──────────┴────────┘
-```
 
 List everything that's available with `md.available_datasets()` (58 datasets), and
 see {doc}`datasets` for a thematic tour.
@@ -43,7 +50,7 @@ see {doc}`datasets` for a thematic tour.
 `tidy_summary` gives a per-variable five-number summary (numeric) or counts
 (categorical):
 
-```python
+```{code-cell} python
 from moderndive import tidy_summary
 
 tidy_summary(md.load_almonds_sample_100(), columns=["weight"])
@@ -54,7 +61,7 @@ tidy_summary(md.load_almonds_sample_100(), columns=["weight"])
 The core grammar mirrors R `infer`. You build a pipeline and read it like a
 sentence:
 
-```python
+```{code-cell} python
 from moderndive import specify, observe, get_p_value
 
 # 1. The observed statistic
@@ -83,7 +90,7 @@ Each verb has a focused guide: {doc}`guides/sampling`,
 Plots default to **plotly** (interactive). Pass `engine="plotnine"` for
 grammar-of-graphics output. The composition syntax is identical:
 
-```python
+```{code-cell} python
 from moderndive import visualize, shade_p_value
 
 # Interactive plotly figure
@@ -98,7 +105,7 @@ overlays, and the regression-model plots.
 
 ## Regression
 
-```python
+```{code-cell} python
 import statsmodels.formula.api as smf
 from moderndive import get_regression_table
 
diff --git a/doc/guides/confidence-intervals.md b/doc/guides/confidence-intervals.md
index e02676e..b507a51 100644
--- a/doc/guides/confidence-intervals.md
+++ b/doc/guides/confidence-intervals.md
@@ -1,3 +1,21 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  name: python3
+---
+
+```{code-cell} python
+:tags: [remove-input]
+import matplotlib
+matplotlib.use("Agg")
+import plotly.io as pio
+pio.renderers.default = "png"
+```
+
 # Bootstrapping & confidence intervals
 
 Resample your data with replacement to build a **bootstrap distribution**, then
@@ -5,7 +23,7 @@ read a confidence interval off it.
 
 ## A bootstrap distribution for a mean
 
-```python
+```{code-cell} python
 import moderndive as md
 from moderndive import specify, get_confidence_interval, visualize, shade_confidence_interval
 
@@ -20,7 +38,7 @@ boot = (
 
 ## Confidence intervals — three methods
 
-```python
+```{code-cell} python
 # Percentile method (default)
 get_confidence_interval(boot, level=0.95, type="percentile")
 # shape: (1, 2) → lower_ci ≈ 3.61, upper_ci ≈ 3.75
@@ -35,13 +53,13 @@ get_confidence_interval(boot, level=0.95, type="bias-corrected", point_estimate=
 
 Distribution objects also expose the getter as a method:
 
-```python
+```{code-cell} python
 boot.get_confidence_interval(level=0.95, type="percentile")
 ```
 
 ## Visualize the interval
 
-```python
+```{code-cell} python
 ci = get_confidence_interval(boot, level=0.95, type="percentile")
 
 # plotly (default) — interactive
@@ -56,7 +74,7 @@ visualize(boot, shade_ci=ci)
 
 ## A confidence interval for a proportion
 
-```python
+```{code-cell} python
 import polars as pl
 
 mythbusters = md.load_mythbusters_yawn()
@@ -73,7 +91,7 @@ get_confidence_interval(boot_prop, level=0.95, type="percentile")
 
 Add an explanatory variable and an `order=` to compare two groups:
 
-```python
+```{code-cell} python
 boot_diff = (
     specify(mythbusters, formula="yawn ~ group", success="yes")
     .generate(reps=1000, type="bootstrap", seed=1)
diff --git a/doc/guides/hypothesis-testing.md b/doc/guides/hypothesis-testing.md
index 3a0d8c2..e57cdcc 100644
--- a/doc/guides/hypothesis-testing.md
+++ b/doc/guides/hypothesis-testing.md
@@ -1,3 +1,21 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  name: python3
+---
+
+```{code-cell} python
+:tags: [remove-input]
+import matplotlib
+matplotlib.use("Agg")
+import plotly.io as pio
+pio.renderers.default = "png"
+```
+
 # Hypothesis testing
 
 Hypothesis tests follow the same grammar as confidence intervals, with an added
@@ -9,7 +27,7 @@ from it.
 The classic "promotions" experiment — does the (perceived) gender on a résumé
 change the promotion rate?
 
-```python
+```{code-cell} python
 import moderndive as md
 from moderndive import specify, observe, get_p_value, visualize, shade_p_value
 
@@ -34,7 +52,7 @@ get_p_value(null, obs_stat=obs, direction="right")   # ≈ 0.025
 
 ## Shade the p-value
 
-```python
+```{code-cell} python
 visualize(null) + shade_p_value(obs_stat=obs, direction="right")
 ```
 
@@ -45,7 +63,7 @@ The two-sided shading mirrors the observed statistic about 0.
 
 Use a `"point"` null with bootstrap resampling, supplying the hypothesized value:
 
-```python
+```{code-cell} python
 age = md.load_age_at_marriage()
 
 obs_t = observe(age, response="age", stat="t", null="point", mu=23)
@@ -61,7 +79,7 @@ get_p_value(null_t, obs_stat=obs_t, direction="two-sided")
 
 For a one-proportion test you can also *simulate* draws directly:
 
-```python
+```{code-cell} python
 import polars as pl
 
 coins = pl.DataFrame({"flip": ["heads"] * 30 + ["tails"] * 70})
diff --git a/doc/guides/plotting.md b/doc/guides/plotting.md
index 1d5cf94..2492c09 100644
--- a/doc/guides/plotting.md
+++ b/doc/guides/plotting.md
@@ -1,3 +1,21 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  name: python3
+---
+
+```{code-cell} python
+:tags: [remove-input]
+import matplotlib
+matplotlib.use("Agg")
+import plotly.io as pio
+pio.renderers.default = "png"
+```
+
 # Plotting: plotly & plotnine
 
 Every plotting function in `moderndive` takes an `engine=` argument:
@@ -6,16 +24,15 @@ Every plotting function in `moderndive` takes an `engine=` argument:
 - `engine="plotnine"` — grammar-of-graphics figures (`plotnine.ggplot`).
 
 The composition syntax is identical across engines, so you can switch a whole
-analysis by changing one argument.
-
-## visualize() returns an InferPlot
+analysis by changing one argument. (In this documentation, plotly figures are
+shown as static images; in a notebook they're fully interactive.)
 
-`visualize()` returns a small `InferPlot` wrapper that you compose with shading via
-`+` in **both** engines:
-
-```python
+```{code-cell} python
 import moderndive as md
-from moderndive import specify, get_confidence_interval, visualize, shade_confidence_interval
+from moderndive import (
+    specify, observe, get_confidence_interval,
+    visualize, shade_confidence_interval, shade_p_value,
+)
 
 boot = (
     specify(md.load_almonds_sample_100(), response="weight")
@@ -23,51 +40,54 @@ boot = (
     .calculate(stat="mean")
 )
 ci = get_confidence_interval(boot, level=0.95, type="percentile")
-
-p = visualize(boot) + shade_confidence_interval(ci)   # plotly InferPlot
-p.figure          # the underlying plotly Figure
-p.show()          # display it
+ci
 ```
 
-For the plotnine engine, the raw `ggplot` is available via `.gg`:
+## visualize() returns an InferPlot
 
-```python
-g = visualize(boot, engine="plotnine") + shade_confidence_interval(ci)
-g.gg              # the underlying plotnine ggplot
+`visualize()` returns a small `InferPlot` wrapper that you compose with shading via
+`+` in **both** engines. The underlying figure is on `.figure` (and, for plotnine,
+the raw `ggplot` on `.gg`).
+
+```{code-cell} python
+visualize(boot) + shade_confidence_interval(ci)
+```
+
+```{code-cell} python
+# the same plot via the plotnine engine
+visualize(boot, engine="plotnine") + shade_confidence_interval(ci)
 ```
 
 ### Keyword form
 
 If you'd rather not use `+`, pass the shading inline (handy for plotly):
 
-```python
+```{code-cell} python
 visualize(boot, shade_ci=ci)
-visualize(null, shade_pvalue={"obs_stat": obs, "direction": "right"})
 ```
 
 ## Shading p-values
 
-```python
-from moderndive import observe, shade_p_value
-
-promotions = md.load_promotions()
-obs = observe(promotions, formula="decision ~ gender", success="promoted",
-              stat="diff in props", order=("male", "female"))
+```{code-cell} python
+spotify = md.load_spotify_metal_deephouse()
+obs = observe(spotify, formula="popular_or_not ~ track_genre", success="popular",
+              stat="diff in props", order=("metal", "deep-house"))
 null = (
-    specify(promotions, formula="decision ~ gender", success="promoted")
+    specify(spotify, formula="popular_or_not ~ track_genre", success="popular")
     .hypothesize(null="independence")
-    .generate(reps=1000, type="permute", seed=42)
-    .calculate(stat="diff in props", order=("male", "female"))
+    .generate(reps=1000, type="permute", seed=76)
+    .calculate(stat="diff in props", order=("metal", "deep-house"))
 )
-
 visualize(null) + shade_p_value(obs_stat=obs, direction="right")
 ```
 
 ## Simulation vs. theory overlays
 
-```python
-visualize(boot, method="simulation")    # histogram (default)
+```{code-cell} python
 visualize(boot, method="theoretical")   # normal-approximation curve
+```
+
+```{code-cell} python
 visualize(boot, method="both")          # histogram + curve overlaid
 ```
 
@@ -75,24 +95,35 @@ visualize(boot, method="both")          # histogram + curve overlaid
 
 `visualize_fit` shows one panel per term, and shading is **per-facet**:
 
-```python
+```{code-cell} python
 from moderndive.infer.viz import visualize_fit
 
-f = "price ~ living_area + bedrooms"
 houses = md.load_saratoga_houses()
-boot_fit = specify(houses, formula=f).generate(reps=1000, type="bootstrap", seed=1).fit()
-
+boot_fit = (
+    specify(houses, formula="price ~ living_area + bedrooms")
+    .generate(reps=1000, type="bootstrap", seed=1)
+    .fit()
+)
 visualize_fit(boot_fit) + shade_confidence_interval(boot_fit.get_confidence_interval())
 ```
 
 ## Model plots
 
-```python
-from moderndive import gg_parallel_slopes, gg_categorical_model, pairplot
+```{code-cell} python
+from moderndive import gg_parallel_slopes
 
 evals = md.load_evals()
 gg_parallel_slopes(evals, response="score", explanatory="age", by="gender")
+```
+
+```{code-cell} python
+from moderndive import gg_categorical_model
+
 gg_categorical_model(evals, response="score", explanatory="rank")
+```
+
+```{code-cell} python
+from moderndive import pairplot
 
 # Scatterplot matrix (~ GGally::ggpairs)
 pairplot(md.load_coffee_quality(), columns=["total_cup_points", "aroma", "flavor"])
diff --git a/doc/guides/regression.md b/doc/guides/regression.md
index b89f2b1..3971971 100644
--- a/doc/guides/regression.md
+++ b/doc/guides/regression.md
@@ -1,3 +1,21 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  name: python3
+---
+
+```{code-cell} python
+:tags: [remove-input]
+import matplotlib
+matplotlib.use("Agg")
+import plotly.io as pio
+pio.renderers.default = "png"
+```
+
 # Regression
 
 The regression helpers turn a fitted [statsmodels](https://www.statsmodels.org)
@@ -6,7 +24,7 @@ model into tidy polars tables — the analog of R `moderndive`'s
 
 Fit models with statsmodels' formula API, then tidy them:
 
-```python
+```{code-cell} python
 import statsmodels.formula.api as smf
 import moderndive as md
 from moderndive import (
@@ -19,40 +37,30 @@ model = smf.ols("price ~ living_area + bedrooms", data=houses.to_pandas()).fit()
 
 ## Coefficient table (with confidence intervals)
 
-```python
+```{code-cell} python
 get_regression_table(model)
 ```
 
-```text
-shape: (3, 7)
-┌─────────────┬───────────┬───────────┬───────────┬─────────┬────────────┬───────────┐
-│ term        ┆ estimate  ┆ std_error ┆ statistic ┆ p_value ┆ lower_ci   ┆ upper_ci  │
-╞═════════════╪═══════════╪═══════════╪═══════════╪═════════╪════════════╪═══════════╡
-│ intercept   ┆ 20986.094 ┆ 6816.251  ┆ 3.079     ┆ 0.002   ┆ 7611.128   ┆ 34361.06  │
-│ living_area ┆ 93.842    ┆ 3.109     ┆ 30.183    ┆ 0.0     ┆ 87.741     ┆ 99.943    │
-│ bedrooms    ┆ -7483.095 ┆ 2783.531  ┆ -2.688    ┆ 0.007   ┆ -12944.988 ┆ -2021.203 │
-└─────────────┴───────────┴───────────┴───────────┴─────────┴────────────┴───────────┘
-```
 
 Change the confidence level with `conf_level=` (e.g. `0.99`).
 
 ## Fitted values & residuals
 
-```python
+```{code-cell} python
 get_regression_points(model).head()
 # columns: ID, price, living_area, bedrooms, price_hat, residual
 ```
 
 ## Model-fit summaries
 
-```python
+```{code-cell} python
 get_regression_summaries(model)
 # r_squared, adj_r_squared, mse, rmse, sigma, statistic (F), p_value, df, nobs
 ```
 
 ## Correlation
 
-```python
+```{code-cell} python
 get_correlation(houses, "price ~ living_area")   # ≈ 0.759
 # or: get_correlation(houses, x="living_area", y="price")
 ```
@@ -61,7 +69,7 @@ get_correlation(houses, "price ~ living_area")   # ≈ 0.759
 
 Two ports of R `moderndive`'s ggplot helpers, both dual-engine:
 
-```python
+```{code-cell} python
 from moderndive import gg_parallel_slopes, gg_categorical_model
 
 evals = md.load_evals()
@@ -84,7 +92,7 @@ For the plotnine engine you can also drop the parallel-slopes lines onto your ow
 distribution per coefficient. Pair it with `visualize_fit` and **per-facet**
 shading:
 
-```python
+```{code-cell} python
 from moderndive import specify
 from moderndive.infer.viz import visualize_fit
 from moderndive import shade_confidence_interval, shade_p_value
diff --git a/doc/guides/sampling.md b/doc/guides/sampling.md
index cb7a7bc..6720779 100644
--- a/doc/guides/sampling.md
+++ b/doc/guides/sampling.md
@@ -1,3 +1,21 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  name: python3
+---
+
+```{code-cell} python
+:tags: [remove-input]
+import matplotlib
+matplotlib.use("Agg")
+import plotly.io as pio
+pio.renderers.default = "png"
+```
+
 # Sampling
 
 Sampling activities (the "bowl" of balls, tactile samples) are how ModernDive
@@ -7,7 +25,7 @@ and stacks them with a `replicate` column — the analog of R
 
 ## The bowl
 
-```python
+```{code-cell} python
 import moderndive as md
 import polars as pl
 
@@ -17,7 +35,7 @@ bowl.head()
 
 ## One virtual sample
 
-```python
+```{code-cell} python
 from moderndive import rep_slice_sample
 
 sample = rep_slice_sample(bowl, n=50, seed=1)
@@ -29,7 +47,7 @@ sample.select((pl.col("color") == "red").mean().alias("prop_red"))
 
 Take 1000 samples of size 50 and compute the proportion red in each:
 
-```python
+```{code-cell} python
 samples = rep_slice_sample(bowl, n=50, reps=1000, seed=1)
 
 prop_red = (
@@ -49,7 +67,7 @@ one with the inference pipeline instead).
 `rep_slice_sample` samples **without** replacement by default (like dealing from a
 deck). Pass `replace=True` for bootstrap-style resampling:
 
-```python
+```{code-cell} python
 rep_slice_sample(bowl, n=50, reps=1000, replace=True, seed=1)
 ```
 
@@ -57,7 +75,7 @@ rep_slice_sample(bowl, n=50, reps=1000, replace=True, seed=1)
 
 The hand-collected counterparts are bundled too:
 
-```python
+```{code-cell} python
 md.load_tactile_prop_red()   # 33 groups' samples of 50 balls
 ```
 
diff --git a/doc/guides/theory-based.md b/doc/guides/theory-based.md
index 3bb59b0..51f6247 100644
--- a/doc/guides/theory-based.md
+++ b/doc/guides/theory-based.md
@@ -1,3 +1,21 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  name: python3
+---
+
+```{code-cell} python
+:tags: [remove-input]
+import matplotlib
+matplotlib.use("Agg")
+import plotly.io as pio
+pio.renderers.default = "png"
+```
+
 # Theory-based inference
 
 Alongside the simulation grammar, `moderndive` ships tidy wrappers for the
@@ -6,7 +24,7 @@ mirroring R `infer`'s `t_test`, `prop_test`, `chisq_test`, and `assume()`.
 
 ## One-line tests
 
-```python
+```{code-cell} python
 import moderndive as md
 from moderndive import t_test, prop_test, chisq_test
 
@@ -35,7 +53,7 @@ chisq_test(yawn, formula="yawn ~ group")
 `assume()` defines a theoretical sampling distribution you can visualize and use
 for p-values without simulating:
 
-```python
+```{code-cell} python
 from moderndive import assume
 
 # t-distribution with 10 degrees of freedom
@@ -55,7 +73,7 @@ Supported distributions: `"t"`, `"z"`, `"F"` (pass `df=(df1, df2)`), and
 `visualize(..., method=...)` can show the simulation histogram, the theoretical
 curve, or both:
 
-```python
+```{code-cell} python
 from moderndive import specify, visualize
 
 boot = (
@@ -72,7 +90,7 @@ visualize(boot, method="theoretical")   # just the curve
 
 A small helper that divides by `n` (not `n − 1`):
 
-```python
+```{code-cell} python
 from moderndive import pop_sd
 
 pop_sd([1, 2, 3, 4, 5])   # → 1.414…
diff --git a/doc/index.md b/doc/index.md
index 516de35..dda2806 100644
--- a/doc/index.md
+++ b/doc/index.md
@@ -1,3 +1,21 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+kernelspec:
+  display_name: Python 3
+  name: python3
+---
+
+```{code-cell} python
+:tags: [remove-input]
+import matplotlib
+matplotlib.use("Agg")
+import plotly.io as pio
+pio.renderers.default = "png"  # render plotly figures as static images in the docs
+```
+
 # moderndive (Python)
 
 The Python companion package for **ModernDive: Statistical Inference via Data
@@ -35,31 +53,36 @@ pip install "moderndive[image]"   # adds kaleido
 
 ## 30-second example
 
-Did promotion decisions depend on the applicant's (perceived) gender? Shuffle the
-labels 1000 times to build a null distribution and read off a p-value:
+Are tracks more likely to be popular in *metal* than in *deep house*? Compute the
+observed difference in "popular" rates, then shuffle the genre labels 1000 times
+to build a null distribution and read off a p-value.
 
-```python
+```{code-cell} python
 import moderndive as md
 from moderndive import specify, observe, get_p_value, visualize, shade_p_value
 
-promotions = md.load_promotions()
+spotify = md.load_spotify_metal_deephouse()
 
-# Observed difference in promotion rates (male − female)
+# Observed difference in "popular" proportions (metal − deep-house)
 obs = observe(
-    promotions, formula="decision ~ gender", success="promoted",
-    stat="diff in props", order=("male", "female"),
+    spotify, formula="popular_or_not ~ track_genre", success="popular",
+    stat="diff in props", order=("metal", "deep-house"),
 )
+obs
+```
 
-# Null distribution under "gender doesn't matter" (permutation)
+```{code-cell} python
+# Null distribution under "genre doesn't matter" (permutation)
 null = (
-    specify(promotions, formula="decision ~ gender", success="promoted")
+    specify(spotify, formula="popular_or_not ~ track_genre", success="popular")
     .hypothesize(null="independence")
-    .generate(reps=1000, type="permute", seed=42)
-    .calculate(stat="diff in props", order=("male", "female"))
+    .generate(reps=1000, type="permute", seed=76)
+    .calculate(stat="diff in props", order=("metal", "deep-house"))
 )
+get_p_value(null, obs_stat=obs, direction="right")
+```
 
-get_p_value(null, obs_stat=obs, direction="right")   # ≈ 0.025
-
+```{code-cell} python
 # Visualize it (interactive plotly by default; engine="plotnine" also works)
 visualize(null) + shade_p_value(obs_stat=obs, direction="right")
 ```
diff --git a/pyproject.toml b/pyproject.toml
index 7c0fcad..a888df5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -58,7 +58,9 @@ image = ["kaleido>=0.2"]
 doc = [
     "sphinx>=7.0",
     "furo>=2024.1",
-    "myst-parser>=3.0",
+    "myst-nb>=1.1",            # MyST markdown + executes {code-cell} blocks
+    "ipykernel>=6.29",         # kernel myst-nb runs the cells in
+    "kaleido>=0.2",            # static PNG export of plotly figures for the docs
     "sphinx-autodoc-typehints>=2.0",
     "sphinx-copybutton>=0.5",
 ]

From 546926a86a3ed16fa1340658347177307ad67ab8 Mon Sep 17 00:00:00 2001
From: Chester Ismay <chester.ismay@gmail.com>
Date: Fri, 19 Jun 2026 08:25:18 -0700
Subject: [PATCH 4/9] feat: df.specify() accessor and geom_categorical_model
 alias

- Attach a `.specify()` method to polars and pandas DataFrames so you can write
  `md.load_pennies().specify(response="year")`, mirroring R's `df %>% specify(...)`.
  Registered on import; never overwrites an existing attribute.
- Add `geom_categorical_model` as an alias of `gg_categorical_model` so both R
  names are available and do the same thing.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017CTL1QSTg1DmDUpqYuPEog
---
 moderndive/__init__.py         |  2 ++
 moderndive/infer/__init__.py   |  5 ++++
 moderndive/infer/core.py       | 27 ++++++++++++++++++++
 moderndive/plots/__init__.py   |  8 +++++-
 moderndive/plots/models.py     |  4 +++
 tests/test_parity_additions.py | 45 ++++++++++++++++++++++++++++++++++
 6 files changed, 90 insertions(+), 1 deletion(-)

diff --git a/moderndive/__init__.py b/moderndive/__init__.py
index 106e511..14684c8 100644
--- a/moderndive/__init__.py
+++ b/moderndive/__init__.py
@@ -49,6 +49,7 @@
     tidy_summary,
 )
 from .plots import (
+    geom_categorical_model,
     geom_parallel_slopes,
     gg_categorical_model,
     gg_parallel_slopes,
@@ -87,6 +88,7 @@
     "gg_parallel_slopes",
     "geom_parallel_slopes",
     "gg_categorical_model",
+    "geom_categorical_model",
     "rep_sample_n",
     "rep_slice_sample",
     "theory",
diff --git a/moderndive/infer/__init__.py b/moderndive/infer/__init__.py
index 49f9bd7..b4a29e3 100644
--- a/moderndive/infer/__init__.py
+++ b/moderndive/infer/__init__.py
@@ -16,6 +16,7 @@
     ObservedStatistic,
     Specification,
     observe,
+    register_dataframe_accessor,
     specify,
 )
 from .intervals import get_confidence_interval
@@ -24,6 +25,10 @@
 from .viz import shade_confidence_interval, shade_p_value, visualize
 from .wrappers import chisq_stat, chisq_test, prop_test, t_stat, t_test
 
+# Attach `.specify()` to polars/pandas DataFrames so `df.specify(...)` works,
+# mirroring R's `df %>% specify(...)`.
+register_dataframe_accessor()
+
 # infer-parity aliases (British spellings + short forms).
 # (`hypothesise`/`visualise` also exist as methods on Specification/Distribution.)
 visualise = visualize
diff --git a/moderndive/infer/core.py b/moderndive/infer/core.py
index 619cd40..94c7e16 100644
--- a/moderndive/infer/core.py
+++ b/moderndive/infer/core.py
@@ -499,3 +499,30 @@ def observe(
     if null is not None:
         return spec.hypothesize(null=null, mu=mu, p=p, sigma=sigma).calculate(stat, order=order)
     return spec.calculate(stat, order=order, mu=mu, p=p, sigma=sigma)
+
+
+def _dataframe_specify(self, *, response=None, explanatory=None, formula=None, success=None):
+    """Start an inference pipeline from this DataFrame (see :func:`specify`)."""
+    return specify(
+        self, response=response, explanatory=explanatory, formula=formula, success=success
+    )
+
+
+def register_dataframe_accessor() -> None:
+    """Attach a ``.specify()`` method to polars and pandas DataFrames.
+
+    Lets you write ``df.specify(response="y")`` — mirroring the R
+    ``df %>% specify(...)`` flow — instead of ``specify(df, response="y")``.
+    Called once on import; skips a frame library that is not installed and never
+    overwrites an existing ``specify`` attribute.
+    """
+    import polars as pl
+
+    if not hasattr(pl.DataFrame, "specify"):
+        pl.DataFrame.specify = _dataframe_specify
+    try:
+        import pandas as pd
+    except ImportError:  # pragma: no cover - pandas is a dependency, always present
+        return
+    if not hasattr(pd.DataFrame, "specify"):
+        pd.DataFrame.specify = _dataframe_specify
diff --git a/moderndive/plots/__init__.py b/moderndive/plots/__init__.py
index 4a8799f..ac006be 100644
--- a/moderndive/plots/__init__.py
+++ b/moderndive/plots/__init__.py
@@ -12,13 +12,19 @@
 
 import polars as pl
 
-from .models import geom_parallel_slopes, gg_categorical_model, gg_parallel_slopes
+from .models import (
+    geom_categorical_model,
+    geom_parallel_slopes,
+    gg_categorical_model,
+    gg_parallel_slopes,
+)
 
 __all__ = [
     "pairplot",
     "gg_parallel_slopes",
     "geom_parallel_slopes",
     "gg_categorical_model",
+    "geom_categorical_model",
 ]
 
 
diff --git a/moderndive/plots/models.py b/moderndive/plots/models.py
index c4ac2d9..b682ddf 100644
--- a/moderndive/plots/models.py
+++ b/moderndive/plots/models.py
@@ -164,3 +164,7 @@ def gg_categorical_model(data, response: str, explanatory: str, *, engine: str =
         + labs(x=explanatory, y=response, title="Categorical model")
         + theme_light()
     )
+
+
+# R-parity alias: R's helper is named geom_categorical_model(); expose both names.
+geom_categorical_model = gg_categorical_model
diff --git a/tests/test_parity_additions.py b/tests/test_parity_additions.py
index 2c02904..f344aa0 100644
--- a/tests/test_parity_additions.py
+++ b/tests/test_parity_additions.py
@@ -133,6 +133,51 @@ def test_pairplot_plotly_engine_and_bad_engine():
         md.pairplot(coffee, engine="bogus")
 
 
+def test_geom_categorical_model_is_alias():
+    ev = md.load_evals()
+    assert md.geom_categorical_model is md.gg_categorical_model
+    assert isinstance(md.geom_categorical_model(ev, "score", "rank", engine="plotly"), go.Figure)
+    assert isinstance(
+        md.geom_categorical_model(ev, "score", "rank", engine="plotnine"), ggplot
+    )
+
+
+# ============================ DataFrame .specify() accessor ===============
+
+
+def test_dataframe_specify_accessor_polars_and_pandas():
+    import pandas as pd
+
+    # polars: df.specify(...) mirrors specify(df, ...) and chains the pipeline
+    dist = (
+        md.load_pennies()
+        .specify(response="year")
+        .generate(reps=50, type="bootstrap", seed=1)
+        .calculate(stat="mean")
+    )
+    assert dist.data.height == 50
+    # formula + success via the method
+    obs = (
+        md.load_mythbusters_yawn()
+        .specify(formula="yawn ~ group", success="yes")
+        .calculate(stat="diff in props", order=("seed", "control"))
+    )
+    assert isinstance(float(obs), float)
+    # pandas DataFrames gain the same method
+    pdf = pd.DataFrame({"x": [1.0, 2, 3, 4]})
+    assert float(pdf.specify(response="x").calculate(stat="mean")) == pytest.approx(2.5)
+
+
+def test_register_dataframe_accessor_is_idempotent():
+    import polars as pl
+
+    from moderndive.infer.core import register_dataframe_accessor
+
+    before = pl.DataFrame.specify
+    register_dataframe_accessor()  # second call must not overwrite the method
+    assert pl.DataFrame.specify is before
+
+
 # ============================ WS4: infer gaps ============================
 
 

From c17432d21b7736bea7e2031d788aab0013a437f1 Mon Sep 17 00:00:00 2001
From: Chester Ismay <chester.ismay@gmail.com>
Date: Fri, 19 Jun 2026 08:25:18 -0700
Subject: [PATCH 5/9] ci: parity-drift detection against upstream
 moderndive/infer

Add tooling to catch when the upstream R packages change in ways the Python port
hasn't followed:

- tools/parity_probe.R dumps upstream structure (versions, exports, dataset
  dims/columns) as JSON.
- tools/check_parity.py compares it to the bundled datasets + a recorded
  manifest, reporting version bumps, added/removed exports, and missing or
  shape-changed datasets; exits non-zero on drift.
- tools/parity_manifest.json records the reviewed upstream snapshot (provenance).
- .github/workflows/parity-drift.yml runs this weekly against upstream main and
  opens/updates a GitHub issue on drift.
- tests/test_parity_manifest.py asserts bundled moderndive/infer datasets match
  the manifest dims, so a bad data regeneration fails CI.
- tools/README.md documents the data-refresh and parity processes.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017CTL1QSTg1DmDUpqYuPEog
---
 .github/workflows/parity-drift.yml |  66 +++
 tests/test_parity_manifest.py      |  37 ++
 tools/README.md                    |  53 ++
 tools/check_parity.py              | 100 ++++
 tools/parity_manifest.json         | 743 +++++++++++++++++++++++++++++
 tools/parity_probe.R               |  53 ++
 6 files changed, 1052 insertions(+)
 create mode 100644 .github/workflows/parity-drift.yml
 create mode 100644 tests/test_parity_manifest.py
 create mode 100644 tools/README.md
 create mode 100644 tools/check_parity.py
 create mode 100644 tools/parity_manifest.json
 create mode 100644 tools/parity_probe.R

diff --git a/.github/workflows/parity-drift.yml b/.github/workflows/parity-drift.yml
new file mode 100644
index 0000000..3b70623
--- /dev/null
+++ b/.github/workflows/parity-drift.yml
@@ -0,0 +1,66 @@
+name: Parity drift
+
+# Detects when the upstream R packages (moderndive, infer) change in a way the
+# Python port hasn't caught up with — a version bump, an added/removed export, or
+# a dataset whose dimensions/columns changed — and opens (or comments on) an issue.
+on:
+  schedule:
+    - cron: "0 6 * * MON"
+  workflow_dispatch:
+
+permissions:
+  contents: read
+  issues: write
+
+concurrency:
+  group: parity-drift
+  cancel-in-progress: true
+
+jobs:
+  drift:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v7
+
+      - name: Set up R
+        uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - name: Install upstream R packages (latest from GitHub)
+        run: |
+          Rscript -e 'install.packages(c("remotes", "jsonlite"))'
+          Rscript -e 'remotes::install_github(c("moderndive/moderndive", "tidymodels/infer"), upgrade = "never")'
+
+      - name: Set up uv
+        uses: astral-sh/setup-uv@v7
+        with:
+          python-version: "3.12"
+
+      - name: Install the Python package
+        run: uv sync
+
+      - name: Probe upstream
+        run: Rscript tools/parity_probe.R > upstream.json
+
+      - name: Check parity
+        id: check
+        run: |
+          set +e
+          uv run python tools/check_parity.py upstream.json | tee report.md
+          code=${PIPESTATUS[0]}
+          set -e
+          if [ "$code" -ne 0 ]; then echo "drift=1" >> "$GITHUB_OUTPUT"; else echo "drift=0" >> "$GITHUB_OUTPUT"; fi
+
+      - name: Open or update a drift issue
+        if: steps.check.outputs.drift == '1'
+        env:
+          GH_TOKEN: ${{ github.token }}
+        run: |
+          title="Upstream parity drift detected"
+          num=$(gh issue list --state open --search "$title in:title" --json number --jq '.[0].number // empty')
+          if [ -n "$num" ]; then
+            gh issue comment "$num" --body-file report.md
+          else
+            gh issue create --title "$title" --body-file report.md
+          fi
diff --git a/tests/test_parity_manifest.py b/tests/test_parity_manifest.py
new file mode 100644
index 0000000..aa8baa6
--- /dev/null
+++ b/tests/test_parity_manifest.py
@@ -0,0 +1,37 @@
+"""Guardrail: bundled moderndive/infer datasets must match the recorded upstream.
+
+`tools/parity_manifest.json` is the last-reviewed snapshot of the upstream R
+packages (see tools/README.md). If a data regeneration changes a dataset's shape
+away from that snapshot, this test fails — catching drift in normal CI without
+needing R.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import pytest
+
+import moderndive as md
+
+_MANIFEST = Path(__file__).resolve().parents[1] / "tools" / "parity_manifest.json"
+_DATASETS = json.loads(_MANIFEST.read_text())
+
+
+def _cases():
+    for pkg, info in _DATASETS.items():
+        for name, meta in info["datasets"].items():
+            yield pytest.param(name, meta["rows"], meta["cols"], id=f"{pkg}:{name}")
+
+
+@pytest.mark.parametrize("name,rows,cols", _cases())
+def test_dataset_matches_upstream_dims(name, rows, cols):
+    df = md.load_dataset(name)
+    assert (df.height, df.width) == (rows, cols)
+
+
+def test_manifest_records_upstream_versions():
+    # Provenance: the manifest pins which upstream versions the data was built from.
+    assert {"moderndive", "infer"} <= set(_DATASETS)
+    assert all("version" in info for info in _DATASETS.values())
diff --git a/tools/README.md b/tools/README.md
new file mode 100644
index 0000000..854bce1
--- /dev/null
+++ b/tools/README.md
@@ -0,0 +1,53 @@
+# Maintenance tooling
+
+Scripts that keep the Python package in sync with the upstream R packages
+(`moderndive`, `infer`) and the source data packages.
+
+## Regenerating the bundled datasets
+
+The `load_*()` datasets are bundled as zstd Parquet under `moderndive/data/`,
+generated from the R packages. To refresh them after an upstream data change:
+
+```bash
+# 1. Export the datasets from R to CSV (needs R + the upstream packages installed)
+Rscript ../ModernDive_book/scripts/export_pilot_data.R   # core book datasets
+Rscript tools/export_extra_data.R                        # full-parity datasets + gss
+
+# 2. Convert the CSVs to typed Parquet under moderndive/data/
+uv run python tools/build_data.py /tmp/pilot_csv
+uv run python tools/build_extra_data.py
+
+# 3. Confirm dimensions still match upstream (see parity check below)
+```
+
+`early_january_2023_weather` is **derived** from `weather` inside `build_data.py`
+(the R dataset ships those measurement columns as all-`NA`).
+
+## Parity drift checking
+
+Upstream can add/rename datasets or functions. These tools surface that:
+
+- `parity_probe.R` — dumps the current upstream structure (versions, exported
+  objects, dataset dims + columns for `moderndive`/`infer`) as JSON.
+- `check_parity.py` — compares that JSON against (a) the Python package's bundled
+  datasets and (b) `parity_manifest.json`, reporting any drift; exits non-zero
+  when drift is found.
+- `parity_manifest.json` — the last-reviewed upstream snapshot (the provenance the
+  bundled data + API were built against).
+
+Run it locally:
+
+```bash
+Rscript tools/parity_probe.R > /tmp/upstream.json
+uv run python tools/check_parity.py /tmp/upstream.json            # report drift
+uv run python tools/check_parity.py /tmp/upstream.json --update-manifest  # accept current upstream
+```
+
+The **Parity drift** GitHub workflow (`.github/workflows/parity-drift.yml`) runs
+this weekly against the latest upstream `main` and opens/updates a GitHub issue
+when drift appears. After porting an upstream change, refresh the data (above) and
+run `--update-manifest` to record the new baseline.
+
+The test suite also asserts (via `tests/test_parity_manifest.py`) that every
+`moderndive`/`infer` dataset in the manifest is present in the package with
+matching dimensions, so a botched data regeneration fails CI immediately.
diff --git a/tools/check_parity.py b/tools/check_parity.py
new file mode 100644
index 0000000..34ac19e
--- /dev/null
+++ b/tools/check_parity.py
@@ -0,0 +1,100 @@
+"""Check the Python port for drift against the upstream R moderndive/infer packages.
+
+Workflow::
+
+    Rscript tools/parity_probe.R > upstream.json
+    python tools/check_parity.py upstream.json                 # report drift; exit 1 if any
+    python tools/check_parity.py upstream.json --update-manifest  # accept current upstream
+
+Drift is anything that means the Python port may have fallen behind upstream:
+
+- a moderndive/infer **version bump** (vs the recorded manifest);
+- **exported objects added/removed** upstream (vs the manifest);
+- a bundled **dataset missing** from the Python package, or whose **dimensions or
+  columns differ** from upstream.
+
+The manifest (``tools/parity_manifest.json``) records the last-reviewed upstream
+snapshot — i.e. the provenance the bundled data was built against. After porting
+any upstream changes, re-run with ``--update-manifest`` to accept the new state.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+import moderndive as md
+
+MANIFEST = Path(__file__).resolve().parent / "parity_manifest.json"
+
+
+def _python_datasets() -> dict[str, dict]:
+    """Dimensions + columns of every dataset bundled in the Python package."""
+    out = {}
+    for name in md.available_datasets():
+        df = md.load_dataset(name)
+        out[name] = {"rows": df.height, "cols": df.width, "columns": list(df.columns)}
+    return out
+
+
+def _diff(upstream: dict, manifest: dict, py: dict) -> list[str]:
+    lines: list[str] = []
+    py_names = set(py)
+    for pkg, info in upstream.items():
+        man = manifest.get(pkg, {})
+        if man.get("version") and man["version"] != info["version"]:
+            lines.append(f"- **{pkg}**: version changed `{man['version']}` → `{info['version']}`")
+        old_exports = set(man.get("exports", [])) - {"%>%"}
+        new_exports = set(info["exports"]) - {"%>%"}
+        if old_exports:  # only report export drift once we have a baseline
+            added = sorted(new_exports - old_exports)
+            removed = sorted(old_exports - new_exports)
+            if added:
+                lines.append(f"- **{pkg}**: new exported object(s) upstream: {added}")
+            if removed:
+                lines.append(f"- **{pkg}**: exported object(s) removed upstream: {removed}")
+        for ds, meta in info["datasets"].items():
+            r_dims = [meta["rows"], meta["cols"]]
+            if ds not in py_names:
+                lines.append(f"- dataset `{ds}` ({pkg}) is **missing** from the Python package "
+                             f"(R dims {r_dims})")
+                continue
+            if [py[ds]["rows"], py[ds]["cols"]] != r_dims:
+                lines.append(f"- dataset `{ds}`: dims differ — Python "
+                             f"{[py[ds]['rows'], py[ds]['cols']]} vs R {r_dims}")
+            missing_cols = sorted(set(meta["columns"]) - set(py[ds]["columns"]))
+            if missing_cols:
+                lines.append(f"- dataset `{ds}`: columns missing in Python: {missing_cols}")
+    return lines
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("upstream", help="JSON produced by tools/parity_probe.R")
+    ap.add_argument("--update-manifest", action="store_true",
+                    help="overwrite the manifest with the current upstream snapshot")
+    args = ap.parse_args()
+
+    upstream = json.loads(Path(args.upstream).read_text())
+
+    if args.update_manifest:
+        MANIFEST.write_text(json.dumps(upstream, indent=2, sort_keys=True) + "\n")
+        versions = ", ".join(f"{k} {v['version']}" for k, v in upstream.items())
+        print(f"Wrote {MANIFEST.name} from current upstream ({versions}).")
+        return 0
+
+    manifest = json.loads(MANIFEST.read_text()) if MANIFEST.exists() else {}
+    lines = _diff(upstream, manifest, _python_datasets())
+    if lines:
+        print("# Upstream parity drift detected\n")
+        print("\n".join(lines))
+        print("\nPort the changes into the Python package, then run "
+              "`python tools/check_parity.py <upstream.json> --update-manifest` to accept.")
+        return 1
+    print("No parity drift: the Python port matches upstream moderndive/infer.")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/tools/parity_manifest.json b/tools/parity_manifest.json
new file mode 100644
index 0000000..ca983ac
--- /dev/null
+++ b/tools/parity_manifest.json
@@ -0,0 +1,743 @@
+{
+  "infer": {
+    "datasets": {
+      "gss": {
+        "cols": 11,
+        "columns": [
+          "year",
+          "age",
+          "sex",
+          "college",
+          "partyid",
+          "hompop",
+          "hours",
+          "income",
+          "class",
+          "finrela",
+          "weight"
+        ],
+        "rows": 500
+      }
+    },
+    "exports": [
+      "%>%",
+      "assume",
+      "calculate",
+      "chisq_stat",
+      "chisq_test",
+      "conf_int",
+      "fit",
+      "fit.infer",
+      "generate",
+      "get_ci",
+      "get_confidence_interval",
+      "get_p_value",
+      "get_pvalue",
+      "ggplot_add",
+      "hypothesise",
+      "hypothesize",
+      "observe",
+      "p_value",
+      "prop_test",
+      "rep_sample_n",
+      "rep_slice_sample",
+      "shade_ci",
+      "shade_confidence_interval",
+      "shade_p_value",
+      "shade_pvalue",
+      "specify",
+      "t_stat",
+      "t_test",
+      "visualise",
+      "visualize"
+    ],
+    "version": "1.0.9"
+  },
+  "moderndive": {
+    "datasets": {
+      "DD_vs_SB": {
+        "cols": 6,
+        "columns": [
+          "county",
+          "FIPS",
+          "median_income",
+          "population",
+          "shop_type",
+          "shops"
+        ],
+        "rows": 2048
+      },
+      "MA_schools": {
+        "cols": 4,
+        "columns": [
+          "school_name",
+          "average_sat_math",
+          "perc_disadvan",
+          "size"
+        ],
+        "rows": 332
+      },
+      "alaska_flights": {
+        "cols": 19,
+        "columns": [
+          "year",
+          "month",
+          "day",
+          "dep_time",
+          "sched_dep_time",
+          "dep_delay",
+          "arr_time",
+          "sched_arr_time",
+          "arr_delay",
+          "carrier",
+          "flight",
+          "tailnum",
+          "origin",
+          "dest",
+          "air_time",
+          "distance",
+          "hour",
+          "minute",
+          "time_hour"
+        ],
+        "rows": 714
+      },
+      "almonds_bowl": {
+        "cols": 2,
+        "columns": [
+          "ID",
+          "weight"
+        ],
+        "rows": 5000
+      },
+      "almonds_sample": {
+        "cols": 3,
+        "columns": [
+          "replicate",
+          "ID",
+          "weight"
+        ],
+        "rows": 25
+      },
+      "almonds_sample_100": {
+        "cols": 3,
+        "columns": [
+          "replicate",
+          "ID",
+          "weight"
+        ],
+        "rows": 100
+      },
+      "amazon_books": {
+        "cols": 13,
+        "columns": [
+          "title",
+          "author",
+          "list_price",
+          "amazon_price",
+          "hard_paper",
+          "num_pages",
+          "publisher",
+          "pub_year",
+          "isbn_10",
+          "height",
+          "width",
+          "thick",
+          "weight_oz"
+        ],
+        "rows": 325
+      },
+      "avocados": {
+        "cols": 13,
+        "columns": [
+          "date",
+          "average_price",
+          "total_volume",
+          "small_hass_sold",
+          "large_hass_sold",
+          "xlarge_hass_sold",
+          "total_bags",
+          "small_bags",
+          "large_bags",
+          "x_large_bags",
+          "type",
+          "year",
+          "region"
+        ],
+        "rows": 18249
+      },
+      "babies": {
+        "cols": 24,
+        "columns": [
+          "id",
+          "pluralty",
+          "outcome",
+          "date",
+          "gestation",
+          "sex",
+          "wt",
+          "parity",
+          "race",
+          "age",
+          "ed",
+          "ht",
+          "wt_1",
+          "drace",
+          "dage",
+          "ded",
+          "dht",
+          "dwt",
+          "marital",
+          "inc",
+          "smoke",
+          "time",
+          "number",
+          "birthday"
+        ],
+        "rows": 1236
+      },
+      "bowl": {
+        "cols": 2,
+        "columns": [
+          "ball_ID",
+          "color"
+        ],
+        "rows": 2400
+      },
+      "bowl_sample_1": {
+        "cols": 1,
+        "columns": [
+          "color"
+        ],
+        "rows": 50
+      },
+      "bowl_samples": {
+        "cols": 5,
+        "columns": [
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+      "coffee_quality": {
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+        "columns": [
+          "country_of_origin",
+          "continent_of_origin",
+          "farm_name",
+          "lot_number",
+          "mill",
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+          "producer",
+          "in_country_partner",
+          "harvest_year",
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+          "owner",
+          "variety",
+          "processing_method",
+          "aroma",
+          "flavor",
+          "aftertaste",
+          "acidity",
+          "body",
+          "balance",
+          "uniformity",
+          "clean_cup",
+          "sweetness",
+          "overall",
+          "total_cup_points",
+          "moisture_percentage",
+          "color",
+          "expiration",
+          "certification_body"
+        ],
+        "rows": 207
+      },
+      "coffee_ratings": {
+        "cols": 43,
+        "columns": [
+          "total_cup_points",
+          "species",
+          "owner",
+          "country_of_origin",
+          "farm_name",
+          "lot_number",
+          "mill",
+          "ico_number",
+          "company",
+          "altitude",
+          "region",
+          "producer",
+          "number_of_bags",
+          "bag_weight",
+          "in_country_partner",
+          "harvest_year",
+          "grading_date",
+          "owner_1",
+          "variety",
+          "processing_method",
+          "aroma",
+          "flavor",
+          "aftertaste",
+          "acidity",
+          "body",
+          "balance",
+          "uniformity",
+          "clean_cup",
+          "sweetness",
+          "cupper_points",
+          "moisture",
+          "category_one_defects",
+          "quakers",
+          "color",
+          "category_two_defects",
+          "expiration",
+          "certification_body",
+          "certification_address",
+          "certification_contact",
+          "unit_of_measurement",
+          "altitude_low_meters",
+          "altitude_high_meters",
+          "altitude_mean_meters"
+        ],
+        "rows": 1339
+      },
+      "early_january_2023_weather": {
+        "cols": 15,
+        "columns": [
+          "origin",
+          "year",
+          "month",
+          "day",
+          "hour",
+          "temp",
+          "dewp",
+          "humid",
+          "wind_dir",
+          "wind_speed",
+          "wind_gust",
+          "precip",
+          "pressure",
+          "visib",
+          "time_hour"
+        ],
+        "rows": 360
+      },
+      "early_january_weather": {
+        "cols": 15,
+        "columns": [
+          "origin",
+          "year",
+          "month",
+          "day",
+          "hour",
+          "temp",
+          "dewp",
+          "humid",
+          "wind_dir",
+          "wind_speed",
+          "wind_gust",
+          "precip",
+          "pressure",
+          "visib",
+          "time_hour"
+        ],
+        "rows": 358
+      },
+      "envoy_flights": {
+        "cols": 19,
+        "columns": [
+          "year",
+          "month",
+          "day",
+          "dep_time",
+          "sched_dep_time",
+          "dep_delay",
+          "arr_time",
+          "sched_arr_time",
+          "arr_delay",
+          "carrier",
+          "flight",
+          "tailnum",
+          "origin",
+          "dest",
+          "air_time",
+          "distance",
+          "hour",
+          "minute",
+          "time_hour"
+        ],
+        "rows": 357
+      },
+      "ev_charging": {
+        "cols": 24,
+        "columns": [
+          "session_id",
+          "kwh_total",
+          "dollars",
+          "created",
+          "ended",
+          "start_time",
+          "end_time",
+          "charge_time_hrs",
+          "weekday",
+          "platform",
+          "distance",
+          "user_id",
+          "station_id",
+          "location_id",
+          "manager_vehicle",
+          "mon",
+          "tues",
+          "wed",
+          "thurs",
+          "fri",
+          "sat",
+          "sun",
+          "reported_zip",
+          "facility_type"
+        ],
+        "rows": 3395
+      },
+      "evals": {
+        "cols": 14,
+        "columns": [
+          "ID",
+          "prof_ID",
+          "score",
+          "age",
+          "bty_avg",
+          "gender",
+          "ethnicity",
+          "language",
+          "rank",
+          "pic_outfit",
+          "pic_color",
+          "cls_did_eval",
+          "cls_students",
+          "cls_level"
+        ],
+        "rows": 463
+      },
+      "house_prices": {
+        "cols": 21,
+        "columns": [
+          "id",
+          "date",
+          "price",
+          "bedrooms",
+          "bathrooms",
+          "sqft_living",
+          "sqft_lot",
+          "floors",
+          "waterfront",
+          "view",
+          "condition",
+          "grade",
+          "sqft_above",
+          "sqft_basement",
+          "yr_built",
+          "yr_renovated",
+          "zipcode",
+          "lat",
+          "long",
+          "sqft_living15",
+          "sqft_lot15"
+        ],
+        "rows": 21613
+      },
+      "ipf_lifts": {
+        "cols": 16,
+        "columns": [
+          "name",
+          "sex",
+          "event",
+          "equipment",
+          "age",
+          "age_class",
+          "division",
+          "bodyweight_kg",
+          "weight_class_kg",
+          "best3squat_kg",
+          "best3bench_kg",
+          "best3deadlift_kg",
+          "place",
+          "date",
+          "federation",
+          "meet_name"
+        ],
+        "rows": 41152
+      },
+      "ma_traffic_2020_vs_2019": {
+        "cols": 5,
+        "columns": [
+          "community",
+          "functional_class",
+          "change_in_speed",
+          "change_in_volume",
+          "change_in_accidents"
+        ],
+        "rows": 264
+      },
+      "mario_kart_auction": {
+        "cols": 12,
+        "columns": [
+          "id",
+          "duration",
+          "n_bids",
+          "cond",
+          "start_pr",
+          "ship_pr",
+          "total_pr",
+          "ship_sp",
+          "seller_rate",
+          "stock_photo",
+          "wheels",
+          "title"
+        ],
+        "rows": 143
+      },
+      "mass_traffic_2020": {
+        "cols": 11,
+        "columns": [
+          "site_id",
+          "county",
+          "community",
+          "rural_urban",
+          "dir",
+          "functional_class",
+          "avg_speed",
+          "total_volume",
+          "crashes",
+          "nonfatal_injuries",
+          "fatal_injuries"
+        ],
+        "rows": 874
+      },
+      "movies_sample": {
+        "cols": 4,
+        "columns": [
+          "title",
+          "year",
+          "rating",
+          "genre"
+        ],
+        "rows": 68
+      },
+      "mythbusters_yawn": {
+        "cols": 3,
+        "columns": [
+          "subj",
+          "group",
+          "yawn"
+        ],
+        "rows": 50
+      },
+      "old_faithful_2024": {
+        "cols": 6,
+        "columns": [
+          "eruption_id",
+          "date",
+          "time",
+          "waiting",
+          "webcam",
+          "duration"
+        ],
+        "rows": 114
+      },
+      "orig_pennies_sample": {
+        "cols": 2,
+        "columns": [
+          "year",
+          "age_in_2011"
+        ],
+        "rows": 40
+      },
+      "pennies": {
+        "cols": 2,
+        "columns": [
+          "year",
+          "age_in_2011"
+        ],
+        "rows": 800
+      },
+      "pennies_resamples": {
+        "cols": 3,
+        "columns": [
+          "replicate",
+          "name",
+          "year"
+        ],
+        "rows": 1750
+      },
+      "pennies_sample": {
+        "cols": 2,
+        "columns": [
+          "ID",
+          "year"
+        ],
+        "rows": 50
+      },
+      "promotions": {
+        "cols": 3,
+        "columns": [
+          "id",
+          "decision",
+          "gender"
+        ],
+        "rows": 48
+      },
+      "promotions_shuffled": {
+        "cols": 3,
+        "columns": [
+          "id",
+          "decision",
+          "gender"
+        ],
+        "rows": 48
+      },
+      "saratoga_houses": {
+        "cols": 8,
+        "columns": [
+          "price",
+          "living_area",
+          "bathrooms",
+          "bedrooms",
+          "fireplaces",
+          "lot_size",
+          "age",
+          "fireplace"
+        ],
+        "rows": 1057
+      },
+      "spotify_52_original": {
+        "cols": 6,
+        "columns": [
+          "track_id",
+          "track_genre",
+          "artists",
+          "track_name",
+          "popularity",
+          "popular_or_not"
+        ],
+        "rows": 52
+      },
+      "spotify_52_shuffled": {
+        "cols": 6,
+        "columns": [
+          "track_id",
+          "track_genre",
+          "artists",
+          "track_name",
+          "popularity",
+          "popular_or_not"
+        ],
+        "rows": 52
+      },
+      "spotify_by_genre": {
+        "cols": 21,
+        "columns": [
+          "track_id",
+          "artists",
+          "album_name",
+          "track_name",
+          "popularity",
+          "duration_ms",
+          "explicit",
+          "danceability",
+          "energy",
+          "key",
+          "loudness",
+          "mode",
+          "speechiness",
+          "acousticness",
+          "instrumentalness",
+          "liveness",
+          "valence",
+          "tempo",
+          "time_signature",
+          "track_genre",
+          "popular_or_not"
+        ],
+        "rows": 6000
+      },
+      "tactile_prop_red": {
+        "cols": 4,
+        "columns": [
+          "group",
+          "replicate",
+          "red_balls",
+          "prop_red"
+        ],
+        "rows": 33
+      },
+      "un_member_states_2024": {
+        "cols": 39,
+        "columns": [
+          "country",
+          "iso",
+          "official_state_name",
+          "continent",
+          "region",
+          "capital_city",
+          "capital_population",
+          "capital_perc_of_country",
+          "capital_data_year",
+          "gdp_per_capita",
+          "gdp_per_capita_year",
+          "summers_competed_in",
+          "summer_golds",
+          "summer_silvers",
+          "summer_bronzes",
+          "summer_total",
+          "winters_competed_in",
+          "winter_golds",
+          "winter_silvers",
+          "winter_bronzes",
+          "winter_total",
+          "combined_competed_ins",
+          "combined_golds",
+          "combined_silvers",
+          "combined_bronzes",
+          "combined_total",
+          "driving_side",
+          "obesity_rate_2024",
+          "obesity_rate_2016",
+          "has_nuclear_weapons_2024",
+          "population_2024",
+          "area_in_square_km",
+          "area_in_square_miles",
+          "population_density_in_square_km",
+          "population_density_in_square_miles",
+          "income_group_2024",
+          "life_expectancy_2022",
+          "fertility_rate_2022",
+          "hdi_2022"
+        ],
+        "rows": 193
+      }
+    },
+    "exports": [
+      "%>%",
+      "geom_categorical_model",
+      "geom_parallel_slopes",
+      "get_correlation",
+      "get_regression_points",
+      "get_regression_summaries",
+      "get_regression_table",
+      "gg_parallel_slopes",
+      "plot_3d_regression",
+      "pop_sd",
+      "rep_sample_n",
+      "rep_slice_sample",
+      "tidy_summary",
+      "View"
+    ],
+    "version": "0.8.0.9000"
+  }
+}
diff --git a/tools/parity_probe.R b/tools/parity_probe.R
new file mode 100644
index 0000000..01d9d6e
--- /dev/null
+++ b/tools/parity_probe.R
@@ -0,0 +1,53 @@
+#!/usr/bin/env Rscript
+# Dump the current structure of the upstream R packages as JSON, for parity
+# drift-checking against the Python port. Prints to stdout.
+#
+#   Rscript tools/parity_probe.R > /tmp/upstream.json
+#
+# Captures, per package: version, exported object names, and the dimensions +
+# column names of every bundled data frame.
+
+suppressMessages({
+  library(jsonlite)
+})
+
+PACKAGES <- c("moderndive", "infer")
+
+probe_pkg <- function(pkg) {
+  if (!requireNamespace(pkg, quietly = TRUE)) {
+    return(NULL)
+  }
+  suppressMessages(library(pkg, character.only = TRUE))
+  items <- tryCatch(data(package = pkg)$results[, "Item"], error = function(e) character(0))
+  # `Item` can look like "name (alias)"; keep the leading token.
+  items <- unique(sub("\\s.*$", "", items))
+  datasets <- list()
+  env <- new.env()
+  for (nm in items) {
+    ok <- tryCatch(
+      {
+        data(list = nm, package = pkg, envir = env)
+        TRUE
+      },
+      error = function(e) FALSE
+    )
+    if (!ok || !exists(nm, envir = env)) next
+    obj <- get(nm, envir = env)
+    if (is.data.frame(obj)) {
+      # I() keeps length-1 vectors as JSON arrays (auto_unbox would scalarize them).
+      datasets[[nm]] <- list(rows = nrow(obj), cols = ncol(obj), columns = I(names(obj)))
+    }
+  }
+  list(
+    version = as.character(packageVersion(pkg)),
+    exports = I(sort(getNamespaceExports(pkg))),
+    datasets = datasets
+  )
+}
+
+out <- list()
+for (pkg in PACKAGES) {
+  res <- probe_pkg(pkg)
+  if (!is.null(res)) out[[pkg]] <- res
+}
+cat(toJSON(out, auto_unbox = TRUE, pretty = TRUE))

From f877fc3dd123fd7d80071bf2a3459f9bb8842286 Mon Sep 17 00:00:00 2001
From: Chester Ismay <chester.ismay@gmail.com>
Date: Fri, 19 Jun 2026 08:25:28 -0700
Subject: [PATCH 6/9] docs: drop promotions everywhere; add custom-statistic
 example

- Remove the promotions dataset from all examples and the dataset catalog (we're
  not using it going forward): getting-started and the hypothesis-testing guide
  now use the MythBusters yawn experiment / spotify; coming-from-r uses pennies.
- Add a worked "custom test statistic" example (a callable passed to
  calculate()) to the hypothesis-testing guide, bootstrapping a custom IQR.
- Use the new df.specify() flow in examples; note geom_categorical_model as a
  same-name function in the R map.
- Rebuilt HTML.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017CTL1QSTg1DmDUpqYuPEog
---
 doc/_build/html/.doctrees/api.doctree         | Bin 3765443 -> 3766775 bytes
 .../html/.doctrees/coming-from-r.doctree      | Bin 24818 -> 23979 bytes
 doc/_build/html/.doctrees/datasets.doctree    | Bin 24389 -> 22853 bytes
 doc/_build/html/.doctrees/environment.pickle  | Bin 3507939 -> 3509556 bytes
 .../html/.doctrees/getting-started.doctree    | Bin 30097 -> 29983 bytes
 .../guides/confidence-intervals.doctree       | Bin 63512 -> 63512 bytes
 .../guides/hypothesis-testing.doctree         | Bin 64540 -> 70802 bytes
 .../html/.doctrees/guides/plotting.doctree    | Bin 356028 -> 356028 bytes
 .../html/.doctrees/guides/regression.doctree  | Bin 140614 -> 140614 bytes
 .../.doctrees/guides/theory-based.doctree     | Bin 53331 -> 53331 bytes
 doc/_build/html/.doctrees/index.doctree       | Bin 66991 -> 66991 bytes
 ...fc4936f3afde3e478c71e657c8305dcee97764.png | Bin 24332 -> 0 bytes
 ...0d46c94c46266531703754ae8cf2b7aea89916.png | Bin 29052 -> 0 bytes
 ...ba190577d8d13ed180fbd9a689d9d06129dac6.png | Bin 0 -> 35058 bytes
 .../html/_modules/moderndive/infer/core.html  |  27 ++++
 .../html/_modules/moderndive/plots.html       |   8 +-
 .../_modules/moderndive/plots/models.html     |   4 +
 doc/_build/html/_sources/coming-from-r.md.txt |   5 +-
 doc/_build/html/_sources/datasets.md.txt      |  11 +-
 .../html/_sources/getting-started.md.txt      |  16 +--
 .../_sources/guides/hypothesis-testing.md.txt |  53 +++++--
 doc/_build/html/coming-from-r.html            |  14 +-
 doc/_build/html/datasets.html                 |  13 +-
 doc/_build/html/getting-started.html          |  22 +--
 .../html/guides/hypothesis-testing.html       |  69 ++++++---
 doc/_build/html/searchindex.js                |   2 +-
 ...ba190577d8d13ed180fbd9a689d9d06129dac6.png | Bin 0 -> 35058 bytes
 doc/_build/jupyter_execute/datasets.ipynb     |  57 ++++----
 .../jupyter_execute/getting-started.ipynb     |  72 +++++-----
 .../guides/confidence-intervals.ipynb         |  30 ++--
 .../guides/hypothesis-testing.ipynb           | 135 +++++++++++++-----
 .../jupyter_execute/guides/plotting.ipynb     |  54 +++----
 .../jupyter_execute/guides/regression.ipynb   |  34 ++---
 .../jupyter_execute/guides/sampling.ipynb     |  24 ++--
 .../jupyter_execute/guides/theory-based.ipynb |  22 +--
 doc/_build/jupyter_execute/index.ipynb        |  14 +-
 doc/coming-from-r.md                          |   5 +-
 doc/datasets.md                               |  11 +-
 doc/getting-started.md                        |  16 +--
 doc/guides/hypothesis-testing.md              |  53 +++++--
 40 files changed, 476 insertions(+), 295 deletions(-)
 delete mode 100644 doc/_build/html/_images/446856f7a7a4b3ae8beb128934fc4936f3afde3e478c71e657c8305dcee97764.png
 delete mode 100644 doc/_build/html/_images/679346fce431777e0d221fb96d0d46c94c46266531703754ae8cf2b7aea89916.png
 create mode 100644 doc/_build/html/_images/a716ffc5702927ebba6a9176ddba190577d8d13ed180fbd9a689d9d06129dac6.png
 create mode 100644 doc/_build/jupyter_execute/a716ffc5702927ebba6a9176ddba190577d8d13ed180fbd9a689d9d06129dac6.png

diff --git a/doc/_build/html/.doctrees/api.doctree b/doc/_build/html/.doctrees/api.doctree
index 835f9e8b509a9508e2cc1a252b67af1400a6eb55..0e9dd98fd84ce9cf9d8f3d5e4d31ef0197185233 100644
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diff --git a/doc/_build/html/.doctrees/coming-from-r.doctree b/doc/_build/html/.doctrees/coming-from-r.doctree
index 3e3cdfa2842eb0872b74c2e82400aff569db50a8..7c55c23cd92fa782254cbfe7a10950589762c2df 100644
GIT binary patch
delta 2690
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diff --git a/doc/_build/html/.doctrees/datasets.doctree b/doc/_build/html/.doctrees/datasets.doctree
index 5c073fe6ccc70544befd26a6b452de0bf8d70b60..90a8328eee3bfe6c4e304d5d27b51ff7dfdbd249 100644
GIT binary patch
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diff --git a/doc/_build/html/.doctrees/environment.pickle b/doc/_build/html/.doctrees/environment.pickle
index 9d108034c1fc05dcfe829f8403399023dff21a19..b9f3c55ca3909000e8c33e8abd0d7166f9d631ce 100644
GIT binary patch
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diff --git a/doc/_build/html/.doctrees/getting-started.doctree b/doc/_build/html/.doctrees/getting-started.doctree
index d74c8bb0aae9a9a059caaa17c41568d6dd66b805..6357db694a1b7bc13a09dc0de2b93d41789d443e 100644
GIT binary patch
delta 1692
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delta 1780
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diff --git a/doc/_build/html/.doctrees/guides/confidence-intervals.doctree b/doc/_build/html/.doctrees/guides/confidence-intervals.doctree
index 17c26313cea7ade6d5f64a86f348f9641729c7db..e6477207bd5ceec625c1f58e67ef5fc26da2d970 100644
GIT binary patch
delta 264
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delta 264
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diff --git a/doc/_build/html/.doctrees/guides/hypothesis-testing.doctree b/doc/_build/html/.doctrees/guides/hypothesis-testing.doctree
index 1b0568732a56191b186b25d6918c02e555f81fb3..d4470f592e547cd4fe11fa8c3f997fa583a3cfd7 100644
GIT binary patch
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diff --git a/doc/_build/html/.doctrees/guides/plotting.doctree b/doc/_build/html/.doctrees/guides/plotting.doctree
index f5a1e17ba667855782b39e63f927dd90eb0fa5f1..3cf1102e0cc4a218bd91b7a82b54b7ca43147791 100644
GIT binary patch
delta 1616
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delta 1612
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diff --git a/doc/_build/html/.doctrees/guides/regression.doctree b/doc/_build/html/.doctrees/guides/regression.doctree
index 7399e41b36065a96c657f62e9187f1c4861c3854..c5c0a41c718badc5b07286713652af772135f1ed 100644
GIT binary patch
delta 293
zcmX?hisRTRjtweXRFVvg4b6>{EOm_yQj&B{%q&fGlTr<gbPdfcOj0b3k_{~lO(uJ6
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delta 293
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diff --git a/doc/_build/html/.doctrees/guides/theory-based.doctree b/doc/_build/html/.doctrees/guides/theory-based.doctree
index e5732e8153435ffeff3f52cc271d2ab10b0ec5b8..3a050c2559a3284715e575006ba1c1b97fb316be 100644
GIT binary patch
delta 295
zcmcb-fcf$Q<_(9HSdvW4j3ysfG6pj?|5NG|Q8qQSNHVlcOwlzoHc!ztNliA_wKOv@
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delta 295
zcmcb-fcf$Q<_(9HSS$_GQYRl*G6pj?|5NG|Q8r1kuuMudOw%<_Gc?yVu}n75O|nQz
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diff --git a/doc/_build/html/.doctrees/index.doctree b/doc/_build/html/.doctrees/index.doctree
index 53b2e0774a3c24f9d4b724c4debd8896d24269ce..482d91bdfa75fbbcd3a4be2df5d0a183b7628546 100644
GIT binary patch
delta 262
zcmZ4A&9c6mWy4-Cl@tquG_xd=6kRh*^CVppV`EF*q(loN-4ru36T_4g1IxrDi^&hY
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delta 262
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diff --git a/doc/_build/html/_images/446856f7a7a4b3ae8beb128934fc4936f3afde3e478c71e657c8305dcee97764.png b/doc/_build/html/_images/446856f7a7a4b3ae8beb128934fc4936f3afde3e478c71e657c8305dcee97764.png
deleted file mode 100644
index ca7796365fb52c9c69754c1fe4bca3285a60c6e9..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/doc/_build/html/_modules/moderndive/infer/core.html b/doc/_build/html/_modules/moderndive/infer/core.html
index 811d484..78b5e73 100644
--- a/doc/_build/html/_modules/moderndive/infer/core.html
+++ b/doc/_build/html/_modules/moderndive/infer/core.html
@@ -783,6 +783,33 @@ <h1>Source code for moderndive.infer.core</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">spec</span><span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="n">null</span><span class="p">,</span> <span class="n">mu</span><span class="o">=</span><span class="n">mu</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="n">sigma</span><span class="p">)</span><span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">order</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">spec</span><span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">order</span><span class="p">,</span> <span class="n">mu</span><span class="o">=</span><span class="n">mu</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="n">sigma</span><span class="p">)</span></div>
 
+
+
+<span class="k">def</span><span class="w"> </span><span class="nf">_dataframe_specify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">explanatory</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Start an inference pipeline from this DataFrame (see :func:`specify`).&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">specify</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="n">response</span><span class="p">,</span> <span class="n">explanatory</span><span class="o">=</span><span class="n">explanatory</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">formula</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="n">success</span>
+    <span class="p">)</span>
+
+
+<span class="k">def</span><span class="w"> </span><span class="nf">register_dataframe_accessor</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Attach a ``.specify()`` method to polars and pandas DataFrames.</span>
+
+<span class="sd">    Lets you write ``df.specify(response=&quot;y&quot;)`` — mirroring the R</span>
+<span class="sd">    ``df %&gt;% specify(...)`` flow — instead of ``specify(df, response=&quot;y&quot;)``.</span>
+<span class="sd">    Called once on import; skips a frame library that is not installed and never</span>
+<span class="sd">    overwrites an existing ``specify`` attribute.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span><span class="w"> </span><span class="nn">polars</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pl</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">pl</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="s2">&quot;specify&quot;</span><span class="p">):</span>
+        <span class="n">pl</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">specify</span> <span class="o">=</span> <span class="n">_dataframe_specify</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
+    <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>  <span class="c1"># pragma: no cover - pandas is a dependency, always present</span>
+        <span class="k">return</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="s2">&quot;specify&quot;</span><span class="p">):</span>
+        <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">specify</span> <span class="o">=</span> <span class="n">_dataframe_specify</span>
 </pre></div>
         </article>
       </div>
diff --git a/doc/_build/html/_modules/moderndive/plots.html b/doc/_build/html/_modules/moderndive/plots.html
index 5ab75de..93bd69e 100644
--- a/doc/_build/html/_modules/moderndive/plots.html
+++ b/doc/_build/html/_modules/moderndive/plots.html
@@ -260,13 +260,19 @@ <h1>Source code for moderndive.plots</h1><div class="highlight"><pre>
 
 <span class="kn">import</span><span class="w"> </span><span class="nn">polars</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pl</span>
 
-<span class="kn">from</span><span class="w"> </span><span class="nn">.models</span><span class="w"> </span><span class="kn">import</span> <span class="n">geom_parallel_slopes</span><span class="p">,</span> <span class="n">gg_categorical_model</span><span class="p">,</span> <span class="n">gg_parallel_slopes</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">.models</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
+    <span class="n">geom_categorical_model</span><span class="p">,</span>
+    <span class="n">geom_parallel_slopes</span><span class="p">,</span>
+    <span class="n">gg_categorical_model</span><span class="p">,</span>
+    <span class="n">gg_parallel_slopes</span><span class="p">,</span>
+<span class="p">)</span>
 
 <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
     <span class="s2">&quot;pairplot&quot;</span><span class="p">,</span>
     <span class="s2">&quot;gg_parallel_slopes&quot;</span><span class="p">,</span>
     <span class="s2">&quot;geom_parallel_slopes&quot;</span><span class="p">,</span>
     <span class="s2">&quot;gg_categorical_model&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;geom_categorical_model&quot;</span><span class="p">,</span>
 <span class="p">]</span>
 
 
diff --git a/doc/_build/html/_modules/moderndive/plots/models.html b/doc/_build/html/_modules/moderndive/plots/models.html
index 77cf29d..e1b7ae9 100644
--- a/doc/_build/html/_modules/moderndive/plots/models.html
+++ b/doc/_build/html/_modules/moderndive/plots/models.html
@@ -421,6 +421,10 @@ <h1>Source code for moderndive.plots.models</h1><div class="highlight"><pre>
         <span class="o">+</span> <span class="n">theme_light</span><span class="p">()</span>
     <span class="p">)</span></div>
 
+
+
+<span class="c1"># R-parity alias: R&#39;s helper is named geom_categorical_model(); expose both names.</span>
+<span class="n">geom_categorical_model</span> <span class="o">=</span> <span class="n">gg_categorical_model</span>
 </pre></div>
         </article>
       </div>
diff --git a/doc/_build/html/_sources/coming-from-r.md.txt b/doc/_build/html/_sources/coming-from-r.md.txt
index 510cc69..6f3b032 100644
--- a/doc/_build/html/_sources/coming-from-r.md.txt
+++ b/doc/_build/html/_sources/coming-from-r.md.txt
@@ -41,14 +41,13 @@ methods on the pipeline where applicable:
 > `t_test`, `prop_test`, `chisq_test`, `t_stat`, `chisq_stat`,
 > `rep_sample_n`/`rep_slice_sample`, `get_regression_table`,
 > `get_regression_points`, `get_regression_summaries`, `get_correlation`,
-> `pop_sd`, `tidy_summary`, `geom_parallel_slopes`.
+> `pop_sd`, `tidy_summary`, `geom_parallel_slopes`, `geom_categorical_model`.
 
 ## What's actually different
 
 | R | Python | Why |
 | --- | --- | --- |
 | `x %>% f(...)` / `x \|> f(...)` | `x.f(...)` (method chaining) | no pipe operator in Python |
-| `geom_categorical_model()` | `gg_categorical_model(engine=...)` | renamed to match `gg_parallel_slopes` |
 | ggplot2 `geom_*` layers | plotly by default; `engine="plotnine"` for ggplot-style | dual-engine plotting |
 | `lm(y ~ x, data)` object | a fitted **statsmodels** model: `smf.ols("y ~ x", data=df.to_pandas()).fit()` | regression backend |
 | `get_correlation(df, y ~ x)` | `get_correlation(df, "y ~ x")` *or* `get_correlation(df, x="x", y="y")` | formula passed as a string |
@@ -64,4 +63,4 @@ methods on the pipeline where applicable:
 ## Same datasets
 
 Most R `moderndive`/`infer` datasets are bundled here under the same name —
-`load_pennies()`, `load_promotions()`, `load_gss()`, etc. See {doc}`datasets`.
+`load_pennies()`, `load_mythbusters_yawn()`, `load_gss()`, etc. See {doc}`datasets`.
diff --git a/doc/_build/html/_sources/datasets.md.txt b/doc/_build/html/_sources/datasets.md.txt
index ed19957..8137600 100644
--- a/doc/_build/html/_sources/datasets.md.txt
+++ b/doc/_build/html/_sources/datasets.md.txt
@@ -25,8 +25,8 @@ derived tables). Each loads with `load_<name>()` and returns a polars DataFrame.
 import moderndive as md
 
 md.available_datasets()          # sorted list of every dataset name
-md.load_dataset("promotions")    # load by name (string)
-md.load_promotions()             # or via the generated loader
+md.load_dataset("pennies")       # load by name (string)
+md.load_pennies()                # or via the generated loader
 ```
 
 ## By topic
@@ -37,10 +37,9 @@ md.load_promotions()             # or via the generated loader
   `pennies`, `pennies_sample`, `pennies_resamples`, `orig_pennies_sample`
 
 **Hypothesis testing & CIs**
-: `promotions`, `promotions_shuffled`, `mythbusters_yawn`, `movies_sample`,
-  `spotify_by_genre`, `spotify_52_original`, `spotify_52_shuffled`,
-  `spotify_metal_deephouse`, `offshore`, `age_at_marriage`, `zinc_tidy`,
-  `cle_sac`, `gss`
+: `mythbusters_yawn`, `movies_sample`, `spotify_by_genre`, `spotify_52_original`,
+  `spotify_52_shuffled`, `spotify_metal_deephouse`, `offshore`, `age_at_marriage`,
+  `zinc_tidy`, `cle_sac`, `gss`
 
 **Regression**
 : `evals`, `saratoga_houses`, `house_prices`, `coffee_quality`, `coffee_ratings`,
diff --git a/doc/_build/html/_sources/getting-started.md.txt b/doc/_build/html/_sources/getting-started.md.txt
index 2a3e65f..e8c9670 100644
--- a/doc/_build/html/_sources/getting-started.md.txt
+++ b/doc/_build/html/_sources/getting-started.md.txt
@@ -37,8 +37,8 @@ All datasets ship with the package and load with `load_<name>()`:
 ```{code-cell} python
 import moderndive as md
 
-promotions = md.load_promotions()
-promotions.head()
+yawn = md.load_mythbusters_yawn()
+yawn.head()
 ```
 
 
@@ -64,22 +64,22 @@ sentence:
 ```{code-cell} python
 from moderndive import specify, observe, get_p_value
 
-# 1. The observed statistic
+# 1. The observed statistic: do "seeded" people yawn more than the control group?
 obs = observe(
-    promotions, formula="decision ~ gender", success="promoted",
-    stat="diff in props", order=("male", "female"),
+    yawn, formula="yawn ~ group", success="yes",
+    stat="diff in props", order=("seed", "control"),
 )
 
 # 2. A null distribution: specify → hypothesize → generate → calculate
 null = (
-    specify(promotions, formula="decision ~ gender", success="promoted")
+    specify(yawn, formula="yawn ~ group", success="yes")
     .hypothesize(null="independence")
     .generate(reps=1000, type="permute", seed=42)
-    .calculate(stat="diff in props", order=("male", "female"))
+    .calculate(stat="diff in props", order=("seed", "control"))
 )
 
 # 3. Summarize
-get_p_value(null, obs_stat=obs, direction="right")   # ≈ 0.025
+get_p_value(null, obs_stat=obs, direction="right")
 ```
 
 Each verb has a focused guide: {doc}`guides/sampling`,
diff --git a/doc/_build/html/_sources/guides/hypothesis-testing.md.txt b/doc/_build/html/_sources/guides/hypothesis-testing.md.txt
index e57cdcc..a09c22f 100644
--- a/doc/_build/html/_sources/guides/hypothesis-testing.md.txt
+++ b/doc/_build/html/_sources/guides/hypothesis-testing.md.txt
@@ -24,30 +24,30 @@ from it.
 
 ## Two groups: a permutation test
 
-The classic "promotions" experiment — does the (perceived) gender on a résumé
-change the promotion rate?
+Are tracks more likely to be popular in *metal* than in *deep house*? Compare the
+two genres' "popular" rates, then permute the genre labels to build the null.
 
 ```{code-cell} python
 import moderndive as md
 from moderndive import specify, observe, get_p_value, visualize, shade_p_value
 
-promotions = md.load_promotions()
+spotify = md.load_spotify_metal_deephouse()
 
-# Observed difference in promotion proportions, male − female
+# Observed difference in "popular" proportions, metal − deep-house
 obs = observe(
-    promotions, formula="decision ~ gender", success="promoted",
-    stat="diff in props", order=("male", "female"),
-)   # ≈ 0.292
+    spotify, formula="popular_or_not ~ track_genre", success="popular",
+    stat="diff in props", order=("metal", "deep-house"),
+)
 
-# Null: gender is independent of the decision → permute the labels
+# Null: genre is independent of popularity → permute the labels
 null = (
-    specify(promotions, formula="decision ~ gender", success="promoted")
+    specify(spotify, formula="popular_or_not ~ track_genre", success="popular")
     .hypothesize(null="independence")
-    .generate(reps=1000, type="permute", seed=42)
-    .calculate(stat="diff in props", order=("male", "female"))
+    .generate(reps=1000, type="permute", seed=76)
+    .calculate(stat="diff in props", order=("metal", "deep-house"))
 )
 
-get_p_value(null, obs_stat=obs, direction="right")   # ≈ 0.025
+get_p_value(null, obs_stat=obs, direction="right")
 ```
 
 ## Shade the p-value
@@ -97,8 +97,33 @@ null_p = (
 `calculate(stat=...)` supports the full infer vocabulary: `"mean"`, `"median"`,
 `"sum"`, `"sd"`, `"prop"`, `"count"`, `"diff in means"`, `"diff in medians"`,
 `"diff in props"`, `"ratio of means"`, `"ratio of props"`, `"odds ratio"`,
-`"slope"`, `"correlation"`, `"t"`, `"z"`, `"F"`, `"Chisq"`, plus any custom
-callable `stat(response, explanatory) -> float`.
+`"slope"`, `"correlation"`, `"t"`, `"z"`, `"F"`, `"Chisq"`.
+
+## Custom test statistics
+
+Beyond those strings, `stat=` accepts **any function** that takes the response
+(and explanatory) arrays and returns a single number — so you can infer about a
+statistic that isn't built in. Here we bootstrap the interquartile range of
+almond weights and read off a 95% interval:
+
+```{code-cell} python
+import numpy as np
+from moderndive import get_confidence_interval
+
+def iqr(response, explanatory):
+    return float(np.percentile(response, 75) - np.percentile(response, 25))
+
+boot_iqr = (
+    md.load_almonds_sample_100()
+    .specify(response="weight")
+    .generate(reps=1000, type="bootstrap", seed=1)
+    .calculate(stat=iqr)
+)
+get_confidence_interval(boot_iqr, level=0.95, type="percentile")
+```
+
+The function receives `(response, explanatory)` as numpy arrays (`explanatory` is
+`None` for a single-variable `specify`), and must return a scalar.
 
 ```{seealso}
 Prefer a one-line classical test? See the tidy wrappers in {doc}`theory-based`
diff --git a/doc/_build/html/coming-from-r.html b/doc/_build/html/coming-from-r.html
index 1acd1a8..e18e318 100644
--- a/doc/_build/html/coming-from-r.html
+++ b/doc/_build/html/coming-from-r.html
@@ -292,7 +292,7 @@ <h2>Most names are identical<a class="headerlink" href="#most-names-are-identica
 <code class="docutils literal notranslate"><span class="pre">t_test</span></code>, <code class="docutils literal notranslate"><span class="pre">prop_test</span></code>, <code class="docutils literal notranslate"><span class="pre">chisq_test</span></code>, <code class="docutils literal notranslate"><span class="pre">t_stat</span></code>, <code class="docutils literal notranslate"><span class="pre">chisq_stat</span></code>,
 <code class="docutils literal notranslate"><span class="pre">rep_sample_n</span></code>/<code class="docutils literal notranslate"><span class="pre">rep_slice_sample</span></code>, <code class="docutils literal notranslate"><span class="pre">get_regression_table</span></code>,
 <code class="docutils literal notranslate"><span class="pre">get_regression_points</span></code>, <code class="docutils literal notranslate"><span class="pre">get_regression_summaries</span></code>, <code class="docutils literal notranslate"><span class="pre">get_correlation</span></code>,
-<code class="docutils literal notranslate"><span class="pre">pop_sd</span></code>, <code class="docutils literal notranslate"><span class="pre">tidy_summary</span></code>, <code class="docutils literal notranslate"><span class="pre">geom_parallel_slopes</span></code>.</p>
+<code class="docutils literal notranslate"><span class="pre">pop_sd</span></code>, <code class="docutils literal notranslate"><span class="pre">tidy_summary</span></code>, <code class="docutils literal notranslate"><span class="pre">geom_parallel_slopes</span></code>, <code class="docutils literal notranslate"><span class="pre">geom_categorical_model</span></code>.</p>
 </div></blockquote>
 </section>
 <section id="what-s-actually-different">
@@ -310,19 +310,15 @@ <h2>What’s actually different<a class="headerlink" href="#what-s-actually-diff
 <td><p><code class="docutils literal notranslate"><span class="pre">x.f(...)</span></code> (method chaining)</p></td>
 <td><p>no pipe operator in Python</p></td>
 </tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">geom_categorical_model()</span></code></p></td>
-<td><p><code class="docutils literal notranslate"><span class="pre">gg_categorical_model(engine=...)</span></code></p></td>
-<td><p>renamed to match <code class="docutils literal notranslate"><span class="pre">gg_parallel_slopes</span></code></p></td>
-</tr>
-<tr class="row-even"><td><p>ggplot2 <code class="docutils literal notranslate"><span class="pre">geom_*</span></code> layers</p></td>
+<tr class="row-odd"><td><p>ggplot2 <code class="docutils literal notranslate"><span class="pre">geom_*</span></code> layers</p></td>
 <td><p>plotly by default; <code class="docutils literal notranslate"><span class="pre">engine=&quot;plotnine&quot;</span></code> for ggplot-style</p></td>
 <td><p>dual-engine plotting</p></td>
 </tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">lm(y</span> <span class="pre">~</span> <span class="pre">x,</span> <span class="pre">data)</span></code> object</p></td>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">lm(y</span> <span class="pre">~</span> <span class="pre">x,</span> <span class="pre">data)</span></code> object</p></td>
 <td><p>a fitted <strong>statsmodels</strong> model: <code class="docutils literal notranslate"><span class="pre">smf.ols(&quot;y</span> <span class="pre">~</span> <span class="pre">x&quot;,</span> <span class="pre">data=df.to_pandas()).fit()</span></code></p></td>
 <td><p>regression backend</p></td>
 </tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">get_correlation(df,</span> <span class="pre">y</span> <span class="pre">~</span> <span class="pre">x)</span></code></p></td>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">get_correlation(df,</span> <span class="pre">y</span> <span class="pre">~</span> <span class="pre">x)</span></code></p></td>
 <td><p><code class="docutils literal notranslate"><span class="pre">get_correlation(df,</span> <span class="pre">&quot;y</span> <span class="pre">~</span> <span class="pre">x&quot;)</span></code> <em>or</em> <code class="docutils literal notranslate"><span class="pre">get_correlation(df,</span> <span class="pre">x=&quot;x&quot;,</span> <span class="pre">y=&quot;y&quot;)</span></code></p></td>
 <td><p>formula passed as a string</p></td>
 </tr>
@@ -343,7 +339,7 @@ <h2>Other things to know<a class="headerlink" href="#other-things-to-know" title
 <section id="same-datasets">
 <h2>Same datasets<a class="headerlink" href="#same-datasets" title="Link to this heading">¶</a></h2>
 <p>Most R <code class="docutils literal notranslate"><span class="pre">moderndive</span></code>/<code class="docutils literal notranslate"><span class="pre">infer</span></code> datasets are bundled here under the same name —
-<code class="docutils literal notranslate"><span class="pre">load_pennies()</span></code>, <code class="docutils literal notranslate"><span class="pre">load_promotions()</span></code>, <code class="docutils literal notranslate"><span class="pre">load_gss()</span></code>, etc. See <a class="reference internal" href="datasets.html"><span class="doc">Datasets</span></a>.</p>
+<code class="docutils literal notranslate"><span class="pre">load_pennies()</span></code>, <code class="docutils literal notranslate"><span class="pre">load_mythbusters_yawn()</span></code>, <code class="docutils literal notranslate"><span class="pre">load_gss()</span></code>, etc. See <a class="reference internal" href="datasets.html"><span class="doc">Datasets</span></a>.</p>
 </section>
 </section>
 
diff --git a/doc/_build/html/datasets.html b/doc/_build/html/datasets.html
index 320b3d8..7026b96 100644
--- a/doc/_build/html/datasets.html
+++ b/doc/_build/html/datasets.html
@@ -263,8 +263,8 @@ <h1>Datasets<a class="headerlink" href="#datasets" title="Link to this heading">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
 
 <span class="n">md</span><span class="o">.</span><span class="n">available_datasets</span><span class="p">()</span>          <span class="c1"># sorted list of every dataset name</span>
-<span class="n">md</span><span class="o">.</span><span class="n">load_dataset</span><span class="p">(</span><span class="s2">&quot;promotions&quot;</span><span class="p">)</span>    <span class="c1"># load by name (string)</span>
-<span class="n">md</span><span class="o">.</span><span class="n">load_promotions</span><span class="p">()</span>             <span class="c1"># or via the generated loader</span>
+<span class="n">md</span><span class="o">.</span><span class="n">load_dataset</span><span class="p">(</span><span class="s2">&quot;pennies&quot;</span><span class="p">)</span>       <span class="c1"># load by name (string)</span>
+<span class="n">md</span><span class="o">.</span><span class="n">load_pennies</span><span class="p">()</span>                <span class="c1"># or via the generated loader</span>
 </pre></div>
 </div>
 </div>
@@ -276,7 +276,7 @@ <h1>Datasets<a class="headerlink" href="#datasets" title="Link to this heading">
   white-space: pre-wrap;
 }
 </style>
-<small>shape: (48, 3)</small><table border="1" class="dataframe"><thead><tr><th>id</th><th>decision</th><th>gender</th></tr><tr><td>i64</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>1</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>2</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>3</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>4</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>5</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>44</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr><tr><td>45</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr><tr><td>46</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr><tr><td>47</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr><tr><td>48</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr></tbody></table></div></div></div>
+<small>shape: (800, 2)</small><table border="1" class="dataframe"><thead><tr><th>year</th><th>age_in_2011</th></tr><tr><td>i64</td><td>i64</td></tr></thead><tbody><tr><td>1986</td><td>25</td></tr><tr><td>1996</td><td>15</td></tr><tr><td>1994</td><td>17</td></tr><tr><td>2008</td><td>3</td></tr><tr><td>1999</td><td>12</td></tr><tr><td>&hellip;</td><td>&hellip;</td></tr><tr><td>1975</td><td>36</td></tr><tr><td>1989</td><td>22</td></tr><tr><td>1981</td><td>30</td></tr><tr><td>1991</td><td>20</td></tr><tr><td>1993</td><td>18</td></tr></tbody></table></div></div></div>
 </div>
 <section id="by-topic">
 <h2>By topic<a class="headerlink" href="#by-topic" title="Link to this heading">¶</a></h2>
@@ -285,10 +285,9 @@ <h2>By topic<a class="headerlink" href="#by-topic" title="Link to this heading">
 <code class="docutils literal notranslate"><span class="pre">almonds_bowl</span></code>, <code class="docutils literal notranslate"><span class="pre">almonds_sample</span></code>, <code class="docutils literal notranslate"><span class="pre">almonds_sample_100</span></code>,
 <code class="docutils literal notranslate"><span class="pre">pennies</span></code>, <code class="docutils literal notranslate"><span class="pre">pennies_sample</span></code>, <code class="docutils literal notranslate"><span class="pre">pennies_resamples</span></code>, <code class="docutils literal notranslate"><span class="pre">orig_pennies_sample</span></code></p>
 </dd>
-<dt><strong>Hypothesis testing &amp; CIs</strong></dt><dd><p><code class="docutils literal notranslate"><span class="pre">promotions</span></code>, <code class="docutils literal notranslate"><span class="pre">promotions_shuffled</span></code>, <code class="docutils literal notranslate"><span class="pre">mythbusters_yawn</span></code>, <code class="docutils literal notranslate"><span class="pre">movies_sample</span></code>,
-<code class="docutils literal notranslate"><span class="pre">spotify_by_genre</span></code>, <code class="docutils literal notranslate"><span class="pre">spotify_52_original</span></code>, <code class="docutils literal notranslate"><span class="pre">spotify_52_shuffled</span></code>,
-<code class="docutils literal notranslate"><span class="pre">spotify_metal_deephouse</span></code>, <code class="docutils literal notranslate"><span class="pre">offshore</span></code>, <code class="docutils literal notranslate"><span class="pre">age_at_marriage</span></code>, <code class="docutils literal notranslate"><span class="pre">zinc_tidy</span></code>,
-<code class="docutils literal notranslate"><span class="pre">cle_sac</span></code>, <code class="docutils literal notranslate"><span class="pre">gss</span></code></p>
+<dt><strong>Hypothesis testing &amp; CIs</strong></dt><dd><p><code class="docutils literal notranslate"><span class="pre">mythbusters_yawn</span></code>, <code class="docutils literal notranslate"><span class="pre">movies_sample</span></code>, <code class="docutils literal notranslate"><span class="pre">spotify_by_genre</span></code>, <code class="docutils literal notranslate"><span class="pre">spotify_52_original</span></code>,
+<code class="docutils literal notranslate"><span class="pre">spotify_52_shuffled</span></code>, <code class="docutils literal notranslate"><span class="pre">spotify_metal_deephouse</span></code>, <code class="docutils literal notranslate"><span class="pre">offshore</span></code>, <code class="docutils literal notranslate"><span class="pre">age_at_marriage</span></code>,
+<code class="docutils literal notranslate"><span class="pre">zinc_tidy</span></code>, <code class="docutils literal notranslate"><span class="pre">cle_sac</span></code>, <code class="docutils literal notranslate"><span class="pre">gss</span></code></p>
 </dd>
 <dt><strong>Regression</strong></dt><dd><p><code class="docutils literal notranslate"><span class="pre">evals</span></code>, <code class="docutils literal notranslate"><span class="pre">saratoga_houses</span></code>, <code class="docutils literal notranslate"><span class="pre">house_prices</span></code>, <code class="docutils literal notranslate"><span class="pre">coffee_quality</span></code>, <code class="docutils literal notranslate"><span class="pre">coffee_ratings</span></code>,
 <code class="docutils literal notranslate"><span class="pre">credit</span></code>, <code class="docutils literal notranslate"><span class="pre">MA_schools</span></code>, <code class="docutils literal notranslate"><span class="pre">un_member_states_2024</span></code>, <code class="docutils literal notranslate"><span class="pre">mario_kart_auction</span></code>,
diff --git a/doc/_build/html/getting-started.html b/doc/_build/html/getting-started.html
index 95cf0b4..835f13f 100644
--- a/doc/_build/html/getting-started.html
+++ b/doc/_build/html/getting-started.html
@@ -273,8 +273,8 @@ <h2>Load a dataset<a class="headerlink" href="#load-a-dataset" title="Link to th
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
 
-<span class="n">promotions</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_promotions</span><span class="p">()</span>
-<span class="n">promotions</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+<span class="n">yawn</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_mythbusters_yawn</span><span class="p">()</span>
+<span class="n">yawn</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
@@ -286,7 +286,7 @@ <h2>Load a dataset<a class="headerlink" href="#load-a-dataset" title="Link to th
   white-space: pre-wrap;
 }
 </style>
-<small>shape: (5, 3)</small><table border="1" class="dataframe"><thead><tr><th>id</th><th>decision</th><th>gender</th></tr><tr><td>i64</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>1</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>2</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>3</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>4</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>5</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr></tbody></table></div></div></div>
+<small>shape: (5, 3)</small><table border="1" class="dataframe"><thead><tr><th>subj</th><th>group</th><th>yawn</th></tr><tr><td>i64</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>1</td><td>&quot;seed&quot;</td><td>&quot;yes&quot;</td></tr><tr><td>2</td><td>&quot;control&quot;</td><td>&quot;yes&quot;</td></tr><tr><td>3</td><td>&quot;seed&quot;</td><td>&quot;no&quot;</td></tr><tr><td>4</td><td>&quot;seed&quot;</td><td>&quot;yes&quot;</td></tr><tr><td>5</td><td>&quot;seed&quot;</td><td>&quot;no&quot;</td></tr></tbody></table></div></div></div>
 </div>
 <p>List everything that’s available with <code class="docutils literal notranslate"><span class="pre">md.available_datasets()</span></code> (58 datasets), and
 see <a class="reference internal" href="datasets.html"><span class="doc">Datasets</span></a> for a thematic tour.</p>
@@ -322,22 +322,22 @@ <h2>The inference pipeline<a class="headerlink" href="#the-inference-pipeline" t
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span><span class="p">,</span> <span class="n">observe</span><span class="p">,</span> <span class="n">get_p_value</span>
 
-<span class="c1"># 1. The observed statistic</span>
+<span class="c1"># 1. The observed statistic: do &quot;seeded&quot; people yawn more than the control group?</span>
 <span class="n">obs</span> <span class="o">=</span> <span class="n">observe</span><span class="p">(</span>
-    <span class="n">promotions</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;decision ~ gender&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;promoted&quot;</span><span class="p">,</span>
-    <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;male&quot;</span><span class="p">,</span> <span class="s2">&quot;female&quot;</span><span class="p">),</span>
+    <span class="n">yawn</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;yawn ~ group&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;yes&quot;</span><span class="p">,</span>
+    <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;seed&quot;</span><span class="p">,</span> <span class="s2">&quot;control&quot;</span><span class="p">),</span>
 <span class="p">)</span>
 
 <span class="c1"># 2. A null distribution: specify → hypothesize → generate → calculate</span>
 <span class="n">null</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">promotions</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;decision ~ gender&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;promoted&quot;</span><span class="p">)</span>
+    <span class="n">specify</span><span class="p">(</span><span class="n">yawn</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;yawn ~ group&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;yes&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;independence&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;permute&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
-    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;male&quot;</span><span class="p">,</span> <span class="s2">&quot;female&quot;</span><span class="p">))</span>
+    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;seed&quot;</span><span class="p">,</span> <span class="s2">&quot;control&quot;</span><span class="p">))</span>
 <span class="p">)</span>
 
 <span class="c1"># 3. Summarize</span>
-<span class="n">get_p_value</span><span class="p">(</span><span class="n">null</span><span class="p">,</span> <span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>   <span class="c1"># ≈ 0.025</span>
+<span class="n">get_p_value</span><span class="p">(</span><span class="n">null</span><span class="p">,</span> <span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -349,7 +349,7 @@ <h2>The inference pipeline<a class="headerlink" href="#the-inference-pipeline" t
   white-space: pre-wrap;
 }
 </style>
-<small>shape: (1, 1)</small><table border="1" class="dataframe"><thead><tr><th>p_value</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.025</td></tr></tbody></table></div></div></div>
+<small>shape: (1, 1)</small><table border="1" class="dataframe"><thead><tr><th>p_value</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.512</td></tr></tbody></table></div></div></div>
 </div>
 <p>Each verb has a focused guide: <a class="reference internal" href="guides/sampling.html"><span class="doc">Sampling</span></a>,
 <a class="reference internal" href="guides/confidence-intervals.html"><span class="doc">Bootstrapping &amp; confidence intervals</span></a>, and <a class="reference internal" href="guides/hypothesis-testing.html"><span class="doc">Hypothesis testing</span></a>.</p>
@@ -371,7 +371,7 @@ <h2>Visualizing — choose your engine<a class="headerlink" href="#visualizing-c
 </div>
 </div>
 <div class="cell_output docutils container">
-<a class="reference internal image-reference" href="_images/679346fce431777e0d221fb96d0d46c94c46266531703754ae8cf2b7aea89916.png"><img alt="_images/679346fce431777e0d221fb96d0d46c94c46266531703754ae8cf2b7aea89916.png" height="480" src="_images/679346fce431777e0d221fb96d0d46c94c46266531703754ae8cf2b7aea89916.png" width="640" />
+<a class="reference internal image-reference" href="_images/a716ffc5702927ebba6a9176ddba190577d8d13ed180fbd9a689d9d06129dac6.png"><img alt="_images/a716ffc5702927ebba6a9176ddba190577d8d13ed180fbd9a689d9d06129dac6.png" height="480" src="_images/a716ffc5702927ebba6a9176ddba190577d8d13ed180fbd9a689d9d06129dac6.png" width="640" />
 </a>
 </div>
 </div>
diff --git a/doc/_build/html/guides/hypothesis-testing.html b/doc/_build/html/guides/hypothesis-testing.html
index d7fbcc3..e776442 100644
--- a/doc/_build/html/guides/hypothesis-testing.html
+++ b/doc/_build/html/guides/hypothesis-testing.html
@@ -261,30 +261,30 @@ <h1>Hypothesis testing<a class="headerlink" href="#hypothesis-testing" title="Li
 from it.</p>
 <section id="two-groups-a-permutation-test">
 <h2>Two groups: a permutation test<a class="headerlink" href="#two-groups-a-permutation-test" title="Link to this heading">¶</a></h2>
-<p>The classic “promotions” experiment — does the (perceived) gender on a résumé
-change the promotion rate?</p>
+<p>Are tracks more likely to be popular in <em>metal</em> than in <em>deep house</em>? Compare the
+two genres’ “popular” rates, then permute the genre labels to build the null.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
 <span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span><span class="p">,</span> <span class="n">observe</span><span class="p">,</span> <span class="n">get_p_value</span><span class="p">,</span> <span class="n">visualize</span><span class="p">,</span> <span class="n">shade_p_value</span>
 
-<span class="n">promotions</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_promotions</span><span class="p">()</span>
+<span class="n">spotify</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_spotify_metal_deephouse</span><span class="p">()</span>
 
-<span class="c1"># Observed difference in promotion proportions, male − female</span>
+<span class="c1"># Observed difference in &quot;popular&quot; proportions, metal − deep-house</span>
 <span class="n">obs</span> <span class="o">=</span> <span class="n">observe</span><span class="p">(</span>
-    <span class="n">promotions</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;decision ~ gender&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;promoted&quot;</span><span class="p">,</span>
-    <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;male&quot;</span><span class="p">,</span> <span class="s2">&quot;female&quot;</span><span class="p">),</span>
-<span class="p">)</span>   <span class="c1"># ≈ 0.292</span>
+    <span class="n">spotify</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;popular_or_not ~ track_genre&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;popular&quot;</span><span class="p">,</span>
+    <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;metal&quot;</span><span class="p">,</span> <span class="s2">&quot;deep-house&quot;</span><span class="p">),</span>
+<span class="p">)</span>
 
-<span class="c1"># Null: gender is independent of the decision → permute the labels</span>
+<span class="c1"># Null: genre is independent of popularity → permute the labels</span>
 <span class="n">null</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">promotions</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;decision ~ gender&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;promoted&quot;</span><span class="p">)</span>
+    <span class="n">specify</span><span class="p">(</span><span class="n">spotify</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;popular_or_not ~ track_genre&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;popular&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;independence&quot;</span><span class="p">)</span>
-    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;permute&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
-    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;male&quot;</span><span class="p">,</span> <span class="s2">&quot;female&quot;</span><span class="p">))</span>
+    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;permute&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">76</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;metal&quot;</span><span class="p">,</span> <span class="s2">&quot;deep-house&quot;</span><span class="p">))</span>
 <span class="p">)</span>
 
-<span class="n">get_p_value</span><span class="p">(</span><span class="n">null</span><span class="p">,</span> <span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>   <span class="c1"># ≈ 0.025</span>
+<span class="n">get_p_value</span><span class="p">(</span><span class="n">null</span><span class="p">,</span> <span class="n">obs_stat</span><span class="o">=</span><span class="n">obs</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -296,7 +296,7 @@ <h2>Two groups: a permutation test<a class="headerlink" href="#two-groups-a-perm
   white-space: pre-wrap;
 }
 </style>
-<small>shape: (1, 1)</small><table border="1" class="dataframe"><thead><tr><th>p_value</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.025</td></tr></tbody></table></div></div></div>
+<small>shape: (1, 1)</small><table border="1" class="dataframe"><thead><tr><th>p_value</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.075</td></tr></tbody></table></div></div></div>
 </div>
 </section>
 <section id="shade-the-p-value">
@@ -308,7 +308,7 @@ <h2>Shade the p-value<a class="headerlink" href="#shade-the-p-value" title="Link
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/446856f7a7a4b3ae8beb128934fc4936f3afde3e478c71e657c8305dcee97764.png" src="../_images/446856f7a7a4b3ae8beb128934fc4936f3afde3e478c71e657c8305dcee97764.png" />
+<img alt="../_images/57bcfc0de676a5294afac27836489f257436cfdc208aba5a2907c8b2aa95d9f8.png" src="../_images/57bcfc0de676a5294afac27836489f257436cfdc208aba5a2907c8b2aa95d9f8.png" />
 </div>
 </div>
 <p><code class="docutils literal notranslate"><span class="pre">direction</span></code> is one of <code class="docutils literal notranslate"><span class="pre">&quot;right&quot;</span></code>/<code class="docutils literal notranslate"><span class="pre">&quot;greater&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;left&quot;</span></code>/<code class="docutils literal notranslate"><span class="pre">&quot;less&quot;</span></code>, or <code class="docutils literal notranslate"><span class="pre">&quot;two-sided&quot;</span></code>.
@@ -366,8 +366,44 @@ <h2>Available statistics<a class="headerlink" href="#available-statistics" title
 <p><code class="docutils literal notranslate"><span class="pre">calculate(stat=...)</span></code> supports the full infer vocabulary: <code class="docutils literal notranslate"><span class="pre">&quot;mean&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;median&quot;</span></code>,
 <code class="docutils literal notranslate"><span class="pre">&quot;sum&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;sd&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;prop&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;count&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;diff</span> <span class="pre">in</span> <span class="pre">means&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;diff</span> <span class="pre">in</span> <span class="pre">medians&quot;</span></code>,
 <code class="docutils literal notranslate"><span class="pre">&quot;diff</span> <span class="pre">in</span> <span class="pre">props&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;ratio</span> <span class="pre">of</span> <span class="pre">means&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;ratio</span> <span class="pre">of</span> <span class="pre">props&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;odds</span> <span class="pre">ratio&quot;</span></code>,
-<code class="docutils literal notranslate"><span class="pre">&quot;slope&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;correlation&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;t&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;z&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;F&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;Chisq&quot;</span></code>, plus any custom
-callable <code class="docutils literal notranslate"><span class="pre">stat(response,</span> <span class="pre">explanatory)</span> <span class="pre">-&gt;</span> <span class="pre">float</span></code>.</p>
+<code class="docutils literal notranslate"><span class="pre">&quot;slope&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;correlation&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;t&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;z&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;F&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;Chisq&quot;</span></code>.</p>
+</section>
+<section id="custom-test-statistics">
+<h2>Custom test statistics<a class="headerlink" href="#custom-test-statistics" title="Link to this heading">¶</a></h2>
+<p>Beyond those strings, <code class="docutils literal notranslate"><span class="pre">stat=</span></code> accepts <strong>any function</strong> that takes the response
+(and explanatory) arrays and returns a single number — so you can infer about a
+statistic that isn’t built in. Here we bootstrap the interquartile range of
+almond weights and read off a 95% interval:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">get_confidence_interval</span>
+
+<span class="k">def</span><span class="w"> </span><span class="nf">iqr</span><span class="p">(</span><span class="n">response</span><span class="p">,</span> <span class="n">explanatory</span><span class="p">):</span>
+    <span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">response</span><span class="p">,</span> <span class="mi">75</span><span class="p">)</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">response</span><span class="p">,</span> <span class="mi">25</span><span class="p">))</span>
+
+<span class="n">boot_iqr</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">md</span><span class="o">.</span><span class="n">load_almonds_sample_100</span><span class="p">()</span>
+    <span class="o">.</span><span class="n">specify</span><span class="p">(</span><span class="n">response</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="n">iqr</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">boot_iqr</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;percentile&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output text_html"><div><style>
+.dataframe > thead > tr,
+.dataframe > tbody > tr {
+  text-align: right;
+  white-space: pre-wrap;
+}
+</style>
+<small>shape: (1, 2)</small><table border="1" class="dataframe"><thead><tr><th>lower_ci</th><th>upper_ci</th></tr><tr><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>0.4</td><td>0.625</td></tr></tbody></table></div></div></div>
+</div>
+<p>The function receives <code class="docutils literal notranslate"><span class="pre">(response,</span> <span class="pre">explanatory)</span></code> as numpy arrays (<code class="docutils literal notranslate"><span class="pre">explanatory</span></code> is
+<code class="docutils literal notranslate"><span class="pre">None</span></code> for a single-variable <code class="docutils literal notranslate"><span class="pre">specify</span></code>), and must return a scalar.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
 <p>Prefer a one-line classical test? See the tidy wrappers in <a class="reference internal" href="theory-based.html"><span class="doc">Theory-based inference</span></a>
@@ -436,6 +472,7 @@ <h2>Available statistics<a class="headerlink" href="#available-statistics" title
 <li><a class="reference internal" href="#shade-the-p-value">Shade the p-value</a></li>
 <li><a class="reference internal" href="#one-mean-one-proportion-point-null">One mean / one proportion (point null)</a></li>
 <li><a class="reference internal" href="#available-statistics">Available statistics</a></li>
+<li><a class="reference internal" href="#custom-test-statistics">Custom test statistics</a></li>
 </ul>
 </li>
 </ul>
diff --git a/doc/_build/html/searchindex.js b/doc/_build/html/searchindex.js
index 2c51e1e..ebca82a 100644
--- a/doc/_build/html/searchindex.js
+++ b/doc/_build/html/searchindex.js
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diff --git a/doc/_build/jupyter_execute/datasets.ipynb b/doc/_build/jupyter_execute/datasets.ipynb
index 38b9855..78fb45f 100644
--- a/doc/_build/jupyter_execute/datasets.ipynb
+++ b/doc/_build/jupyter_execute/datasets.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "72ce050a",
+   "id": "eb0874e7",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f97e9152",
+   "id": "b3860619",
    "metadata": {},
    "source": [
     "# Datasets\n",
@@ -31,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "a67c08b5",
+   "id": "ed09d3d4",
    "metadata": {},
    "outputs": [
     {
@@ -44,27 +44,27 @@
        "  white-space: pre-wrap;\n",
        "}\n",
        "</style>\n",
-       "<small>shape: (48, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>id</th><th>decision</th><th>gender</th></tr><tr><td>i64</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>1</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>2</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>3</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>4</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>5</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>44</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr><tr><td>45</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr><tr><td>46</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr><tr><td>47</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr><tr><td>48</td><td>&quot;not&quot;</td><td>&quot;female&quot;</td></tr></tbody></table></div>"
+       "<small>shape: (800, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>year</th><th>age_in_2011</th></tr><tr><td>i64</td><td>i64</td></tr></thead><tbody><tr><td>1986</td><td>25</td></tr><tr><td>1996</td><td>15</td></tr><tr><td>1994</td><td>17</td></tr><tr><td>2008</td><td>3</td></tr><tr><td>1999</td><td>12</td></tr><tr><td>&hellip;</td><td>&hellip;</td></tr><tr><td>1975</td><td>36</td></tr><tr><td>1989</td><td>22</td></tr><tr><td>1981</td><td>30</td></tr><tr><td>1991</td><td>20</td></tr><tr><td>1993</td><td>18</td></tr></tbody></table></div>"
       ],
       "text/plain": [
-       "shape: (48, 3)\n",
-       "┌─────┬──────────┬────────┐\n",
-       "│ id  ┆ decision ┆ gender │\n",
-       "│ --- ┆ ---      ┆ ---    │\n",
-       "│ i64 ┆ str      ┆ str    │\n",
-       "╞═════╪══════════╪════════╡\n",
-       "│ 1   ┆ promoted ┆ male   │\n",
-       "│ 2   ┆ promoted ┆ male   │\n",
-       "│ 3   ┆ promoted ┆ male   │\n",
-       "│ 4   ┆ promoted ┆ male   │\n",
-       "│ 5   ┆ promoted ┆ male   │\n",
-       "│ …   ┆ …        ┆ …      │\n",
-       "│ 44  ┆ not      ┆ female │\n",
-       "│ 45  ┆ not      ┆ female │\n",
-       "│ 46  ┆ not      ┆ female │\n",
-       "│ 47  ┆ not      ┆ female │\n",
-       "│ 48  ┆ not      ┆ female │\n",
-       "└─────┴──────────┴────────┘"
+       "shape: (800, 2)\n",
+       "┌──────┬─────────────┐\n",
+       "│ year ┆ age_in_2011 │\n",
+       "│ ---  ┆ ---         │\n",
+       "│ i64  ┆ i64         │\n",
+       "╞══════╪═════════════╡\n",
+       "│ 1986 ┆ 25          │\n",
+       "│ 1996 ┆ 15          │\n",
+       "│ 1994 ┆ 17          │\n",
+       "│ 2008 ┆ 3           │\n",
+       "│ 1999 ┆ 12          │\n",
+       "│ …    ┆ …           │\n",
+       "│ 1975 ┆ 36          │\n",
+       "│ 1989 ┆ 22          │\n",
+       "│ 1981 ┆ 30          │\n",
+       "│ 1991 ┆ 20          │\n",
+       "│ 1993 ┆ 18          │\n",
+       "└──────┴─────────────┘"
       ]
      },
      "execution_count": 2,
@@ -76,13 +76,13 @@
     "import moderndive as md\n",
     "\n",
     "md.available_datasets()          # sorted list of every dataset name\n",
-    "md.load_dataset(\"promotions\")    # load by name (string)\n",
-    "md.load_promotions()             # or via the generated loader"
+    "md.load_dataset(\"pennies\")       # load by name (string)\n",
+    "md.load_pennies()                # or via the generated loader"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f315ab33",
+   "id": "a3349063",
    "metadata": {},
    "source": [
     "## By topic\n",
@@ -93,10 +93,9 @@
     "  `pennies`, `pennies_sample`, `pennies_resamples`, `orig_pennies_sample`\n",
     "\n",
     "**Hypothesis testing & CIs**\n",
-    ": `promotions`, `promotions_shuffled`, `mythbusters_yawn`, `movies_sample`,\n",
-    "  `spotify_by_genre`, `spotify_52_original`, `spotify_52_shuffled`,\n",
-    "  `spotify_metal_deephouse`, `offshore`, `age_at_marriage`, `zinc_tidy`,\n",
-    "  `cle_sac`, `gss`\n",
+    ": `mythbusters_yawn`, `movies_sample`, `spotify_by_genre`, `spotify_52_original`,\n",
+    "  `spotify_52_shuffled`, `spotify_metal_deephouse`, `offshore`, `age_at_marriage`,\n",
+    "  `zinc_tidy`, `cle_sac`, `gss`\n",
     "\n",
     "**Regression**\n",
     ": `evals`, `saratoga_houses`, `house_prices`, `coffee_quality`, `coffee_ratings`,\n",
diff --git a/doc/_build/jupyter_execute/getting-started.ipynb b/doc/_build/jupyter_execute/getting-started.ipynb
index 309968e..c22eb33 100644
--- a/doc/_build/jupyter_execute/getting-started.ipynb
+++ b/doc/_build/jupyter_execute/getting-started.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "3268861f",
+   "id": "713ab722",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09601081",
+   "id": "b4b9b797",
    "metadata": {},
    "source": [
     "# Getting started\n",
@@ -44,7 +44,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "edc1cccb",
+   "id": "03a0299c",
    "metadata": {},
    "outputs": [
     {
@@ -57,21 +57,21 @@
        "  white-space: pre-wrap;\n",
        "}\n",
        "</style>\n",
-       "<small>shape: (5, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>id</th><th>decision</th><th>gender</th></tr><tr><td>i64</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>1</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>2</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>3</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>4</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr><tr><td>5</td><td>&quot;promoted&quot;</td><td>&quot;male&quot;</td></tr></tbody></table></div>"
+       "<small>shape: (5, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>subj</th><th>group</th><th>yawn</th></tr><tr><td>i64</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>1</td><td>&quot;seed&quot;</td><td>&quot;yes&quot;</td></tr><tr><td>2</td><td>&quot;control&quot;</td><td>&quot;yes&quot;</td></tr><tr><td>3</td><td>&quot;seed&quot;</td><td>&quot;no&quot;</td></tr><tr><td>4</td><td>&quot;seed&quot;</td><td>&quot;yes&quot;</td></tr><tr><td>5</td><td>&quot;seed&quot;</td><td>&quot;no&quot;</td></tr></tbody></table></div>"
       ],
       "text/plain": [
        "shape: (5, 3)\n",
-       "┌─────┬──────────┬────────┐\n",
-       "│ id  ┆ decision ┆ gender │\n",
-       "│ --- ┆ ---      ┆ ---    │\n",
-       "│ i64 ┆ str      ┆ str    │\n",
-       "╞═════╪══════════╪════════╡\n",
-       "│ 1   ┆ promoted ┆ male   │\n",
-       "│ 2   ┆ promoted ┆ male   │\n",
-       "│ 3   ┆ promoted ┆ male   │\n",
-       "│ 4   ┆ promoted ┆ male   │\n",
-       "│ 5   ┆ promoted ┆ male   │\n",
-       "└─────┴──────────┴────────┘"
+       "┌──────┬─────────┬──────┐\n",
+       "│ subj ┆ group   ┆ yawn │\n",
+       "│ ---  ┆ ---     ┆ ---  │\n",
+       "│ i64  ┆ str     ┆ str  │\n",
+       "╞══════╪═════════╪══════╡\n",
+       "│ 1    ┆ seed    ┆ yes  │\n",
+       "│ 2    ┆ control ┆ yes  │\n",
+       "│ 3    ┆ seed    ┆ no   │\n",
+       "│ 4    ┆ seed    ┆ yes  │\n",
+       "│ 5    ┆ seed    ┆ no   │\n",
+       "└──────┴─────────┴──────┘"
       ]
      },
      "execution_count": 2,
@@ -82,13 +82,13 @@
    "source": [
     "import moderndive as md\n",
     "\n",
-    "promotions = md.load_promotions()\n",
-    "promotions.head()"
+    "yawn = md.load_mythbusters_yawn()\n",
+    "yawn.head()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7ef99026",
+   "id": "8c6e0ce4",
    "metadata": {},
    "source": [
     "List everything that's available with `md.available_datasets()` (58 datasets), and\n",
@@ -103,7 +103,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "f6c6ec04",
+   "id": "01b3afca",
    "metadata": {},
    "outputs": [
     {
@@ -142,7 +142,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "379fb4ad",
+   "id": "99e2eb9f",
    "metadata": {},
    "source": [
     "## The inference pipeline\n",
@@ -154,7 +154,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "e1f08c56",
+   "id": "cdcd4d55",
    "metadata": {},
    "outputs": [
     {
@@ -167,7 +167,7 @@
        "  white-space: pre-wrap;\n",
        "}\n",
        "</style>\n",
-       "<small>shape: (1, 1)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>p_value</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.025</td></tr></tbody></table></div>"
+       "<small>shape: (1, 1)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>p_value</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.512</td></tr></tbody></table></div>"
       ],
       "text/plain": [
        "shape: (1, 1)\n",
@@ -176,7 +176,7 @@
        "│ ---     │\n",
        "│ f64     │\n",
        "╞═════════╡\n",
-       "│ 0.025   │\n",
+       "│ 0.512   │\n",
        "└─────────┘"
       ]
      },
@@ -188,27 +188,27 @@
    "source": [
     "from moderndive import specify, observe, get_p_value\n",
     "\n",
-    "# 1. The observed statistic\n",
+    "# 1. The observed statistic: do \"seeded\" people yawn more than the control group?\n",
     "obs = observe(\n",
-    "    promotions, formula=\"decision ~ gender\", success=\"promoted\",\n",
-    "    stat=\"diff in props\", order=(\"male\", \"female\"),\n",
+    "    yawn, formula=\"yawn ~ group\", success=\"yes\",\n",
+    "    stat=\"diff in props\", order=(\"seed\", \"control\"),\n",
     ")\n",
     "\n",
     "# 2. A null distribution: specify → hypothesize → generate → calculate\n",
     "null = (\n",
-    "    specify(promotions, formula=\"decision ~ gender\", success=\"promoted\")\n",
+    "    specify(yawn, formula=\"yawn ~ group\", success=\"yes\")\n",
     "    .hypothesize(null=\"independence\")\n",
     "    .generate(reps=1000, type=\"permute\", seed=42)\n",
-    "    .calculate(stat=\"diff in props\", order=(\"male\", \"female\"))\n",
+    "    .calculate(stat=\"diff in props\", order=(\"seed\", \"control\"))\n",
     ")\n",
     "\n",
     "# 3. Summarize\n",
-    "get_p_value(null, obs_stat=obs, direction=\"right\")   # ≈ 0.025"
+    "get_p_value(null, obs_stat=obs, direction=\"right\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "56377c2e",
+   "id": "b209acd5",
    "metadata": {},
    "source": [
     "Each verb has a focused guide: {doc}`guides/sampling`,\n",
@@ -223,14 +223,14 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "8d18f028",
+   "id": "aabd5f6b",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<plotnine.ggplot.ggplot object at 0x131a109d0>"
+       "<plotnine.ggplot.ggplot object at 0x1380d1f30>"
       ]
      },
      "execution_count": 5,
@@ -255,7 +255,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9abe800d",
+   "id": "d04bd0cc",
    "metadata": {},
    "source": [
     "See {doc}`guides/plotting` for shading, confidence-interval overlays, theoretical\n",
@@ -267,7 +267,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "addcf1c5",
+   "id": "d7cc0ce8",
    "metadata": {},
    "outputs": [
     {
@@ -311,7 +311,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7bf74dcd",
+   "id": "e324ad3f",
    "metadata": {},
    "source": [
     "Full details in {doc}`guides/regression`."
diff --git a/doc/_build/jupyter_execute/guides/confidence-intervals.ipynb b/doc/_build/jupyter_execute/guides/confidence-intervals.ipynb
index 6305a27..9087839 100644
--- a/doc/_build/jupyter_execute/guides/confidence-intervals.ipynb
+++ b/doc/_build/jupyter_execute/guides/confidence-intervals.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "31357d1d",
+   "id": "6db8d2e6",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff28f610",
+   "id": "21ef3e5f",
    "metadata": {},
    "source": [
     "# Bootstrapping & confidence intervals\n",
@@ -33,7 +33,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "acd56aac",
+   "id": "e03f85f1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "37281e53",
+   "id": "5398a005",
    "metadata": {},
    "source": [
     "## Confidence intervals — three methods"
@@ -60,7 +60,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "2b337ec9",
+   "id": "661d9537",
    "metadata": {},
    "outputs": [
     {
@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abccc242",
+   "id": "c5a8df6f",
    "metadata": {},
    "source": [
     "Distribution objects also expose the getter as a method:"
@@ -115,7 +115,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "2c770122",
+   "id": "e7e34fbb",
    "metadata": {},
    "outputs": [
     {
@@ -152,7 +152,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "565034ef",
+   "id": "278a565b",
    "metadata": {},
    "source": [
     "## Visualize the interval"
@@ -161,7 +161,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "e4036868",
+   "id": "ca8fb836",
    "metadata": {},
    "outputs": [
     {
@@ -169,7 +169,7 @@
       "image/png": 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Bootstrap Distribution\"},\"xaxis\":{\"title\":{\"text\":\"mean\"}},\"yaxis\":{\"title\":{\"text\":\"count\"}},\"shapes\":[{\"fillcolor\":\"#1f77b4\",\"line\":{\"width\":0},\"opacity\":0.3,\"type\":\"rect\",\"x0\":3.606975,\"x1\":3.7499999999999996,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"},{\"line\":{\"color\":\"#1f77b4\",\"width\":2},\"type\":\"line\",\"x0\":3.606975,\"x1\":3.606975,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"},{\"line\":{\"color\":\"#1f77b4\",\"width\":2},\"type\":\"line\",\"x0\":3.7499999999999996,\"x1\":3.7499999999999996,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"}]},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -233,7 +233,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3446664d",
+   "id": "5528a704",
    "metadata": {},
    "source": [
     "## A confidence interval for a proportion"
@@ -242,7 +242,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "fefa30e8",
+   "id": "11dfc037",
    "metadata": {},
    "outputs": [
     {
@@ -288,7 +288,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b5531efd",
+   "id": "6d99896f",
    "metadata": {},
    "source": [
     "## A confidence interval for a difference\n",
@@ -299,7 +299,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "21ac7056",
+   "id": "f8c8e1f4",
    "metadata": {},
    "outputs": [
     {
@@ -341,7 +341,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "656b6bf3",
+   "id": "c4b0debc",
    "metadata": {},
    "source": [
     "```{seealso}\n",
diff --git a/doc/_build/jupyter_execute/guides/hypothesis-testing.ipynb b/doc/_build/jupyter_execute/guides/hypothesis-testing.ipynb
index 8e425c0..fa387a2 100644
--- a/doc/_build/jupyter_execute/guides/hypothesis-testing.ipynb
+++ b/doc/_build/jupyter_execute/guides/hypothesis-testing.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "a8d4de14",
+   "id": "fd24ec8c",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "45bd0d3d",
+   "id": "c5db58fb",
    "metadata": {},
    "source": [
     "# Hypothesis testing\n",
@@ -30,14 +30,14 @@
     "\n",
     "## Two groups: a permutation test\n",
     "\n",
-    "The classic \"promotions\" experiment — does the (perceived) gender on a résumé\n",
-    "change the promotion rate?"
+    "Are tracks more likely to be popular in *metal* than in *deep house*? Compare the\n",
+    "two genres' \"popular\" rates, then permute the genre labels to build the null."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "2e44c72b",
+   "id": "0ab670c6",
    "metadata": {},
    "outputs": [
     {
@@ -50,7 +50,7 @@
        "  white-space: pre-wrap;\n",
        "}\n",
        "</style>\n",
-       "<small>shape: (1, 1)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>p_value</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.025</td></tr></tbody></table></div>"
+       "<small>shape: (1, 1)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>p_value</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.075</td></tr></tbody></table></div>"
       ],
       "text/plain": [
        "shape: (1, 1)\n",
@@ -59,7 +59,7 @@
        "│ ---     │\n",
        "│ f64     │\n",
        "╞═════════╡\n",
-       "│ 0.025   │\n",
+       "│ 0.075   │\n",
        "└─────────┘"
       ]
      },
@@ -72,28 +72,28 @@
     "import moderndive as md\n",
     "from moderndive import specify, observe, get_p_value, visualize, shade_p_value\n",
     "\n",
-    "promotions = md.load_promotions()\n",
+    "spotify = md.load_spotify_metal_deephouse()\n",
     "\n",
-    "# Observed difference in promotion proportions, male − female\n",
+    "# Observed difference in \"popular\" proportions, metal − deep-house\n",
     "obs = observe(\n",
-    "    promotions, formula=\"decision ~ gender\", success=\"promoted\",\n",
-    "    stat=\"diff in props\", order=(\"male\", \"female\"),\n",
-    ")   # ≈ 0.292\n",
+    "    spotify, formula=\"popular_or_not ~ track_genre\", success=\"popular\",\n",
+    "    stat=\"diff in props\", order=(\"metal\", \"deep-house\"),\n",
+    ")\n",
     "\n",
-    "# Null: gender is independent of the decision → permute the labels\n",
+    "# Null: genre is independent of popularity → permute the labels\n",
     "null = (\n",
-    "    specify(promotions, formula=\"decision ~ gender\", success=\"promoted\")\n",
+    "    specify(spotify, formula=\"popular_or_not ~ track_genre\", success=\"popular\")\n",
     "    .hypothesize(null=\"independence\")\n",
-    "    .generate(reps=1000, type=\"permute\", seed=42)\n",
-    "    .calculate(stat=\"diff in props\", order=(\"male\", \"female\"))\n",
+    "    .generate(reps=1000, type=\"permute\", seed=76)\n",
+    "    .calculate(stat=\"diff in props\", order=(\"metal\", \"deep-house\"))\n",
     ")\n",
     "\n",
-    "get_p_value(null, obs_stat=obs, direction=\"right\")   # ≈ 0.025"
+    "get_p_value(null, obs_stat=obs, direction=\"right\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6725639d",
+   "id": "757ab4ba",
    "metadata": {},
    "source": [
     "## Shade the p-value"
@@ -102,28 +102,28 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "f8df35f5",
+   "id": "10687007",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
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       ],
       "text/plain": [
        "Figure({\n",
        "    'data': [{'marker': {'color': '#7f7f7f', 'line': {'color': 'white', 'width': 1}},\n",
        "              'nbinsx': 20,\n",
        "              'type': 'histogram',\n",
-       "              'x': {'bdata': ('qqqqqqqq0r9QVVVVVVWlP1BVVVVVVa' ... 'AAAADAv1BVVVVVVaU/AAAAAAAAwL8='),\n",
+       "              'x': {'bdata': ('QLTIdr6fmj+AFK5H4XqEvwCq8dJNYm' ... 'YxCKyMPwCq8dJNYmA/AH9qvHSTeD8='),\n",
        "                    'dtype': 'f8'}}],\n",
        "    'layout': {'bargap': 0.02,\n",
        "               'shapes': [{'line': {'color': '#d62728', 'dash': 'solid', 'width': 2},\n",
        "                           'type': 'line',\n",
-       "                           'x0': 0.29166666666666663,\n",
-       "                           'x1': 0.29166666666666663,\n",
+       "                           'x0': 0.03399999999999992,\n",
+       "                           'x1': 0.03399999999999992,\n",
        "                           'xref': 'x',\n",
        "                           'y0': 0,\n",
        "                           'y1': 1,\n",
@@ -132,8 +132,8 @@
        "                           'line': {'width': 0},\n",
        "                           'opacity': 0.3,\n",
        "                           'type': 'rect',\n",
-       "                           'x0': 0.29166666666666663,\n",
-       "                           'x1': 0.4166666666666667,\n",
+       "                           'x0': 0.03399999999999992,\n",
+       "                           'x1': 0.07509999999999994,\n",
        "                           'xref': 'x',\n",
        "                           'y0': 0,\n",
        "                           'y1': 1,\n",
@@ -157,7 +157,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "322039dd",
+   "id": "78dc9c6e",
    "metadata": {},
    "source": [
     "`direction` is one of `\"right\"`/`\"greater\"`, `\"left\"`/`\"less\"`, or `\"two-sided\"`.\n",
@@ -171,7 +171,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "d2b210f4",
+   "id": "9bc5f431",
    "metadata": {},
    "outputs": [
     {
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b49eef5",
+   "id": "07ba6c30",
    "metadata": {},
    "source": [
     "For a one-proportion test you can also *simulate* draws directly:"
@@ -227,7 +227,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "4fc3dbfd",
+   "id": "77b331b4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -245,7 +245,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7a25388b",
+   "id": "f586c384",
    "metadata": {},
    "source": [
     "## Available statistics\n",
@@ -253,8 +253,73 @@
     "`calculate(stat=...)` supports the full infer vocabulary: `\"mean\"`, `\"median\"`,\n",
     "`\"sum\"`, `\"sd\"`, `\"prop\"`, `\"count\"`, `\"diff in means\"`, `\"diff in medians\"`,\n",
     "`\"diff in props\"`, `\"ratio of means\"`, `\"ratio of props\"`, `\"odds ratio\"`,\n",
-    "`\"slope\"`, `\"correlation\"`, `\"t\"`, `\"z\"`, `\"F\"`, `\"Chisq\"`, plus any custom\n",
-    "callable `stat(response, explanatory) -> float`.\n",
+    "`\"slope\"`, `\"correlation\"`, `\"t\"`, `\"z\"`, `\"F\"`, `\"Chisq\"`.\n",
+    "\n",
+    "## Custom test statistics\n",
+    "\n",
+    "Beyond those strings, `stat=` accepts **any function** that takes the response\n",
+    "(and explanatory) arrays and returns a single number — so you can infer about a\n",
+    "statistic that isn't built in. Here we bootstrap the interquartile range of\n",
+    "almond weights and read off a 95% interval:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3151f227",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (1, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>lower_ci</th><th>upper_ci</th></tr><tr><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>0.4</td><td>0.625</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (1, 2)\n",
+       "┌──────────┬──────────┐\n",
+       "│ lower_ci ┆ upper_ci │\n",
+       "│ ---      ┆ ---      │\n",
+       "│ f64      ┆ f64      │\n",
+       "╞══════════╪══════════╡\n",
+       "│ 0.4      ┆ 0.625    │\n",
+       "└──────────┴──────────┘"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from moderndive import get_confidence_interval\n",
+    "\n",
+    "def iqr(response, explanatory):\n",
+    "    return float(np.percentile(response, 75) - np.percentile(response, 25))\n",
+    "\n",
+    "boot_iqr = (\n",
+    "    md.load_almonds_sample_100()\n",
+    "    .specify(response=\"weight\")\n",
+    "    .generate(reps=1000, type=\"bootstrap\", seed=1)\n",
+    "    .calculate(stat=iqr)\n",
+    ")\n",
+    "get_confidence_interval(boot_iqr, level=0.95, type=\"percentile\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56d38353",
+   "metadata": {},
+   "source": [
+    "The function receives `(response, explanatory)` as numpy arrays (`explanatory` is\n",
+    "`None` for a single-variable `specify`), and must return a scalar.\n",
     "\n",
     "```{seealso}\n",
     "Prefer a one-line classical test? See the tidy wrappers in {doc}`theory-based`\n",
@@ -296,7 +361,9 @@
    66,
    78,
    82,
-   93
+   93,
+   109,
+   123
   ]
  },
  "nbformat": 4,
diff --git a/doc/_build/jupyter_execute/guides/plotting.ipynb b/doc/_build/jupyter_execute/guides/plotting.ipynb
index 7dfd2d9..89bec08 100644
--- a/doc/_build/jupyter_execute/guides/plotting.ipynb
+++ b/doc/_build/jupyter_execute/guides/plotting.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "4a57ea3b",
+   "id": "6980f148",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3feb4bb4",
+   "id": "9f6cbe57",
    "metadata": {},
    "source": [
     "# Plotting: plotly & plotnine\n",
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "828ca414",
+   "id": "da325bf6",
    "metadata": {},
    "outputs": [
     {
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a846f4e4",
+   "id": "fe97e607",
    "metadata": {},
    "source": [
     "## visualize() returns an InferPlot\n",
@@ -99,7 +99,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "8a6a4af3",
+   "id": "f6a547db",
    "metadata": {},
    "outputs": [
     {
@@ -107,7 +107,7 @@
       "image/png": 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       ],
       "text/plain": [
        "Figure({\n",
@@ -163,14 +163,14 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "e70f8f0c",
+   "id": "b13f011a",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "<plotnine.ggplot.ggplot object at 0x13145f950>"
+       "<plotnine.ggplot.ggplot object at 0x134933950>"
       ]
      },
      "execution_count": 4,
@@ -190,7 +190,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35d68302",
+   "id": "1134270e",
    "metadata": {},
    "source": [
     "### Keyword form\n",
@@ -201,7 +201,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "eaf6ea20",
+   "id": "6d59b471",
    "metadata": {},
    "outputs": [
     {
@@ -209,7 +209,7 @@
       "image/png": 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NWfSRjqTndZeJ5k+3roAFJCDBD0N6pV0zBANfvQqrV+t1mSb9EZCOQ0kaVGkbNSBKKlPGbKr9d8ugx3N+JGm3H9KsK6R6ZVc/dFzR4lxTlg40uLrx2kvNcfCldeVUl5WU9OvujdbVumC+WjWqyc1drgjOljjWK+j6oUuD6+pW3zXI/iGkngMJgQTRD5L6VfjcxR9KwOoHLXDthi06ykkaTN1o9UVSUpaL1qHr1ZeY9Wt+/N2MSzPQq8a6vV5x1nFRqbQWWq5+oLj4grNE21C/Xi0pa1091eWFpUYNT5KLrQ8BwXV6rqmn+mz+e3twcVTG+qFPC9YPqnp+6LSeY7d3a6uTstT6xsdMZA8ubHy66Dchwbz6Oqr9pt80ZGdhhAACDhSgSrETcFzAq02/+oqLZPnMseYq6MN33yTXWVf69IqSfu15Xa+HzJu05gslVbSuqmr+9IwMHeUkvdqqMxrw6Li0Kd0KzvRxS/pINb1aqLdWDBs9RfTrUC07+Kt1nbab1q7PPszUfgAAEABJREFUCkBO/OdRBTYJ3s+8fuPWAutyL6hSuaKZ1a/RzYQ1eG3muzLOuhKZO3329Q/WmkP/NUjSK8Sa9Eq5XiWcY12p1P4Y8fTrok/V0Nx2293ywrM1u0x6dZ40sa4WDxw2Qaa9vczcW21WWIPf/lhvDUX0vtLTW/SUYNLbAXTFpuxHcP2efSVSf+Sny0uTBvW6TrSNmvRq8ZeLnzM/ltR7M/XJIFq23X7YlB2gnlzIj6COO+ZILUr0UVRmooiBBittWp1nbh+47Pq7zL3menvDj/nuNy9i85zFyRUq5EznnnjPCpiadugn+kFSP7zo1/xLrQ9nmicjM+vJFTpdVAp+8Nq45e+isthevmFzVhlH1a1V6DbJFcpLZCzKFxvgFrrzfAv/+Y+s/tuc3cf5Vkds9r/WN1p67unvFnIXGrx//n9rC16Fz50vEAiIbq8fvHMvZxoBBBDwq4AjA17tjMTEBHOFVp9UMGRAN1n8xuM5Twd4P4wHwidYbwBabv6UmJCQf1Gp5p99ea553JJe3dOvGGdOfkhWzBorua/0hbqD/QcPmk2CVx7NTPYgKfuHY/qjnuxFhY4SEwq2U79uf+vZByR36nRls0K3z73wOOtN/+Yubcyi5dk/rLPb7uOPOVKWvDVarrr0ArP94uWrRB/9dcl1/5bg/Y56G4WufHDQjVJYurJVE10tO3dn3f/6j/p1zHwkB3qVuM+NV5lbEPSKswb0dvshGGQU1l9lsvvrgPUNQEn1feSem2XIgG7mGwm911x/wKY/XNJ7m0vatrj1eh/4nUPGmcf83dWns+hX9XqrxP39uxa3WZ51waA4MfuKfZ6VIc6syf7WRq86F7VptCyK2l9JywOBQElZSrV+9559Eryinrug4LKSzvfc2zCNAAIIICBSMAqKs0qa9XVwYVXQF/pg0PhzEff6FbZduMvCveIb/Cry1WcGm68YT/xnfdGrNMFAJ3990otob+58R9apaWYLuyoYvDpWp9bhJk8oAw1cTzvpGMmdct+XW1xZZawPJLpef5yn41DarU9A0ABm1YJnzVVV/VCjQe68JR9rUXLCsVlXsi9vca50bNO0QNKv5zVjsK7RegZtIBDQ3ZhbEDIlU+z2g/64TDcs7P7WjZu36SqpWydvfxV2HJSxgkn9dkO/htf7SMcM622u2ukTF3bu3mvKCQ7SrW8WgtMljT/I/gt0Y4b1ke6dWpvbYvQrfg3yS9o2uD7YtqKuygbzlTTWH6f+al3R19sF9Cv7ovJHy6Ko/RW1PPhNSvB+8+xDxPrwcKCoTQost9NX+rzrTdZVZL2NKHcBweOntO65y2QaAdcIUFEESiHguIC3+dX9zWOCCvtRiN5rqG3VXyHrOFrpiNqHm6+S9Q0nuA/96l7vdwzOFzXWR1HputxXlPX+2t//yvsVpAbwms/OPXbBr8GnzVsmud8AN1pf7S9e/rno7QU1D6+qxcUk6VMk3pyz1OzrlBP+YcZ2273SCrZ0e90oEAiIXgnX+411/j/ZT5vQp1vo/MRX5uooT9KAI/i1vgbsunJevr+IpU9D0OWlTXqPs37w0T+AogGX3X7QwE2vVurtEBrMBeuRZn240ds3dD54K0qVysk6K7nvrdYF+gFg5WdrdNIk/Vq/dbN/yZnZ9zmrg66oVqWSjmRj9m0BZqaEQfAHmklJWfdTa3atW9BV54tL+jSDl6ctMllOOzGr/81MCAM9jjXY1VsptG0P3dWzyK2jaVHkTgtZoUG+nm96H3T1all/pEI/zGrW734+9KQYfcrIZ9lPHNF1wRRKX51z+glmM/1hoJnIHsx8Z4WZOv2kY82YAQIIIICAPQHHBbz65qY/UmvT7R7zzFB97qQ+f1WfRamP6NE3Gztfu9trfuG5gs8Avv3uJ0Qfl6X34Dbv2N88zaHwLQ4t1R+P6Jw+R1S31ed/XnHD3aJfSevy3Ekfo6U/zNNHHekTHJ6YOC336pxpfVO9reuV5odrPfqPNI+10l/w618700x3W19LBwJZVyN1PpJJ+0Pv1dWk9dTnwJ7f7g5joVfc9ccxuj+77Z67+CNpdd0g8/QNfcbp1Lnvy0NPvqJFyPUdWplxz+suN1cy1U8dp81bLq/Pek/0uNBtv1qT9QPDVk3PMfk0iBz00LPy/OvzReun96SagkIY6K/itY16rOljzvR4eyD7sVuD77zBlBRKPwy4tZPZRh8tpX2rgcutg0YbN71qG7xCpz+c0oBPH7n2rBXg6w8BNf/2HbtEjz99rqreyrB4+SrRJ1LofbYagJ9wbH1Tvn6DoBN6nOm5os9e1VsWdFlRqdEZWcHU0MdfkmdenG1uwel0y1BjXNg2GvSr65Spi8yPHK+++QFzLOpfQKx5eLXCNimw7IvVP4va6vNvtV0XtOtrHg+mGZ97/N8S/OGazudP0bTIv6/c8yutD2d6Tmrf6WvPofOtS0624489ytz2osexPjJM+7BLn4dNW3MyZU+E0lc9rr3UbKXPTZ4wZY55NJze+qPngX5IvKxFY7OeAQIIIICAPQHHBbyP3neL6C+hNRDUoE7fRDQY0Pku7VvIK0/fJxWTy+dpnf7aPLggIfte1UAgEFxkxoFA1nzuvNkrzCj3QH+Bfn6jU01won8QYfr85VYw1lJ0mebLKkmnslLuMu+5o4t5nJX+uVTdVn/wdN7Zp0gwMAwEDm19r5W3dbNGMmvBBzLcCvo00NESA4GsPIFA1liX9e5xlfS5sb0JnPW5wfpGqPf56X3Cra0rf5pHU1Ht13Wagut12m7SIEyT1lMDd/36Wfc7wuqrYBl2261Py9CrY/r0DX3GqQa7//19nehfcjv37JNNcfpDwpmTh0ubVueZx5M9+MQUGfH0a+bKvzoGryrqI+YmP3GXucK98P3PTECoT5kI/gWvQOCQnym4sEF2ns++/tEK+JaIHmvvLP1UyiQmSqc2zUQfXRcMVHRzu/2gx8pTD91hfdWdavpWg3Xdhz7tQD+gaFmaNNjVR5apqQafI55+XfSZsYcfVtU8Mk+3eWz8mzJw2AQT0OuHpBH33iwJCVlt0ydb6CPD9H5hPVf0cX5rN26RQCBrve4jf7q6TVPRe6j1aQMaoOmPLMuXL5dzj3xhm/61brPoH/TQHzjqhyDd5203tM1fdIH5YFm6L7Wds+hDc16dYAWK/W66WpZOHyPa9gIbWguC51VpLayiiv0frGPw3AgEsuzKl0syjxLUvtPAV9s9avBtok9HCRZYtXJF0f7Qef3AoX2oz9kN9nEgkFWWri+qr3SdpmB7dfrww6rI7BcfFr3nfbwV8Gr/6wc7fW188cm7zfGp+QKBQ+XrfO6UmJggelwI/3wqQLMRQCC3gOMC3raXnC8TR/1bVi99wTxDVu9f1Gfarnn/JRl8Z1dzP2ywARXKl5Xvl08RDSyCyzTQ0GX6/MrgMh3rm5Qu1ydA6HwwXXz+maYMvY80uEwDrkmP/Vvem/qEzJz8kOgv9u/rd4Pos0u1jMqVkk3Wwvav90K++ewD5tm/WveP3x4v+uMwfZPSbatWqWi21cGxR9czz6H9ZP4E0ScDfPz2M7pYNPDTvBrgmwXWQAOw3t3byTdLJsvbUx4x5X9qbaePIrJW5/wvqv1aLy1z6MDuOXlLmtD7RnWb3GmleZbrYNH9ap2CZWj5dtqt25m2zhtv3tDffWu0fGxNB6/u5i5Pgws9DjTo1CdEaD+o4+m5HremgZM+x1fX6zOK1UT/gpfWWZ/wESyvqPE1VzYz/a/5cyd9WsOwQT0keCU2uL222U4/aP5WF50j+lxUfd6rBi9fvfu8aKCY/1FYemwGfzimx/pIK6iqVrWSOTa0/Wqk26uTPms4f500iFYjTR/OHWcCdf1QqO0ZM6y3ViVP0jboPdT6QzV9HrQGnW9OGCJ6bOg2zZucmSe/BuXqoWVr32mbdJ/BoDtP5nwzeoxrmbmTtlHbq99aBO/Dzr1Z/vOqtBZatj4/WI9nnc6f9IOk1k+PJV0XtNNjXdusTyXRc077Qj+EaZ7cSfv584WTZNYLw81rhhrpc3e1zOCV/mB+ddN+0qRl64eq/O0N5tVgV/tdf/SqryX6OqGvjfpNQzBPsK6F9bOeE1qXYF7GCCCAgJ8FHBfwBjtD35T13tSTjmsgOrbz5hrcNhLjQCAgei+vXt0L5cc8um+tq956oXXPHeDqusJSFSuA1ufIJiVlPee0sDzBZZpHgwgtX6/gBJeHM470NqG0W6+M6Ru6Pps2+KSJwuqjx4EGeNreovohuN9/1D9CYmVitx+0bnrbgrZVr0gX1sbgMv3QkP9Y1/arkW6vZsG8+ceBQMAE5/q85/zriprX/Z1ywtFSWNBZ2DZath6n2qbC1kd7WTQtiqq79pneK67nnO6/qHzJFcqZH1vqa0ZReYLLA4HQ+koDXH0t0deJYBmMEUAAAQRCE3BswBtaM8iNAAIIIIBA2AJsiAACHhcg4PV4B9M8BEojcFu3tvLAgG6lKYJtEUAAAQQQiLsAAa/dLiAfAj4U0Hvc9b5rHzadJiOAAAIIeEiAgNdDnUlTEEAAgVgIsA8EEEDAbQIEvG7rMeqLAAIIIIAAAgggEJJAlALekOpAZgQQQAABBBBAAAEEoiZAwBs1WgpGAAEERAQEBBBAAIG4CxDwxr0LqAACCCCAAAIIIOB9gXi2kIA3nvrsGwEEEEAAAQQQQCDqAgS8USdmBwggYF+AnAgggAACCERegIA38qaUiAACCCCAAAIIlE6ArSMqQMAbUU4KQwABBBBAAAEEEHCaAAGv03qE+iBgX4CcCCCAAAIIIGBDgIDXBhJZEEAAAQQQQMDJAtQNgeIFCHiL92EtAggggAACCCCAgMsFCHhd3oFU374AORFAAAEEEEDAnwIEvP7sd1qNAAIIIOBfAVqOgO8ECHh91+U0GAEEEEAAAQQQ8JcAAa+/+tt+a8mJAAIIIIAAAgh4RICA1yMdSTMQQAABBKIjQKkIIOB+AQJe9/chLUAAAQQQQAABBBAoRoCAtxgc+6vIiQACCCCAAAIIIOBUAQJep/YM9UIAAQTcKECdEUAAAQcKEPA6sFOoEgIIIIAAAggggEDkBOIR8Eau9pSEAAIIIIAAAggggEAJAgS8JQCxGgEEEIieACUjgAACCMRCgIA3FsrsAwEEEEAAAQQQQKBogSivIeCNMjDFI4AAAggggAACCMRXgIA3vv7sHQEE7AuQEwEEEEAAgbAECHjDYmMjBBBAAAEEEEAgXgLsN1QBAt5QxciPAAIIIIAAAggg4CoBAl5XdReVRcC+ADkRQAABBBBAIEuAgDfLgSECCCCAAAIIeFOAViEgBLwcBAgggAACCCCAAAKeFiDg9XT30jjbAmREAAEEEEAAAc8KEPB6tmtpGAIIIIAAAqELsAUCXhQg4PVir9ImBBBAAAEEEEAAgRwBAt4cCibsC5ATAQQQQAABBBBwjwABr3v6ihb9hYMAABAASURBVJoigAACCDhNgPoggIArBAh4XdFNVBIBBBBAAAEEEEAgXAEC3nDl7G9HTgQQQAABBBBAAIE4ChDwxhGfXSOAAAL+EqC1CCCAQHwECHjj485eEUAAAQQQQAABBGIk4LiAN0btZjcIIIAAAggggAACPhEg4PVJR9NMBBBwnQAVRgABBBCIkAABb4QgKQYBBBBAAAEEEEAgGgKlL5OAt/SGlIAAAggggAACCCDgYAECXgd3DlVDAAH7AuREAAEEEECgKAEC3qJkWF5AYPH3G+Xoe96R9uM/kvd+3ETCgGOAY8B3x0DfN782r4NPLvmlwGskCxBwiADVKESAgLcQFBYhgAACCCCAAAIIeEeAgNc7fUlLELAvQE4EEEAAAQR8JEDA66POpqkIIIAAAgggkFeAOX8IEPD6o59pJQIIIIAAAggg4FsBAl7fdj0Nty9ATgQQQAABBBBwswABr5t7j7ojgAACCCAQSwH2hYBLBQh4XdpxVBsBBBBAAAEEEEDAngABrz0nctkXICcCCCCAAAIIIOAoAQJeR3UHlUEAAQQQ8I4ALUEAAacIEPA6pSeoBwIIIIAAAggggEBUBAh4o8Jqv1ByIoAAAggggAACCERXgIA3ur6UjgACCCBgT4BcCCCAQNQECHijRkvBCCCAAAIIIIAAAk4QcFfA6wQx6oAAAggggAACCCDgKgECXld1F5VFAAEEsgQYIoAAAgjYFyDgtW9FTgQ8L5CRkSFr1qxxTdL6er5TaCACCCCAQHECttYR8NpiIhMC/hDIzMx0TbCrgbk/eoVWIoAAAgiUVoCAt7SCbI8AAs4XoIYIIIAAAr4WIOD1dffTeAQQQAABBBDwk4Bf20rA69eep90IIIAAAggggIBPBAh4fdLRNBMB+wLkRAABBBBAwFsCBLze6k9agwACCCCAAAKREqAczwgQ8HqmK2kIAggggAACCCCAQGECBLyFqbAMAfsC5EQAAQQQQAABhwsQ8Dq8g6geAggggAAC7hCglgg4V4CA17l9Q80QQAABBBBAAAEEIiBAwBsBRIqwL0BOBBBAAAEEEEAg1gIEvLEWZ38IIIAAAgiIYIAAAjEUIOCNITa7QgABBBBAAAEEEIi9AAFv7M3t75GcCCCAAAIIIIAAAqUWIOAtNSEFIIAAAghEW4DyEUAAgdIIEPCWRo9tEUAg7gJpaWmyYcMG1yStb9zRqAACCCDgMwEPBbw+6zmaiwACRiAQCMiyZctckxISeNk1HccAAQQQiKEAr7wxxGZXCCCAQEwE2AkCCCCAQB4BAt48HMwggAACCCCAAAIIeEUg2A4C3qAEYwQQQAABBBBAAAFPChDwerJbaRQCCNgXICcCCCCAgNcFCHi93sO0DwEEEEAAAQQQsCPg4TwEvB7uXJqGAAIIIIAAAgggIELAy1GAAAKhCJAXAQQQQAAB1wkQ8Lquy6gwAggggAACCMRfgBq4SYCA1029RV0RQAABBBBAAAEEQhYg4A2ZjA0QsC9ATgQQQAABBBCIvwABb/z7gBoggAACCCDgdQHah0BcBXwT8O7bf1DWb9wqGRmZhYLr8o1btklaenqh63fvSZHtO3cXuo6FCCCAAAIIIIAAAs4V8EXA23fwWDnn0lul1XWDpGmHfvLExGl5emTFJ6ul8RW9pEWngXJGi5tk2rzlOetT9u0X3f7cNr3lgnZ9pXPv4bJ1286c9UxEUICiEEAAAQQQQACBKAj4IuA94dj6MvvFh+XLxc/J8LtukhffWiBrfvzNcOqV30EPPSt39Gwvq5e+IGOH95UHn5giazdsMevfmL1UfvltrSyb8ZR8On+CJCYkyNjJM806BggggAACCERDgDIRQCCyAr4IeDWYPf6YI6V8ubLSrElDqV3zMPnky++N5KqvfxS9itu53cVSJjFRWl54tjQ4sras+OQbs37RslXSsU1TqVWjmlSulCxdO7aSWQs+kMzMwm+NMBsxQAABBBBAAAEEEHCMgC8C3tzaf6zdJJu2bBe96qvLN23dbgLcsmWTdNakYxvUlY2bt5tpzV+/Xm0zrYOj6tbSkezak2LG8RuwZwQQQAABBBBAAAE7Ar4KePem7Jf+D4yTs047Xi7412nGZ9fuvZJcobyZDg7KWVeC9UdqehVXr/7qleGcddmBcYpVli7buz9N/JL2H8z6QV96RqboNCndcw56TJOiL8C5E+Fzx3ptipVpWnqGOUBS0zJ889rvl/c4p7fTHHgMwhbwTcCr9+oOGPqMpFsvVuMe7ieJiVlNr1K5ormlIbfggQMHze0LgUDABMMHDqbmrA5OJydnBckJVh4/JYWwmix+arOf2qr9S4q+gJ+OKa+1NSABc4AEAgFeBzGI6TEg/CuVQFbUV6oinL/xrj0p0uueMbJz11555en7pFrVSjmVrl3jMNHbFlJT03KW6Y/U6tQ6zMzr/bx/rttkpnXw1/rNOpIqlZLNuEK5RPFLKpuUdbjoG5hOkxLEawbmoGYQdQGvHTd+ak9iYsAcH2WssV9e+2mnM97nzYHHIGyBrAgm7M2dv2HKvgNyQ5+HZfPW7fLQXT1l7779sm7jVtmweZupfKOGJ5rxm3OWmmfwvrfyS/OEhqbnNTTLWzdrJNPnLbe23yF79u6TV2cskQ6XXySBQNaLnsnEAAEEEHCNABVFAAEE/Cfg+YBX78X99Y/15ipuh5uGyCXXDTLpmluHmt5OrlBOxj1yp4wa/6Z5Bu+dQ8bJ/f27ypFH1DTru7RvKcc0qCvNO/Y3z+rVK8F9e3Yw6xgggAACCCCAAAIIOF+g0IDX+dW2X0N9BNn3y6dI/rRyzricQi4+/0z5dumL8u5bo+WbJZOl81UtctZVTC4vz44cIB/PGy8rZo2VqZOGmkeU5WRgAgEEEEAAAQQQQMDRAp4PeO3q64/Y6tWpIUlJZQrdpGrlilKjetVC17EQAQQ8K0DDEEAAAQQ8IEDA64FOpAkIIIAAAggggEB0BdxdOgGvu/uP2iOAAAIIIIAAAgiUIEDAWwIQqxFAwL4AORFAAAEEEHCiAAGvE3uFOiGAAAIIIICAmwWou8MECHgd1iFUBwEEEEAAAQQQQCCyAgS8kfWkNATsC5ATAQQQQAABBGIiQMAbE2Z2ggACCCCAAAJFCbAcgWgLEPBGW5jyEUAAAQQQQAABBOIqQMAbV352bl+AnAgggAACCCCAQHgCBLzhubEVAggggAAC8RFgrwggELIAAW/IZGyAAAIIIIAAAggg4CYBAl439Zb9upITAQQQQAABBBBAIFuAgDcbghECCCCAgBcFaBMCCCAgQsDLUYAAAggggAACCCDgaQECXhHxdA/TOAQQQAABBBBAwOcCBLw+PwBoPgIIIJBLgEkEEEDAkwIEvJ7sVhqFAAIIIIAAAgggEBQIPeANbskYAQQQQAABBBBAAAEXCBDwuqCTqCICCDhTgFohgAACCLhDgIDXHf1ELRFAAAEEEEAAAacKOL5eBLyO7yIqiAACCCCAAAIIIFAaAQLe0uixLQII2BcgJwIIIIAAAnESIOCNEzy7RQABBBBAAAF/CtDq2AsQ8MbenD0igAACCCCAAAIIxFCAgDeG2OwKAfsC5EQAAQQQQACBSAkQ8EZKknIQQAABBBBAIPIClIhABAQIeCOASBEIIIAAAggggAACzhUg4HVu31Az+wLkRAABBBBAAAEEihQg4C2ShhUIIIAAAgi4TYD6IoBAYQIEvIWpsAwBBBBAAAEEEEDAMwIEvJ7pSvsNIScCCCCAAAIIIOAnAQJeP/U2bUUAAQQQyC3ANAII+ESAgNcnHU0zEUAAAQQQQAABvwoQ8JbU86xHAAEEEEAAAQQQcLUAAa+ru4/KI4AAArETYE8IIICAWwUIeN3ac9QbAQQQQAABBBBAwJZAhANeW/skEwIIIIAAAggggAACMRMg4I0ZNTtCAAFfCdBYBBBAAAHHCBDwOqYrqAgCCCCAAAIIIOA9ASe0iIDXCb1AHRBAAAEEEEAAAQSiJkDAGzVaCkYAAfsC5EQAAQQQQCB6AgS80bOlZAQQQAABBBBAIDQBckdFgIA3KqwUigACCCCAAAIIIOAUAQJep/QE9UDAvgA5EUAAAQQQQCAEAQLeELDIigACCCCAAAJOEqAuCNgTIOC150QuBBBAAAEEEEAAAZcKEPC6tOOotn0BciKAAAIIIICAvwUIeP3d/7QeAQQQQMA/ArQUAd8KEPD6tutpOAIIIIAAAggg4A8BAl5/9LP9VpITAQQQQAABBBDwmAABr8c6lOYggAACCERGgFIQQMA7AgS83ulLWoIAAggggAACCCBQiAABbyEo9heREwEEEEAAAQQQQMDpAgS8Tu8h6ocAAgi4QYA6IoAAAg4WIOB1cOdQNQQQQAABBBBAAIHSC8Qy4C19bUtZQkZGpqSnZ4RVyu49KbJ95+6wtmUjBBBAILfAgQMHZP/+/a5KuevPNAIIIOA2Ad8EvJmZmfLgmCny0JMvF+ijtt3vk1Oa9ciTJkyZY/Kl7NsvfQePlXPb9JYL2vWVzr2Hy9ZtO806BggggEA4AmXKlJEvv/zSNals2bLhNJNtEEAAAccI+CLgXbx8lVzUvp/MmL+iSPh+N10tC14blZO6tG9p8r4xe6n88ttaWTbjKfl0/gRJTEiQsZNnmnUMEEAAgXAF/vjjD3FLCreNbIcAAgiUKBCjDL4IeC9sfIZMf/5BadPqvCJZax5eVRocWTsnVataSfTfomWrpGObplKrRjWpXClZunZsJbMWfCB6xVjXkxBAAAEEEEAAAQScLeCLgDe5QjmpU7O6VEyuUGRvTLeu/t4/6gWZMGWO/LluU06+P9Zukvr1aufMH1W3lpnetSfFjBkggEDUBdgBAggggAACpRLwRcBbklDrZo3konNPl7p1asj7H30tV9881AS9ehVX7+EtX+7Q/WvlyiaZ4lJS9ptxalqG+CWlZf/gLyMzU3SalOE5B3NQM0CgEAHO96zzXX/8rDw69strP+10xvu8HnciDMMVIOC15Prc2F56dWsnvbu3kzcnDJHKlSrI0pVfSSAQkOQK5eXAwVQrV9b/4HRycnmzIOVAuvglHUzNesJFRqbIAWualOE5B3NQM0CgEAHO96zzPV1fAC2f1PRM37z2++U9zunttA47/pdCgIA3H15SUhmpWb2a7Dtw0KzR+3pz3+Lw1/rNZnmVSslmXLVikvglJZcvY9pcJiEgFa1pUhlHOpSmX0wHM0CgEIHSHFde2japTILRKZeU4JvXfr+8xzm9nebAYxC2QNaZG/bm7thQn72bmpom6enpkpaWLjqtX0dp7TWYnTJtkWzcsk1SrXXzl3wi3/38uzQ+8yRdLXq7w/R5y2Xz1h2yZ+8+eXXGEulw+UXm6q/JwAABBBBAAAEEnChAnRDIEfBFwDvznRXSsNXN5rFkcxZ9aKbnLFqZg/DK9MXSotNAadjyJrn7kUlyd5/zpizvAAAQAElEQVTOcvbpx5v1+niyYxrUleYd+0vjK3qZYLlvzw5mHQMEEEAAAQQQQAAB5wv4IuC9pm1z+X75lDxJr9Jq9+gTGJZOGyMfzH5aFr3xmKxe+oJ069RaV5lUMbm8PDtygHw8b7ysmDVWpk4aah5RZlYycL8ALUAAAQQQQAABzwv4IuAtqRcDgYAcflgV0UeOlUlMlML+Va1cUWpUr1rYKpYhgAACCCDgegEagICXBQh4vdy7tA0BBBBAAAEEEEBACHg5CEIQICsCCCCAAAIIIOA+AQJe9/UZNUYAAQQQiLcA+0cAAVcJEPC6qruoLAIIIIAAAggggECoAgS8oYrZz09OBBBAAAEEEEAAAQcIEPA6oBOogjcFMjIy5Ntvv3VN0vp6sydoVfwFqAECCCAQXwEC3vj6s3cPC2RmZsp3333nmuThrqBpCCCAAAI+F3BMwOvzfqD5CCCAAAIIIIAAAlESIOCNEizFIoAAAmEKsBkCCCCAQIQFCHgjDEpxCCCAAAIIIIAAApEQiFwZBLyRs6QkBBBAAAEEEEAAAQcKEPA6sFOoEgII2BcgJwIIIIAAAiUJEPCWJMR6BBBAAAEEEEDA+QLUsBgBAt5icFiFAAIIIIAAAggg4H4BAl739yEtQMC+ADkRQAABBBDwoQABrw87nSYjgAACCCDgdwHa7y+BuAS8q77+SX7+9a8C0lv+3iHz3v1Y0tLTC6xjAQIIIIAAAggggAAC4QjEJeB9dcZief+jrwrUNzU1Te4Z8Zz8tW5zgXUsQCD2AuwRAQQQQAABBLwgEJeAtyi4Hbv2mFWJiY6qlqkTAwQQQAABBHwrQMMRcLlATCNLvXp7+91PyKdf/ShzFn4oOh1MPQeMkk63DpOTjmsg9evVdjkr1UcAAQQQQAABBBBwikBMA96kMmWkbNkkKV8uSfQqrk4HU43qVWXYoB4y4dEBTrGhHqEJkBsBBBBAAAEEEHCkQEwD3uF39ZSnh/eTgbddI0P6dzPTOq/psSG3S6c2zaRWjWqOhKJSCCCAAAII2BMgFwIIOE0gpgFvsPHtL7tQzjvnFMnIyJS9KfsLpGA+xggggAACCCCAAAIIlFYgLgHv5q07ZPiTr0jTDv3kX5ffXiDt3L23tO1y/PZUEAEEEEAAAQQQQCA2AnEJeCe/MV/emvu+dG7fUkbce4vo7Qy5U3L5crFpPXtBAAEEEIi3APtHAAEEoi4Ql4B34fufyW1dr5Te3dtJu9bnyxUtzs2TkpLKRL3h7AABBBBAAAEEEEDAHwJxCXhPOeFo2b0nxb4wORFAAAEEEEAAAQQQCFMgLgFv146tZc6ij2Trtp1hVpvNEEAAAX8K0GoEEEAAgdAF4hLwzl64UlL27ZemHe6UU5r1KJD40VroHckWCCCAAAIIIICAjwRCampcAt7LmjeW/+t1XZGpfLmyITWCzAgggAACCCCAAAIIFCUQl4C3xYVnSY9rLy0ylSubVFR9WY4AAgjYFyAnAggggAAClkCClWL+PzMzU4pLMa8QO0QAAQQQQAABBDws4PemxSXgvfOBcXJq8xuLTNzD6/fDkvYjgAACCCCAAAKRE4hLwNupTTMZOrB7gVS9WmW5sPFpUoF7eCPXw5SEgG0BMiKAAAIIIOBNgbgEvBc2Pl2uadu8QBp42zXy5bf/8aY0rUIAAQQQQAABdwhQS88JxCXgLUrx7NOPN48r++//1hWVheUIIIAAAggggAACCIQk4JiANyMjUz5c9Z2pfKWKyWbMAAEHC1A1BBBAAAEEEHCJQFwC3iGPvSgXXtU3Tzrt4hvlkbGvSutmjaR+vVou4aOaCCCAAAII+F2A9iPgfIG4BLznnn2yXNfu4jxJ/xDF7BcfljHD+jhfjRoigAACCCCAAAIIuEYgLgHvFS3OlT43ts+T9A9RHH/Mka6Bo6KhCZAbAQQQQAABBBCIl0BcAl5t7IGDqTJ74UoZ+cwbMnT0S/L86/Plr/WbdRUJAQQQQAABrwrQLgQQiINAXALerdt2Stvu98n9o16QV2e8KwuWfiZPPT9DLu1ylyxe/nkcGNglAggggAACCCCAgFcF4hLwjp08U7bt2C0vj71Xvnr3efl84USZ98qj0qxJQxk4bLzs23/Qq9722kUuBBBAAAEEEEAAgYgJxCXg/ejzNdL5qovlnDNOkHJlk0xjjql/hPTt2cFM//oHz+E1EAwQQAABnwvQfAQQQCASAnEJeKtWrii7dqcUqH9hywpkYgECCCCAAAIIIIAAAiEIxCXg1T8tPH3+ctH0x9pNsnP3Xnn/o6/liYlTpXq1ynLCsfVDaAJZEUAAAQQQQAABBBAoWiAuAW+v7lfJ+Y1OlWGjp8jlN9wtTa7sI30Hj5X1m7bK2OH9JKlMYtE1Zg0CCCCAQOECLEUAAQQQKFQgLgFvhfJl5bnHB8lrzwyWoQO7i/7RibHD+8riNx+Xs047rtCKshABBBBAAAEEEEAAATsC+fPEJeD98T9/yJhJ0+Tww6rKNW2bi/7RiZYXni2vzVxins2bv5LMI4AAAggggAACCCAQrkBcAl4NbFd+9q3Uq1MjT70rVUw2z+blsWR5WJhBAIGoCFAoAggggIBfBOIS8K758Te5ouV5kpiYd/etLjrbuP+5bpMZM0AAAQQQQAABBBCIsoAPis8bccaowRXKl5N1G7cW2Nuf6zabZUlJZcyYAQIIIIAAAggggAACpRWIS8B71unHy7S3l8nqH36V9PQM04bNW3fI86/Pk+QK5eWoI2qaZQwQQMAxAlQEAQQQQAAB1wrEJeC9vWtbqV3zMOnSe7ic26a3tO95vzTv2F9WfrZGHhjQTbjC69rjiYojgAACCCDgcQGa50aBuAS8VatUlPmvPCqDbr9W9I9Q1Kl1uHTr1FqmTRomV17SxI2O1BkBBBBAAAEEEEDAoQJxCXjVQm9duPG6y2TMsN7y7MgBcnefznLKCUfrKhICrhegAQgggAACCCDgHIG4BbzxIMjIyMy5Zzj//nXdxi3bJC09Pf8qM797T4ps37nbTDNAAAEEEEAAAVsCZELAEQK+CXgzMzPlwTFT5KEnXy4Av+KT1dL4il7SotNAOaPFTTJt3vKcPCn79kvfwWPNvcYXtOsrnXsPl63bduasZwIBBBBAAAEEEEDA2QK+CHgXL18lF7XvJzPmryjQG/pHLgY99Kzc0bO9rF76guifOH7wiSmydsMWk/eN2Uvll9/WyrIZT8mn8ydIYkKCjJ0806xjECEBikEAAQQQQAABBKIo4IuA98LGZ8j05x+UNq3OK0C56usfRa/idm53sZRJTBT9E8cNjqwtKz75xuRdtGyVdGzTVGrVqCaVKyVL146tZNaCD0SvGJsMDBBAAAEEEIiQAMUggEB0BHwR8CZXKCd1alaXiskVCihu2rpdNMAtWzYpZ92xDerKxs3bzfwfazdJ/Xq1zbQOjqpbS0eya0+KGWvg66ekjc60Bn5qc7httZj4j4BnBMI9D7y2nXW1I6dPvdY22pNpda9zU86Bx0RYAr4IeIuT2bV7r/ljF7nzlCtXVvRHanry69Xf8tZ8cH257MA4JWW/WfT3roMS2xS//e3el2banJaeIbtT0kjFGOyx1ukHAwPmkoHWV5NLqmuqqfXVZGZcMtD6anJJdU01tb6c81mveQdSs/5YUsqBdN+89vvlPc7p7TQnI4OwBXwf8FapXNHc0pBb8MCBg+b2hUAgYILhAwdTc1YHp5OTy5tlNaqWE7+kKslZV8GTEhOkSsUkUjEGla11AXOEuGeg9dXknhqLaH01iYv+aX01uajKxtmR57x1nsW6XuXKJpquSy6X6JvXfr+8xzm9nebAYxC2QELYW3pkw9o1DhO9bSE1NS2nRfojtTq1DjPzervDn+s2mWkd/LV+s46kSqVkM2aAAAIIIIAAAggg4GwBrwe8Rj/d+gpeA9r09HRJS0sXndbn7urKRg1P1JG8OWeppFnr31v5pXlCQ9PzGprlrZs1kunzlsvmrTtkz9598uqMJdLh8oskEHDbNRrTHAYIIIAAAggggIDvBHwR8M58Z4U0bHWzeSzZnEUfmuk5i1aaztYftI175E4ZNf5N8wzeO4eMk/v7d5Ujj6hp1ndp31KOaVBXmnfsb57Vq8Fy354dzDoGCCCAgHsEqCkCCCDgXwFfBLzXtG0u3y+fkifpVdpgt198/pny7dIX5d23Rss3SyZL56taBFdJxeTy8uzIAfLxvPGyYtZYmTppqHlEWU4GJhBAAAEEEEAAAQQcLZAn4HV0TaNcucTEBKlXp4YkJZUpdE9VK1eUGtWrFrqOhQgggAACCCCAAALOFSDgdW7fUDMEEIifAHtGAAEEEPCQAAGvhzqTpiCAAAIIIIAAApEV8EZpBLze6EdagQACCCCAAAIIIFCEAAFvETAsRgAB+wLkRAABBBBAwMkCBLxO7h3qhgACCCCAAAJuEqCuDhUg4HVox1AtBBBAAAEEEEAAgcgIEPBGxpFSELAvQE4EEEAAAQQQiKkAAW9MudkZAggggAACCAQFGCMQKwEC3lhJsx8EEEAAAQQQQACBuAgQ8MaFnZ3aFyAnAggggAACCCBQOgEC3tL5sTUCCCCAAAKxEWAvCCAQtgABb9h0bIgAAggggAACCCDgBgECXjf0kv06khMBBBBAAAEEEEAgnwABbz4QZhFAAAEEvCBAGxBAAIFDAgS8hyyYQgABBBBAAAEEEPCggK8DXg/2p2eblJGRIWvWrHFN0vp6tjNoGAIIIIAAAi4TIOB1WYf5tbqZmZmuCXY1MPdrP9Fu1wpQcQQQQMDTAgS8nu5eGocAAghERiA1NVXS0tJck7S+kWk5pSCAgBcE7Ae8XmgtbUAAAQQQCEsgISFBlixZ4pqUmJgYVjvZCAEEvClAwOvNfqVVCCAQRQG/Fr19+3ZxS/JrH9FuBBAoXICAt3AXliKAAAIIIIAAAggUL+CatQS8rukqKooAAggggAACCCAQjgABbzhqbIMAAvYFyIkAAggggECcBQh449wB7B4BBBBAAAEE/CFAK+MnQMAbP3v2jAACCCCAAAIIIBADAQLeGCCzCwTsC5ATAQQQQAABBCItQMAbaVHKQwABBBBAAIHSC1ACAhEUIOCNICZFIYAAAggggAACCDhPgIDXeX1CjewLkBMBBBBAAAEEEChRgIC3RCIyIIAAAggg4HQB6ocAAsUJEPAWp8M6BBBAAAEEEEAAAdcLEPC6vgvtN4CcCCCAAAIIIICAHwUIeP3Y67QZAQQQ8LcArUcAAZ8JEPD6rMNpLgIIIIAAAggg4DcBAt6iepzlCCCAAAIIIIAAAp4QIOD1RDfSCAQQQCB6ApSMAAIIuF2AgNftPUj9EUAAAQQQQAABBIoViFDAW+w+WIkAAggggAACCCCAQNwECHjjRs+OEUDAkwI0CgEEEEDAcQIEvI7rEiqEAAIIIIAAAgi4X8BJLSDgdVJvBiHV8wAAEABJREFUUBcEEEAAAQQQQACBiAsQ8EaclAIRQMC+ADkRQAABBBCIvgABb/SN2QMCCCCAAAIIIFC8AGujKkDAG1VeCkcAAQQQQAABBBCItwABb7x7gP0jYF+AnAgggAACCCAQhgABbxhobIIAAggggAAC8RRg3wiEJkDAG5oXuRFAAAEEEEAAAQRcJkDA67IOo7r2BciJAAIIIIAAAgioAAGvKpAQQAABBBDwrgAtQ8D3AgS8vj8EAEAAAQQQQAABBLwtQMDr7f613zpyIoAAAggggAACHhUg4PVox9IsBBBAAIHwBNgKAQS8J0DA670+pUUIIIAAAggggAACuQQIeHNh2J8kJwIIIIAAAggggIBbBAh43dJT1BMBBBBwogB1QgABBFwgQMBrs5N270mR7Tt328xNNgQQQAABBBBAAAGnCMQi4HVKW4usR9vu98kpzXrkSROmzDH5U/btl76Dx8q5bXrLBe36Sufew2Xrtp1mHQMEEEAAAQQQQAAB5wsQ8Gb3Ub+brpYFr43KSV3atzRr3pi9VH75ba0sm/GUfDp/giQmJMjYyTPNOgYIIIBAaALkRgABBBCIhwABb7Z6zcOrSoMja+ekalUrif5btGyVdGzTVGrVqCaVKyVL146tZNaCDyQzM1NXkxBAAAEEEEAAAQRCFYhxfgLebPDp81fI/aNekAlT5sif6zZlLxX5Y+0mqV+vds78UXVrmelde1LMmAECCCCAAAIIIICAswUIeK3+ad2skVx07ulSt04Nef+jr+Xqm4eaoFev4uo9vOXLlbVyZf0vVzbJTKSk7DfjHXsOil/S3v1pps1p6Zmyd19aTJPZMQO/CdBeBEolEI3XqdTUDFOn/QfTffPa75f3OKe30xx4DMIWIOC16Prc2F56dWsnvbu3kzcnDJHKlSrI0pVfSSAQkOQK5eXAwVQrV9b/4HRycnmzoGL5MuKXVC4p63BJsEblyiZILJPBZoAAAgiEIBCN16jExICpQdkyCb557ffLe5yz21nGHHcMwhewQpfwN/bilklJZaRm9Wqy78BB0zy9rzf3LQ5/rd9slleplGzGSdaLnl9SmcSswyXB+iCg07FMBpsBAgggEIJANF6jEhKyAl4d++W1n3YmiBMMhH+lEsiKYEpVhLs31mB2yrRFsnHLNklNS5f5Sz6R737+XRqfeZJpmN7uMH3ectm8dYfs2btPXp2xRDpcfpG5+msyMEDAIQJUAwEEEEAAAQQKF/B9wKssr0xfLC06DZSGLW+Sux+ZJHf36Sxnn368rhJ9PNkxDepK8479pfEVvSQ1NU369uxg1jFAAAEEEEAAAccJUCEECgj4PuDVJzAsnTZGPpj9tCx64zFZvfQF6dapdQ5UxeTy8uzIAfLxvPGyYtZYmTppqHlEWU4GJhBAAAEEEEAAAQQcLeD7gFd7JxAIyOGHVRF95FiZxEQp7F/VyhWlRvWqha1imRsFqDMCCCCAAAII+EaAgNc3XU1DEUAAAQQQKCjAEgT8IEDA64depo0IIIAAAggggICPBQh4fdz59ptOTgQQQAABBBBAwL0CBLzu7TtqjgACCCAQawH2hwACrhQg4HVlt1FpBBBAAAEEEEAAAbsCBLx2peznIycCCCCAAAIIIICAgwQIeB3UGVQFAQQQ8JYArUEAAQScIUDA64x+oBYIIIAAAggggAACURKIe8AbpXZRLAIIIIAAAggggAACRoCA1zAwQAABBOIuQAUQQAABBKIkQMAbJViKRQABBBCIr0BGRoZ8+umnEU1r1641jfrpp59kzpw5EU1aX1M4AwR8LxB5AALeyJtSIgIIIICAAwQyMzPlt99+i2javn27adnGjRvlm2++iWjS+prCGSCAQMQFCHgjTur8AlNSUiQ1NTXklJaWZhqXYb2J6HSs0oEDB8x+GSCQW4BpBBBAAAEE7AoQ8NqV8lC+smXLypNPPhlymjdvnlHYsGGDzJ07N2YpKSnJ7JcBAggggAACCBQQYIENAQJeG0hezKJXeUNNwSutGRnpotOxSl70p00IIIAAAgggEDsBAt7YWbMnBOInwJ4RQAABBBDwsQABr487n6YjgAACCCDgNwHa608BAl5/9jutRgABBBBAAAEEfCNAwOubrqah9gXIiQACCCCAAAJeEiDg9VJv0hYEEEAAAQQiKUBZCHhEgIDXIx1JMxBAAAEEEEAAAQQKFyDgLdyFpfYFyIkAAggggAACCDhagIDX0d1D5RBAAAEE3CNATRFAwKkCBLxO7RnqhQACCCCAAAIIIBARAQLeiDDaL4ScCCCAAAIIIIAAArEVIOCNrTd7QwABBBDIEmCIAAIIxEyAgDdm1OwIAQQQQAABBBBAIB4Czg544yHCPhFAAAEEEEAAAQQ8JUDA66nupDEIIOBVAdqFAAIIIBC+AAFv+HZsiQACCCCAAAIIIBBbgbD2RsAbFhsbIYAAAggggAACCLhFgIDXLT1FPRFAwL4AORFAAAEEEMglQMCbC4NJBBBAAAEEEEDASwK0JUuAgDfLgSECCCCAAAIIIICARwUIeD3asTQLAfsC5EQAAQQQQMDbAgS83u5fWocAAggggAACdgXI51kBAl7Pdi0NQwABBBBAAAEEEFABAl5VICFgX4CcCCCAAAIIIOAyAQJel3UY1UUAAQQQQMAZAtQCAfcIEPC6p6+oKQIIIIAAAggggEAYAgS8YaCxiX0BciKAAAIIIIAAAvEWIOCNdw+wfwQQQAABPwjQRgQQiKMAAW8c8dk1AggggAACCCCAQPQFCHijb2x/D+REAAEEEEAAAQQQiLgAAW/ESSkQAQQQQKC0AmyPAAIIRFKAgDeSmpSFAAIIIIAAAggg4DgBFwe8jrOkQggggAACCCCAAAIOFCDgLWWn7Nu3T9LT012TUlJSStliNkcAAccJUCEEEEAAgWIFCHiL5Sl5ZVJSkowaNco1qVy5ciU3ihwIIIAAAggggIALBYqqMgFvUTIhLD948KC4JYXQLLIigAACCCCAAAKeECDg9UQ30ggEELAvQE4EnCugt8lt2LBB3JK0vs7VpGYIHBIg4D1kwRQCCCCAAAJxFdDb5CZNmiRuSWXLlo2rFzsvpYCPNifg9VFn01QEEEAAAQQQQMCPAgS8fux12oyAfQFyIoAAAggg4HoBAl7XdyENQAABBBBAIH4Ceh/v5s2bxS1J6xueFlu5WYCA12bv7d6TItt37raZm2wIIIAAAgj4Q0DvO54wYYK4JXHfsT+Oy/ytJODNL5JvPmXffuk7eKyc26a3XNCur3TuPVy2btuZLxezCGQJMEQAAQQQQAAB5wkQ8JbQJ2/MXiq//LZWls14Sj6dP0ESExJk7OSZJWzFagQQQAABBHwtQOMRcJQAAW8J3bFo2Srp2Kap1KpRTSpXSpauHVvJrAUfSGZmZglbshoBBBBAAAEEEEDACQIEvCX0wh9rN0n9erVzch1Vt5aZ3rUnxYwZlEKATRFAAAEEEIiDgP511LS0NHFLOnDgQByUvLVLAt5i+lOv4uo9vOXLHXqwdrmySWaLlJT9ZiyBgDRt2tQ1SeurKZw6n3LKKabNVapUkVNPPTVmyezUco7lPku7L+ocm+MDZ5yLOlejdWzUqpV10ePoo4+O6Ou+vibra7MmnXZL0vpqckt9tZ5a34TERPnyyy9dk/SHgeaYZhC2AAFvMXSBQECSK5SXAwdTc3IFp5OTy5tlO/akyWlnNXFN2r47VcKtc4/LLpIv7m0p03q3kG6XXhSzdNaR1eVsK8Vyn6XdF3WOzfGBM85FnavROjYm3NjcvA4OuaZpxF/3S/P6HK/3ITfX+dgTG4pb0jbrvdsEHQzCFiDgLYGuwZG15c91m3Jy/bV+s5muUinZjGtULSexSewHZ44BjgGOAY4BjgG/HgMm6GAQtgABbwl0rZs1kunzlsvmrTtkz9598uqMJdLh8ousb0QCJWzJagQQQACBqAhQKAIIIBCiAAFvCWBd2reUYxrUleYd+0vjK3pJamqa9O3ZoYSt4rs6LT1dNm3ZLhs2/S3p6Rm2K6NtW7dxqxzMdQuH7Y3JWCoBvVVm7YYtsm1HaH/c5O/tu0RTqXbOxhEXSAvzHIx4RSjQtkC456DtHZAxpgLhnIP6m52du/bGtJ7sLHYCXg14IyZYMbm8PDtygHw8b7ysmDVWpk4aah5RFrEdRLigqXPflzNa3CQXdxogLa/9t7S67t/y3c+/F7uX3//cIF37jpCGrW6WS64bJLMWriw2PysjK3D/qBfkrEtukdad/08uvKqv6YsdO/cUuZOMjEx5/vX5Ju9F7fvJpV3uysmrAfMpzXpI/vTpVz/k5GEiugKhnoPa5/n7S+f/+/u66FaU0nMEQjkHZ1uvj9o/+dPAYRNMeZyDhiGug1DPQb1ApH9gqmmH/uZ9U98Pf/zPH3FtAzuPvAABr03TqpUrSo3qVW3mjl82/ZHdxFED5fOFk+ST+RPkn0fXkzETpxVZIT3R23S7V2rXPExeHXeffLHoOdHbOIrcgBURF9BH3U1/bph8894LsvD1UfK/vzbItHnLitzPk89Nl1emL5bbu7WTlXPGybxXRuTk1SeL6MzEUf+WBa+NyklnnPxPXUyKgUCo5+AbE4bk9JP22egHepla6nO/zUSpBxRQkkAo52Cri87J01/aZ6effKwcflhlsxvOQcMQ10Go5+ATE6eKXuH/+O1nrPfN8XL0UXVk7OQZcW0DO4+8AAFv5E3jWuKVlzSRCxufLskVyon+sK6KFahXq5r1QiyF/Ht52iKpXq2yjBx8q5x12vFSoXxZOayY/IUUwaJSCtzW9Uo5+fijJalMohxR63BTWrUqlcw4/2DL3zvkxbcWyMDbrpHrO7Q0fVenZvX82eTII2qI/uAymLRfC2RiQVQEQj0HNdgK9pOO5y7+0PxOQD+ERqWCFFpAIJRzsFLFCnnOrZ279si3P/wqXTu2zlMu52AejpjOhHoOrt/0t9Q8vJokJZWRMomJ1nvhceYvrMa00uws6gIm4I36XthBzAXefvcj6f/AM/LDL/+TW29oU+T+P1y1RurWriGDHnxWrr3tQRk2eops3LKtyPysiI6A3jc98ZW3pdudj8qZpx0nl7c4Vwr79+0Pv5nF3/30u7n14eZBj4v2tVmYazBm0jTRr2n1SvDO3XtzrWEyVgLaL3bOwdz1+fybn2TlZ2ukV7e2uRczHQMBu+dg/qqMsb5xua7dxVK/XtbzeYPrOQeDEvEb2z0He153mcxZ9KH0G/K0LPv4a3PLWO/uV8Wv4uw5KgIEvFFhjX+hv/2xwfyYKT09Q3btLvqvwv36x3rR+5RbXHCW9Ox8mbnft+eAUebHefFvhX9qkJ6Raa4o6NUi7a/de/cV2vjgh5Eah1eVG6+9VM4+/Xi5d8Tz8s7ST01+/cMoXdq3EP2KVa/c672+PawgWt/MTQYGJQlEbL3dczC4Q/0qfPSzUyZifOgAAA0lSURBVKVbp9ZSt06N4GLGMRKwew7mro5+ONEPKbfecGXOYs7BHIq4T9g9B0/4Z31z1T4hkCB3DZ8ku/ekSMNT/hn3+lOByAoQ8EbW0zGl9b+lo7knVx+h9u8Hxxdbr+s7tBL9Cqh1s3/J40Nulz/WbpLf/txQ7DasjKyA3nIwZlhveefVkVKmTKKMf2l2kTs4tkFd6wpgO7nY+pDSq1s703dLVnxh8uvXrYPv7Cq3XN/G3PbwytP3mUD6p//+adYziJ1AKOeg1mrph1+ZD5w3db5cZ0kxFgjlHNSq6cWEMZOmivZX7ttPOAdVxxnJ7jk4cOh4adOqiTz10B3y/vQx0qjhSdK593BJS093RkPiVgtv7ZiA11v9WaA1/6h/hHnUVVEn7knHNcjzhzUyMrIeY3YwNa1AWSyIvkAgEJBjrD4LXsnNv8cjj6gpelU+Ne3QC3GaNZ2aVnh/1apxmCli34GDZswg9gIlnYNaIz0/9Svw27u1dcWPY7XOXk2BQPHnYLDdi5avMh8mb7S+Dg8uK2zMOViYSmyXFXcO7k3Zbz5onnjsUaZS+mNR/RCjjyjTJxiZhQw8IUDA64luPNSICVPmyOoffpX9VoCjz9R9aepCaXzmSeZGfM01cNgEGT1xqk6adHmLxuZHUJpX7/XUP6xRvVpl83QHk4FBVAX0j5looKMvrKlW4Kp9N3vhh9LojBPNfvWrtY63DJWF739m5s887TjRXyBPeuVt84xlza/rzm90mlm/4pPVsth6I9a+1BfssZNnmvwnWl/ZmQwRHlBcQYFQz0Et4e3FH8mWv3ea2xl0nhQ7gVDPQa2Z3iKkv+zvc2P7Aj/y5RxUofimUM5BvaVPLyTok3H0dVOfR79g6afmFgcNlOPbEvYeSQEC3khqOqAsDVy7WF/FnN36VvNM3cSEBHnorp45Nfv9z/WyfuPWnPkbOrSSxmedbPI2ubKPfPDZahk/or95WkNOJiaiJhAIBOTjL74XfTRcw5Y3ifbdJU3PkR7XXmr2mZGRKfo8yB279ph5ffLG08P7ysvTF8vpLXqa/HrP7jVtm5n1B1NT5f5RL4r2ZaPLbrcC5U9l3MP9RB+rZzIwiLpAqOegPg5JHzV36w1t6Keo907BHQQCoZ2DWoI+q3z3nn3S9epWOpsncQ7m4YjLTKjnoN7KULZsknndbNL2DnNL36jBt4k+sSGEBpDV4QIEvA7voFCr98g9N8vX7z4vi998XD6a+4y89sxg0U+vwXJmv/iwjBnWJzgrepLrcz/1mb3vTX1Clk4bY37wlJOBiagK6NWFGc8/KKsWTDTP4NXnJ2sf6g9fdMdVq1SU75dPkc5XtdBZk8475xTzrMh33xotny+cKHrPbvCFWZ8R+sn88fL+9CdN+mD203Lu2Seb7RjERuCREM9B7euVc8aJ3ncdmxqyl9wC4ZyD+lQGPff06+/cZek056AqxDeFeg7qrX1PD+9nXof1fVD/2NRpJx0T30aw94gLEPBGnDT+BWoQq0FutaqFP8u1sBrqlcMjah8ugUCgsNUsi4RAMWXom279erVFn59cTLacVRrg1qtTw8pfPmdZcELX6Y9oNAUC9GfQJZbjcM7BWNaPfRUUCPUcLFjCoSWcg4cs4jUVzjmox4BeZIhXndlvdAUIeKPrS+kIIIAAAgggkE+AWQRiLUDAG2tx9ocAAggggAACCCAQUwEC3physzP7AuREAAEEEEAAAQQiI0DAGxlHSkEAAQQQQCA6ApSKAAKlFiDgLTUhBSCAAAIIIIAAAgg4WYCA18m9Y79u5EQAAQQQQAABBBAoQoCAtwgYFiOAAAIIuFGAOiOAAAIFBQh4C5qwBAEEEEAAAQQQQMBDAr4MeD3UfzQFAQQQQAABBBBAoAQBAt4SgFiNAAIIeFiApiGAAAK+ECDg9UU300gEEEAAAQQQQMC/AiUHvP61oeUIIIAAAggggAACHhAg4PVAJ9IEBBCIjQB7QQABBBBwpwABrzv7jVojgAACCCCAAALxEnDdfgl4XddlVBgBBBBAAAEEEEAgFAEC3lC0yIsAAvYFyIkAAggggIBDBAh4HdIRVAMBBBBAAAEEvClAq+IvQMAb/z6gBggg4AKB1T/8Kjfc8YjMX/KJGZ/SrIf06D9S/ly3SRYs/Uyuve1BaXTZ7TLi6ddlw6a/c1qUnp4hL09fLG273ye6Tfue98vi5Z/nrP/ki++l4y1DzbbB9W+/+1HO+n37D5r9vTX3fRn00LMmX9e+I+TdFV/k5GECAQQQQKB4AQLe4n1Yi0CMBNiN0wV27d4rX3/3H7n7kUly1mnHyX39rpdf/7dOLrv+bhk6+iU5/1+nSp8br5LZC1eaADfYnrGTZ8gzL86WZk0aytjhfeXYo+vJwGHjRQNozbNrz1457aRj5P7+XWXMsD5y/LFHyb0jnpev1vxHV0taWprZ7/AnX5GEQEAG3tZJKiaXkwFDn5Fde1JMHgYIIIAAAsULEPAW78NaBBBAII/ArBeGW0HnNXJ9h1Zy43WXmXXzXhkh/W66Wnpcc6l0ad9Cln30tVn+9/Zd8sK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       ],
       "text/plain": [
        "Figure({\n",
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "351fb1c4",
+   "id": "69b5f4de",
    "metadata": {},
    "source": [
     "## Shading p-values"
@@ -273,7 +273,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "d94931ff",
+   "id": "9c5757d4",
    "metadata": {},
    "outputs": [
     {
@@ -281,7 +281,7 @@
       "image/png": 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Null Distribution\"},\"xaxis\":{\"title\":{\"text\":\"diff in props\"}},\"yaxis\":{\"title\":{\"text\":\"count\"}},\"shapes\":[{\"line\":{\"color\":\"#d62728\",\"dash\":\"solid\",\"width\":2},\"type\":\"line\",\"x0\":0.03399999999999992,\"x1\":0.03399999999999992,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"},{\"fillcolor\":\"#d62728\",\"line\":{\"width\":0},\"opacity\":0.3,\"type\":\"rect\",\"x0\":0.03399999999999992,\"x1\":0.07509999999999994,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"}]},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -337,7 +337,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "950146a4",
+   "id": "661aa2d7",
    "metadata": {},
    "source": [
     "## Simulation vs. theory overlays"
@@ -346,7 +346,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "a1ea5456",
+   "id": "c018d339",
    "metadata": {},
    "outputs": [
     {
@@ -354,7 +354,7 @@
       "image/png": 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",
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Distribution\"},\"xaxis\":{\"title\":{\"text\":\"mean\"}},\"yaxis\":{\"title\":{\"text\":\"density\"}},\"bargap\":0.02,\"showlegend\":false},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -386,7 +386,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "cd09e3c1",
+   "id": "be6a4a77",
    "metadata": {},
    "outputs": [
     {
@@ -394,7 +394,7 @@
       "image/png": 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Bootstrap Distribution\"},\"xaxis\":{\"title\":{\"text\":\"mean\"}},\"yaxis\":{\"title\":{\"text\":\"density\"}},\"bargap\":0.02,\"showlegend\":false},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -431,7 +431,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1720c08f",
+   "id": "162fb3c4",
    "metadata": {},
    "source": [
     "## Faceted regression-coefficient plots\n",
@@ -442,7 +442,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "8750acfc",
+   "id": "7400ec80",
    "metadata": {},
    "outputs": [
     {
@@ -450,7 +450,7 @@
       "image/png": 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TDtusXdrrAoBOjYB8BwcDX1a2hbEHDwDj9FLShVcHAqbTmgVU4zcCbPNe2dLShwN6QPe5gy7rAD14saEdzw8Csn7hVe5GlwE8Aou3TTMHAqjaHSEzGs8AdH5u7ZxS8wKnw2XdH9rvAddzez81TtMCClr+wyYHAwBYHKHig3MPAvkcDE\\u002fDcwsDiZxRU\\u002fzW9wDoroaYeE7fAG\\u002flwoe8Vo8ChSeYn6vPKwJ8TszbZvMDAupmlCw+1sMAevQHXjsO7wI0bi8QoTMLAEoZPCEvqtMBq9yiLpw6YQGC8Pt6iD7TAzttoueYcvcD4Ij6HPti+wFMcm69r28fAWuzOx26ms8DAOSRz8Z6nwKZQ9RUbVrjAmCRkrSvTwMCk16s6KBPCwLx9r8VWdr3ANrhzTPuRtcA+mGRuVmu7wPJDW204XITA4psiyHQIucCb+0qkHRPAwAy1IxBKdrvA51I6JE1kw8DGejnOq13HwF+fXR6hpq3A+Az2Ga5WwsAsKsQ6QXXHwHzZ5W5QjrPA+zBRwMyIwsBt8eWW6g+lwILamDwj57LAL2vI3PDVyMDkEdQ6VRSxwE\\u002fSjqWQvcXANWVJ20BbuMDeNhwW8wHAwMooqDnuk8TAGE931o+8pcDB0MLHGum3wHk10PJ6ibTAUhXyFmP9wsD7r+Fggdi0wAfJakXF0MfA0RBwMxSVw8Aryj0FdxPIwFIfTU6zHsrAeMosMmVYuMASW2S6adipwN7UoZJntL\\u002fAlBfJtnumwcA8BmJPUbrMwPjD1M1BS8bAvywNR+crvcC7krb6ZrG9wAc5qD7l7MbAoVWA5cdlwcC+hC541au4wK7XwMW9kq\\u002fAFMzZZIixysBIK8J1TKm1wP3378fMc7nApywKli7JwMDPg+kCH2m2wHjDXk6QcKfAy42di2Kbq8DIjxxaYWbGwF7I+OBscrzACH6aBr\\u002fducDGRJFjLjXGwF6XsnHabpXAG5L1XMr8ssBwJSE6C3e8wDSX3YwqmsbAQc4bUBs4vsBO0udWEwuxwC5uLMvd\\u002fMHAK+mO6dyIt8A6rf5KFh7DwBYPnmnWA8HAHipKAfIguMDtAXa\\u002fj8++wDOJiNJ2w8nASZkKe54KsMC6OhLzXcm\\u002fwJKFTWc0psLAN876HuYexsAiidiQ7y7CwIEeb0u8IsTAi\\u002fWqJ1xRtMBLy3WiBtvJwN4AQLlyGsPAwL1fkdVwvsAeYOJWHz+zwN+oparJJ7jAX9lzoW\\u002f7usBuM1sDR7C\\u002fwAKUwBFAw6nAWQtNNjg7uMDjPSoI3Qa4wHAjBuiC1bbAyNRhNnM0w8D2DfnA3EykwAbVjAaF3rvAOAXMrSlwucD9eDAm82nDwFMOiY9737jAOI32yHFZusD+zHxsUdS\\u002fwLx5AV94N8bAxdKcD+cOuMCUqas8PbvFwBMMDTYf5avAuQmb+OQZxMAA1A+FM8S8wEA8uLkAVMLAkuj0L1dNvcAsVf5W5H3JwIMDIK0Tr77Awo12llZ7wcDcTLKRHETGwBKNiRaNuLTAXPta7n62ysAXkX6MX2yWQLi5NWV4FL7Au6FhACCsssCeOyBw2ffBwCDek7FPJL3AgOL4OCX0tcAOBrOaHE\\u002fDwBv9OLUdg8bAYeb7mWn0w8ACF0FEVs7EwNodizbiwMfAz\\u002fOh3m2fwsCYypvU18a9wL4EU0xOe7PAnUoZBNkRu8Cu2h0NyGi4wDTnoZXQrcPAYOr6N8E4tMBp+K4mVPDFwPhYosLy+J3A3bkiME+qwMAwN9dgC0OqwDr74p8P08LAmj6UScMbwMB+AqYK5RfBwF6tv3V+BcrAJ15oi\\u002fnyxMD4uxZFZue4wGoqQBYjY8DA+a26no3VwsDvJDHGf4\\u002fGwJj5hg\\u002frMcnACNp354XsnsBu5DTmz8u8wCa3y6DEt8LABWkwBIb9w8BWhEl7mD6SwO0EnwkiybLAkJW\\u002ftSSBwcD6jlr8coy\\u002fwBaOU7ZyrLjAEZt5E5OEtsDrkPPAVl\\u002fAwCEu75ity8bAxPfBhcJGuMA\\u002f9wI2+BSwwFG5Cc1RUa3AtGjtH306wMBu+u+rSD3CwAKjE+J7arrAA8TY8hKkusCKlK1EC3S+wPDzMVRo973AVVzxgJJhxMAswgnd9Zy5wMqd29c+fsXALzgVlXiCo8C6KbcBPTLHwGxnrxia5cLAjK\\u002fBH8QtwcBF3mI9wxzKwE3JDSslAMDAmjkqAvJPxcAt\\u002fX1Jqd3LwIYNyI1DS73ATB3+l0TosMD0VZB8CS\\u002fHwKsp3Hm42K7Ax+80R5vMwsDakpxUln\\u002fCwE4lEJZ9O8LAqAUlECXGfsADAosZScfBwMNqGmDjdMzAygxRm0aCucCigCA0WsutwBTNyF4hvMrAsFTPBodrucBpb90Ow3G1wEIMwDoEoMTAeUPHF+PYz8CPO3Lie8e5wI8L8Np7E7zAbh5BjmROoMAnWDL7W1WzwP7ygY0dz8PAlKkFXlBIuMD2z2eICie\\u002fwCwsXjiU1r7ABoifjpbHtMBeYXky5Fe+wAqwbF9nQ7PAfmkSe0Gut8BKGmXAmACzwAn5vUKWAMjAC7B9T8OjlsBZMwmCapS0wMO0xfezw77ANDFF7mhGz8AHLwv8AzHBwG9V0wJsybjAOTPFA7Hux8AINe4wtDO4wOhJ7+jtwGnAUtOK7+pvsMCkWjaavw7EwKYR1RJDtcHAHMyV9ShewMAzQ6C4s1fFwMFVFQwav7bANMQxVJsIucB57Nv6cFvCwM61jhox0YPALba\\u002faGUxw8CxbwMsb5jDwKVqD0xyhsLA3EtG8XfntsBl464Ir5S4wBOC+QgzycHA6MGmoIF4isDCDMnOyQq3wHCzXnIv9sDADNB7rVuFusDae5CMikG\\u002fwENTW+guAsHA2p533m6VmMAoonUN8nbDwF\\u002fdungTLMTACY\\u002fgzL2R0cBldla3aEbBwFgaVk8gD37ASx\\u002fy\\u002feluxMAUoVC4mIaywCepU2+pEMXAh2NfJkamtsAFRBEb75q5wPBTn8Chy7bABZ2p3yrwssDDIPk4M8y4wIsw5Wr1BsbAHmJlLhfMmcA2FpSsNyi5wCPaRb0fY8LAfnLWvh7IpsBKFkzVRku8wLhsIvUJ28jAZcoXfbvswMBWKumlWfe\\u002fwDBvvFoOQMzA1BQftskSxcCK6nbMU0i1wAckb0fyYMHAwHR9H6jpwsDK4cVLiNnBwOxKtWpyWMHAzhX0GtUBy8BC5L7PfFDDwOD9A\\u002fA6drfA8GL+2w06ucDKQGC1ra2ywIDYZFk8WcHAgqmJTvnat8AYlq5MqOCswHZHQ+kdNLbAgcBxBUk0tcCMhTJJJuS+wFvNdi3Fcb\\u002fASIFBQB2KxMDPPKxWqMuywPpPAYtObr3AosmW5gJPqMDPTa9BaG69wJA0daF0MLnAPNZKoVUdtsCoJFBgzXazwByE\\u002f7PiKL7AjABsxOCrycBpwqoGX4+3wBytuPa9G7nAVwngzLNtu8AxAdy8AkPEwDHQ4+Q\\u002fesPAgWHFlcRdqcBZZpMoGYShwCLde+deD7PAbjIHC0Y+vcCqnqyYyTK8wK7r60xFDMDAS207pLpRwcDyh2QxlXW4wKJdJ6zagMHAxvfSbKHksMDXp0S7vGulwBWmEAjURcjApoUw12mHvsCVm0d969+zwFZbBCcOSq\\u002fAHwExm0NywsBRg6ZTAXyuwB4EedHDTcTANNx6xk6OpMAv0t38bWDIwL+GxK4Vo77As+Szeyp2vsDqFQaaUPXFwJ8sWbwB6arARrsdmgtrsMBylsx\\u002fJZjBwGCIQsJ09K7AuCLZwrMPxsBxkPkwZC26wK2S74TRsMbA1yZFeJGNvcAWWi3lqHi7wE+8SEW458nAbi\\u002fdSZoIw8A5AgRzx8S5wJLgdnC9VsPA\\u002fuZMuxA0ssDQUpY5O\\u002fGkwPCjO2L03L\\u002fAG9I\\u002f5kReqsCv9oyJnp\\u002fEwNdw4RspYMHAIPsiU680wsCtwtDBp9iiwKNEyXew+cjAvf1pInt+wcCE7xFQAcO5wMh7s3dUsbDA\\u002fEEPj4xvrsDaBpcEftC8wERvpnVCtcLAFH+Tu4HEuMBUwwIYgKGUwECBUIxXgsLAyFQkFqkQtMB3Xl0qek7AwB8WMWgpY8DAsfbBNSCspsCM1JTKrQTBwIzIGHvyIrjAVPFM5\\u002foimsA2zlAxlgK7wJINhyihbLnAMdfkWmD4tsDaxha8GDa9wO6gQRHDobDAN3JCf2cSqsBR\\u002fjPieh65wA5j5O+6eMLA8NedKe9Fq8AcHa8iywejwI4\\u002fqB6SirDAx8yIDA7twsAIXd+Q0cy\\u002fwFTGJDRkj7bAHBXH7iz4wMDetdiT5Ri0wDZKNak6mb\\u002fAp0f+SavTusCnJXgYbTuywG7WgwnSO7fAVIWBBGmRuMAwCKRtoFzDwAYomp\\u002fPG7bAgIcVGuCavcARbn3rInq6wJy11J8q6bTAYlb1ajCYtMAZMR7rkALLwJmtKDhUHrjABK\\u002fe4plKtsBmW2gm50+8wIB\\u002fz\\u002fnO+qrAAF6mCR3vxsAbwAMugEGzwKZZkw4XscLA6XjU+So5wsBi7v+tCMCcwFexwWn9P7\\u002fA1wLPEZB2wcB1YegbDxumwAw1MvQIZLrAXHfO5XuotcAoQvJxKsLAwOgdNP9YV7TADJYHiD9cu8BNbrCVxwvGwGm3nfGLVsbAQ3ChnTtyxMCY+Lx7BFKzwNCk6a\\u002f3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TDtusXdrrAoBOjYB8BwcDX1a2hbEHDwDj9FLShVcHAqbTmgVU4zcCbPNe2dLShwN6QPe5gy7rAD14saEdzw8Csn7hVe5GlwE8Aou3TTMHAqjaHSEzGs8AdH5u7ZxS8wKnw2XdH9rvAddzez81TtMCClr+wyYHAwBYHKHig3MPAvkcDE\\u002fDcwsDiZxRU\\u002fzW9wDoroaYeE7fAG\\u002flwoe8Vo8ChSeYn6vPKwJ8TszbZvMDAupmlCw+1sMAevQHXjsO7wI0bi8QoTMLAEoZPCEvqtMBq9yiLpw6YQGC8Pt6iD7TAzttoueYcvcD4Ij6HPti+wFMcm69r28fAWuzOx26ms8DAOSRz8Z6nwKZQ9RUbVrjAmCRkrSvTwMCk16s6KBPCwLx9r8VWdr3ANrhzTPuRtcA+mGRuVmu7wPJDW204XITA4psiyHQIucCb+0qkHRPAwAy1IxBKdrvA51I6JE1kw8DGejnOq13HwF+fXR6hpq3A+Az2Ga5WwsAsKsQ6QXXHwHzZ5W5QjrPA+zBRwMyIwsBt8eWW6g+lwILamDwj57LAL2vI3PDVyMDkEdQ6VRSxwE\\u002fSjqWQvcXANWVJ20BbuMDeNhwW8wHAwMooqDnuk8TAGE931o+8pcDB0MLHGum3wHk10PJ6ibTAUhXyFmP9wsD7r+Fggdi0wAfJakXF0MfA0RBwMxSVw8Aryj0FdxPIwFIfTU6zHsrAeMosMmVYuMASW2S6adipwN7UoZJntL\\u002fAlBfJtnumwcA8BmJPUbrMwPjD1M1BS8bAvywNR+crvcC7krb6ZrG9wAc5qD7l7MbAoVWA5cdlwcC+hC541au4wK7XwMW9kq\\u002fAFMzZZIixysBIK8J1TKm1wP3378fMc7nApywKli7JwMDPg+kCH2m2wHjDXk6QcKfAy42di2Kbq8DIjxxaYWbGwF7I+OBscrzACH6aBr\\u002fducDGRJFjLjXGwF6XsnHabpXAG5L1XMr8ssBwJSE6C3e8wDSX3YwqmsbAQc4bUBs4vsBO0udWEwuxwC5uLMvd\\u002fMHAK+mO6dyIt8A6rf5KFh7DwBYPnmnWA8HAHipKAfIguMDtAXa\\u002fj8++wDOJiNJ2w8nASZkKe54KsMC6OhLzXcm\\u002fwJKFTWc0psLAN876HuYexsAiidiQ7y7CwIEeb0u8IsTAi\\u002fWqJ1xRtMBLy3WiBtvJwN4AQLlyGsPAwL1fkdVwvsAeYOJWHz+zwN+oparJJ7jAX9lzoW\\u002f7usBuM1sDR7C\\u002fwAKUwBFAw6nAWQtNNjg7uMDjPSoI3Qa4wHAjBuiC1bbAyNRhNnM0w8D2DfnA3EykwAbVjAaF3rvAOAXMrSlwucD9eDAm82nDwFMOiY9737jAOI32yHFZusD+zHxsUdS\\u002fwLx5AV94N8bAxdKcD+cOuMCUqas8PbvFwBMMDTYf5avAuQmb+OQZxMAA1A+FM8S8wEA8uLkAVMLAkuj0L1dNvcAsVf5W5H3JwIMDIK0Tr77Awo12llZ7wcDcTLKRHETGwBKNiRaNuLTAXPta7n62ysAXkX6MX2yWQLi5NWV4FL7Au6FhACCsssCeOyBw2ffBwCDek7FPJL3AgOL4OCX0tcAOBrOaHE\\u002fDwBv9OLUdg8bAYeb7mWn0w8ACF0FEVs7EwNodizbiwMfAz\\u002fOh3m2fwsCYypvU18a9wL4EU0xOe7PAnUoZBNkRu8Cu2h0NyGi4wDTnoZXQrcPAYOr6N8E4tMBp+K4mVPDFwPhYosLy+J3A3bkiME+qwMAwN9dgC0OqwDr74p8P08LAmj6UScMbwMB+AqYK5RfBwF6tv3V+BcrAJ15oi\\u002fnyxMD4uxZFZue4wGoqQBYjY8DA+a26no3VwsDvJDHGf4\\u002fGwJj5hg\\u002frMcnACNp354XsnsBu5DTmz8u8wCa3y6DEt8LABWkwBIb9w8BWhEl7mD6SwO0EnwkiybLAkJW\\u002ftSSBwcD6jlr8coy\\u002fwBaOU7ZyrLjAEZt5E5OEtsDrkPPAVl\\u002fAwCEu75ity8bAxPfBhcJGuMA\\u002f9wI2+BSwwFG5Cc1RUa3AtGjtH306wMBu+u+rSD3CwAKjE+J7arrAA8TY8hKkusCKlK1EC3S+wPDzMVRo973AVVzxgJJhxMAswgnd9Zy5wMqd29c+fsXALzgVlXiCo8C6KbcBPTLHwGxnrxia5cLAjK\\u002fBH8QtwcBF3mI9wxzKwE3JDSslAMDAmjkqAvJPxcAt\\u002fX1Jqd3LwIYNyI1DS73ATB3+l0TosMD0VZB8CS\\u002fHwKsp3Hm42K7Ax+80R5vMwsDakpxUln\\u002fCwE4lEJZ9O8LAqAUlECXGfsADAosZScfBwMNqGmDjdMzAygxRm0aCucCigCA0WsutwBTNyF4hvMrAsFTPBodrucBpb90Ow3G1wEIMwDoEoMTAeUPHF+PYz8CPO3Lie8e5wI8L8Np7E7zAbh5BjmROoMAnWDL7W1WzwP7ygY0dz8PAlKkFXlBIuMD2z2eICie\\u002fwCwsXjiU1r7ABoifjpbHtMBeYXky5Fe+wAqwbF9nQ7PAfmkSe0Gut8BKGmXAmACzwAn5vUKWAMjAC7B9T8OjlsBZMwmCapS0wMO0xfezw77ANDFF7mhGz8AHLwv8AzHBwG9V0wJsybjAOTPFA7Hux8AINe4wtDO4wOhJ7+jtwGnAUtOK7+pvsMCkWjaavw7EwKYR1RJDtcHAHMyV9ShewMAzQ6C4s1fFwMFVFQwav7bANMQxVJsIucB57Nv6cFvCwM61jhox0YPALba\\u002faGUxw8CxbwMsb5jDwKVqD0xyhsLA3EtG8XfntsBl464Ir5S4wBOC+QgzycHA6MGmoIF4isDCDMnOyQq3wHCzXnIv9sDADNB7rVuFusDae5CMikG\\u002fwENTW+guAsHA2p533m6VmMAoonUN8nbDwF\\u002fdungTLMTACY\\u002fgzL2R0cBldla3aEbBwFgaVk8gD37ASx\\u002fy\\u002feluxMAUoVC4mIaywCepU2+pEMXAh2NfJkamtsAFRBEb75q5wPBTn8Chy7bABZ2p3yrwssDDIPk4M8y4wIsw5Wr1BsbAHmJlLhfMmcA2FpSsNyi5wCPaRb0fY8LAfnLWvh7IpsBKFkzVRku8wLhsIvUJ28jAZcoXfbvswMBWKumlWfe\\u002fwDBvvFoOQMzA1BQftskSxcCK6nbMU0i1wAckb0fyYMHAwHR9H6jpwsDK4cVLiNnBwOxKtWpyWMHAzhX0GtUBy8BC5L7PfFDDwOD9A\\u002fA6drfA8GL+2w06ucDKQGC1ra2ywIDYZFk8WcHAgqmJTvnat8AYlq5MqOCswHZHQ+kdNLbAgcBxBUk0tcCMhTJJJuS+wFvNdi3Fcb\\u002fASIFBQB2KxMDPPKxWqMuywPpPAYtObr3AosmW5gJPqMDPTa9BaG69wJA0daF0MLnAPNZKoVUdtsCoJFBgzXazwByE\\u002f7PiKL7AjABsxOCrycBpwqoGX4+3wBytuPa9G7nAVwngzLNtu8AxAdy8AkPEwDHQ4+Q\\u002fesPAgWHFlcRdqcBZZpMoGYShwCLde+deD7PAbjIHC0Y+vcCqnqyYyTK8wK7r60xFDMDAS207pLpRwcDyh2QxlXW4wKJdJ6zagMHAxvfSbKHksMDXp0S7vGulwBWmEAjURcjApoUw12mHvsCVm0d969+zwFZbBCcOSq\\u002fAHwExm0NywsBRg6ZTAXyuwB4EedHDTcTANNx6xk6OpMAv0t38bWDIwL+GxK4Vo77As+Szeyp2vsDqFQaaUPXFwJ8sWbwB6arARrsdmgtrsMBylsx\\u002fJZjBwGCIQsJ09K7AuCLZwrMPxsBxkPkwZC26wK2S74TRsMbA1yZFeJGNvcAWWi3lqHi7wE+8SEW458nAbi\\u002fdSZoIw8A5AgRzx8S5wJLgdnC9VsPA\\u002fuZMuxA0ssDQUpY5O\\u002fGkwPCjO2L03L\\u002fAG9I\\u002f5kReqsCv9oyJnp\\u002fEwNdw4RspYMHAIPsiU680wsCtwtDBp9iiwKNEyXew+cjAvf1pInt+wcCE7xFQAcO5wMh7s3dUsbDA\\u002fEEPj4xvrsDaBpcEftC8wERvpnVCtcLAFH+Tu4HEuMBUwwIYgKGUwECBUIxXgsLAyFQkFqkQtMB3Xl0qek7AwB8WMWgpY8DAsfbBNSCspsCM1JTKrQTBwIzIGHvyIrjAVPFM5\\u002foimsA2zlAxlgK7wJINhyihbLnAMdfkWmD4tsDaxha8GDa9wO6gQRHDobDAN3JCf2cSqsBR\\u002fjPieh65wA5j5O+6eMLA8NedKe9Fq8AcHa8iywejwI4\\u002fqB6SirDAx8yIDA7twsAIXd+Q0cy\\u002fwFTGJDRkj7bAHBXH7iz4wMDetdiT5Ri0wDZKNak6mb\\u002fAp0f+SavTusCnJXgYbTuywG7WgwnSO7fAVIWBBGmRuMAwCKRtoFzDwAYomp\\u002fPG7bAgIcVGuCavcARbn3rInq6wJy11J8q6bTAYlb1ajCYtMAZMR7rkALLwJmtKDhUHrjABK\\u002fe4plKtsBmW2gm50+8wIB\\u002fz\\u002fnO+qrAAF6mCR3vxsAbwAMugEGzwKZZkw4XscLA6XjU+So5wsBi7v+tCMCcwFexwWn9P7\\u002fA1wLPEZB2wcB1YegbDxumwAw1MvQIZLrAXHfO5XuotcAoQvJxKsLAwOgdNP9YV7TADJYHiD9cu8BNbrCVxwvGwGm3nfGLVsbAQ3ChnTtyxMCY+Lx7BFKzwNCk6a\\u002f3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       "text/plain": [
        "Figure({\n",
@@ -612,7 +612,7 @@
   },
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+   "id": "aa65c338",
    "metadata": {},
    "source": [
     "## Model plots"
@@ -621,7 +621,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "e12a1dcf",
+   "id": "e80b821e",
    "metadata": {},
    "outputs": [
     {
@@ -642,7 +642,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "1a9fb5fd",
+   "id": "4255f5a5",
    "metadata": {},
    "outputs": [
     {
@@ -662,7 +662,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "11d0277e",
+   "id": "da5d979a",
    "metadata": {},
    "outputs": [
     {
@@ -682,7 +682,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dca282a4",
+   "id": "5a2e2318",
    "metadata": {},
    "source": [
     "## Saving figures\n",
diff --git a/doc/_build/jupyter_execute/guides/regression.ipynb b/doc/_build/jupyter_execute/guides/regression.ipynb
index 6fcc6a3..4a16069 100644
--- a/doc/_build/jupyter_execute/guides/regression.ipynb
+++ b/doc/_build/jupyter_execute/guides/regression.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "c2670bc0",
+   "id": "dc133df4",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d3a66eec",
+   "id": "edb1298f",
    "metadata": {},
    "source": [
     "# Regression\n",
@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "5ad5e15d",
+   "id": "ac90e4f3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a50af0cd",
+   "id": "92055efc",
    "metadata": {},
    "source": [
     "## Coefficient table (with confidence intervals)"
@@ -59,7 +59,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "0a8d464b",
+   "id": "b9670a62",
    "metadata": {},
    "outputs": [
     {
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "057b8f6d",
+   "id": "b59967c1",
    "metadata": {},
    "source": [
     "Change the confidence level with `conf_level=` (e.g. `0.99`).\n",
@@ -109,7 +109,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "3857437b",
+   "id": "8ab3372a",
    "metadata": {},
    "outputs": [
     {
@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b5102446",
+   "id": "7f6ba991",
    "metadata": {},
    "source": [
     "## Model-fit summaries"
@@ -160,7 +160,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "8263ee73",
+   "id": "b22b9ff0",
    "metadata": {},
    "outputs": [
     {
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c73d51b8",
+   "id": "f99874da",
    "metadata": {},
    "source": [
     "## Correlation"
@@ -207,7 +207,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "e7d9a0c6",
+   "id": "4cb6534a",
    "metadata": {},
    "outputs": [
     {
@@ -245,7 +245,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fa993617",
+   "id": "f71362c8",
    "metadata": {},
    "source": [
     "## Visualizing models\n",
@@ -256,7 +256,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "16afab96",
+   "id": "77fc53b2",
    "metadata": {},
    "outputs": [
     {
@@ -283,7 +283,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "04e30907",
+   "id": "6b3d24d2",
    "metadata": {},
    "source": [
     "For the plotnine engine you can also drop the parallel-slopes lines onto your own\n",
@@ -299,7 +299,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "08dc9c71",
+   "id": "77834dfe",
    "metadata": {},
    "outputs": [
     {
@@ -307,7 +307,7 @@
       "image/png": 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",
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a88LSalAG1ZBGF3YncCUGEdLPp2lQAKcS2Zlp8BAIO+dWpSNWUCmpkPozg+WQG4+n\\u002flAx6XAWC9\\u002fJrLZisBQRacfIIpwQGwauLZEtrNAeMUaI2h3hUDcMDzp4VC5wLC78aMpOXNAzXSZ8pwOk0CK0kzT6Le8QOSFUmrcs6nALP\\u002f51c4ns0CsNNHtsbqzQBAlOllrssBAcICtiUYDhkAX0t5iX0qhwEbuYbQnirPAlciSNVvgp8CoEmJ70zWSQNoREH7usLfA6MoucDpakUDsnTwHKkqUQJpmQ4f90aXAOHrdP9gWgUBiBGzb092gwCAUTGXc\\u002fmpAiBnTpNytlEAUNges+9uvQCSX+Q6GLHrAAItTT91Ku8AZfjhlY0GUwAD6z4C8ZJRAHiudadJMtMALEjn6H6yfwH+dH2tV0bDANAXRZ6WEnUCoAjuJ8zWUwG66ibjjQJnAX1Cn04gxtcAyRdLuL06mwALzWlY8DbDAvSmz8i5rtEAYmjmN5EamQDpaWDx7wsHAaG7ZAI5susBgvXH7UKl8QJB\\u002fsY5VtbPAwD2y2ypOcECo4o6+7i2KwEjuvu+SLYhA\\u002fC\\u002f63tZth0CwpeceXNKmQGDoQXsd3LbA5B7vK3HHtkBIAotss66EwNIw6uqmG61AVh5KqXBcr8AavbMJmKauwPRC33FEfrJA0iJ+2i5tpEAkYV++YWieQLQykDISgYVASmjzzQwYxUCUoSgd7niqQPlSIaOoUrVAZsYGHsnNmEDPj4eD8pO+wEa9JTIcrKdAUNNhZ1lQgkCc2RMLy26qQMuRQYESz7ZAKaXQMkdbwcA4E2\\u002fbzY+8QBtCms3nGKBAF3tvHc9Fg0DOP43J4uTBQCjWYIvyHZ\\u002fAOFDV7CiqrsDDIEDvcvqgQA8eU1u7S69ApSG66jZXsUAfk0hCE8KqQKARjSQqhKNAKvRBJZ0sh8BFpeioLgWkwJw8BhDItZbAgdZmuXozokBft6KNH8SNQH8VDgljuZBAZZnznffvlMAq+GA+3ySVQORH1jdP66dA9v7FPcWRksAhKFp153G+wI5NtgLewrlA0l\\u002fbVvyevcC+N6rRLUGSwIrdv\\u002fBmnbZArmccw+aSksDwueD0MMtvwPRGn35T2XHAECOhLHiEssBErlm5PXKaQFxwvdppZInAiKIA3FIpkcCO6MVC1GicQHsiIRPrDrzA9lacmmmMp8Dixtqjr+KnQMy1AF4+4JJAklLc67hPvEBYTamSEGC0QO7AQV8PEpbAbAuuVdn4uMDCjmGGHjekQLQxZJFApKBAqIG+EUhXnEBwUqzQR528QBCkWaafnsXAKK7XMGxyu0DYrqOIUUi2QMmTbv16KKXAVjEIoYnZrkB4\\u002fJJ1wsa8wJAWcH+QBqVA9QI85ljIskBJjX6VOYehQLQof1+41aVAXZZ3F9cstMBpJX6j9mmswMoanm8OkKTAWsEYFPHsmUCSnb+BNAaHwCKgkhj\\u002feaVAtItY2kSAfMAPslXp6Z2zQIaIdpWba7BA4fV8h2n+o8A+X8lZH2y1QKAPWgRUHFZAMOpQs6oRacCQxcZfHgl8QJIg6fRFkZ3AEgyVaOXNp8AI+iT2kNyzwBKRrVZHHKxARO5Q5YmhlMAmYHhi9n7DwHoOIFjurpJAGCwZenWZcsCmOJIUhxaywAWIrcto4sFAKFYP1blNgcCchcUOJX2fwNaXSqOnxpHAdtCC8f+QmMAovLZe5AizwGT8sh5PiqfAkPhSqaK0tcCfY61bxLa2wMx8nm4N4ppAcC7PaLu8fkABg5LcnVu5QBBKYWLed2lAHQUqP91sosAIHj8o76qwwC4iOP2cdbhAYOY9LHxje8AkCICsyU+GwEVSaYQJHKtAestORw76pkBNQYlOFLu6QMhOdqKUzJXAhkLP+k4SdMDNKbVWBMSiQDBeB9MaLrBA\\u002fIzETTgYlMCFj3yGSIaVwAoILed19bJA16Oqcv6TscC4hBOoLruFwDzm443DEbpAdq0e2t9MpMD5vH0\\u002fppmowCKUcB6ms41Am9lxekwepUDZrN6ObuygQHOmxIQsQq9AANS3aL3rlMBCZiXM\\u002ffCpQADi\\u002fJxtERHAHO433X5QmcDpdPoZfaSywA5iLlHkSKrACpgSxM\\u002fntsDWzM5QsUClwGkIaHOzn6tAygtbPw38kMCGNnr2aN23QNNRFBiXir1AKF4ACWWDrMA26uq+jvhzQPgW0\\u002fERFbhAB0TjhZ5PtUAbGIbRMN2wwES0+jW3TbXAbEpnBXFXuUAw8u25DPKeQFwnAendNZRAYSTLKKMmwkDWkI7DrWOiwAATq0AueprAwXWefCVCs0CQmALiVnqEwNofISTZmI\\u002fACvz4xdkMpUANOZq7VZTBwGYmWWdzH7nA7KqG6TQ+gsAAxRP\\u002figJcQCAveWlBNlFAkJ7eE2zjsEAebUa48tavwA67GZAw+qbACLZTTkaqm8DseOLKSFipwEATQJFNA0rA++o93WgNnkA8iqU+EuSjQBEB+OrJSr7ArIADtIR5n0C9x3aC\\u002fQ+iQDLqjK8ZR5dANpkXr9havUAkAA806IqaQBHui6CXaopAyCOL8q0erEBYVpq98MuFQFSNjUySsY1AtpXS2ktvokDaaDCYNUucwLrvuomJurRAvZsbt70oo0AsjwrantRyQFe6OQFFMqzA4DpeMyKyZUDwJchRlZ5zwJ3VyqOPqp5ApMZHxXNQtsBAu0aF3rWAQAk9\\u002fmMrgZNAgxFpFDhmrEBADKyJgkORQEk23FZoI7fAbCEP8VvtiMBtEkVsLQS+QLAYT9XJGYLAcIQGMHxNoMCSvldSTJevwG4SpIT7RqHAYbUdQQhZy8AjbTEZXN26QOColyMXqsnAuIMGkLfhfcC48cdYB1CtwENWMUG5MppAPl8r81YQlsBzjkBbpmqrQHUygPBFV6\\u002fAHGCPwZ5ksMBKlPtLOgOywBpBnR7purbAuEuMDbSrg8DzKUwf532iQA5iZZuguLnAAgZqUiLKmEDJH4VI\\u002fia6wDQ1W0DsfoLA+u7UGItUrUDwxlsdDGnCQP98+CimHrXAAJdVzC+kr0BcpC6MJWrAQEBDBrYYnaNAY\\u002f\\u002fXqpdSr0AYtsSd4u6zQPHcYam3sqDAvqNe7q5+scB87FbgSXOMQNuxc6Z8urdA1BsCNH0vvcA9oUW0scmbQJCoNJHbHGjAcCeyUvzTsECY2syanbCWwHvjIRdueZdAX1tU\\u002fbqDsEAI+PzymmKtQJF83t9T8ZjAsq64iRgKlcC7G0ZXlie8wLVFGcwXyahA5LZdQReimcDMZB9DCNqfQOB4M2CmgX1AgXzS8hJLuMCQddy5ZytxQMqzVCXxDrPAht1dAdeswsD0C0xgNs+hQAnWv\\u002fymgZRAAaA+NnI\\u002fs8D5ktNoBKO\\u002fwBYrtAJoUbhA4hUKRNY8xkAamlz8slKwQNbLc4VoC57A+i8facjiuMB0GMdr38G8wEpEd\\u002fvJk7JAVmtlm3cgnkDd1HecgwO2QKiSs5faYZDAu5zGRQ17tEDqIPNh4pSpQLWdDYpkIqjAcp09duoYo0AW5\\u002fnVwna8QGZ1e8yK86PATmrYJxKArsAuBLtj+7C4QPRJvtLXoabAMN7XkX\\u002f8fMCS32a18cfDQADWKkInsx1AvhTKnb8VtcDZGDtrhzOcQMIvlhFv9YNAJH8\\u002fCcS\\u002fckDCXOdJs9SUQEAvDTNYIqdA\\u002ffdRCqswkUAa1WvTxd+8wDg1783uzbPAwWVCxm+klMDK+cOE6weqwJglOv1bM1vAABNDUZ0AgUCpv7DOyE+cwFi9J+ede6fAWuOwjirPkkDRb6KKYpa5wEJ55udpcqNAg5AecB6Iw0Dm1dPIHXWzQB8TfL8b37nAqplpSwY3wUDjQfzjNvKtQGzaO+57sqJAbEjqmNWNtsCrBoCS50LCQLi0lnC4XrfA9aZmSoLnp0BozvYWjBG3QJ2Zd0lBUa1AVYXuJlo3oEBOEofrovSgwBsyOGTEfqjAAsZoOTeLucDGj+8jYsSTQPf+1DyClMHAuBediS+8r0A\\u002fVkuaGULCwGbAFr9rUJdATh6pV1dKnEDWN8n4Qx+sQPisl9eo9qXAPBNo20yUt0A1c27MjWKjwADj\\u002fc+OZoPACBSdfCOHrkBDH5Qu+pqwQOjg1sUXk6pAABVCY8ietkCpS846R32XwOYIez8scqdAjA\\u002fZqn31eUCARY2lyAg9wPR+3hLqTorAr9uZluBtpsDUhCHt8RKUwD9asLeQyrTAYAVfxoBAuUDWIGYpxf+zwE133MwPvqTA7KKNxSMcrsCU9LBArLR9wMlPPLIwu6pAflyVeiKlrsDIR0XZJOG1wJx9GWXAs6BAPufjWBBdmkAexGhluNCowC86QLf5LbtACh8jr+NbuMCbozJwQWejQJ7EDynhB7NA0i4Hmd0RkcB3Kx9PbOuuwOjKyJK3763A4FKD5ywDaUCkQgS4fqGIQOMQLoJqGp3A4CVZ0h4akkAiMKZn+QijQEBsyk1ObLpA8zbG9YUKo8AM6aI0OH2kwLA7njsZvbFAk9Vf1GpiocD4Xa21v8aFwLyJIxyxd6BA5Ea5W+oHkMDYvY7RrmyGQIZCHXbsmqTAYuSzeLE7msBwUVvnzUyBQEn+N3XHnLXAubbHhJIxkcDRdfUOT3qoQNi\\u002ft5diIppAJxEGOt\\u002fspUDwmZ5v0BhywAT3AqjUFbHAnFGFyXDsqkBton+5Q7mRwI7yWjpP4KFA6Jg+DW3It8C+SbGiKFyIQBjjrp18B6jAAujI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a88LSalAG1ZBGF3YncCUGEdLPp2lQAKcS2Zlp8BAIO+dWpSNWUCmpkPozg+WQG4+n\\u002flAx6XAWC9\\u002fJrLZisBQRacfIIpwQGwauLZEtrNAeMUaI2h3hUDcMDzp4VC5wLC78aMpOXNAzXSZ8pwOk0CK0kzT6Le8QOSFUmrcs6nALP\\u002f51c4ns0CsNNHtsbqzQBAlOllrssBAcICtiUYDhkAX0t5iX0qhwEbuYbQnirPAlciSNVvgp8CoEmJ70zWSQNoREH7usLfA6MoucDpakUDsnTwHKkqUQJpmQ4f90aXAOHrdP9gWgUBiBGzb092gwCAUTGXc\\u002fmpAiBnTpNytlEAUNges+9uvQCSX+Q6GLHrAAItTT91Ku8AZfjhlY0GUwAD6z4C8ZJRAHiudadJMtMALEjn6H6yfwH+dH2tV0bDANAXRZ6WEnUCoAjuJ8zWUwG66ibjjQJnAX1Cn04gxtcAyRdLuL06mwALzWlY8DbDAvSmz8i5rtEAYmjmN5EamQDpaWDx7wsHAaG7ZAI5susBgvXH7UKl8QJB\\u002fsY5VtbPAwD2y2ypOcECo4o6+7i2KwEjuvu+SLYhA\\u002fC\\u002f63tZth0CwpeceXNKmQGDoQXsd3LbA5B7vK3HHtkBIAotss66EwNIw6uqmG61AVh5KqXBcr8AavbMJmKauwPRC33FEfrJA0iJ+2i5tpEAkYV++YWieQLQykDISgYVASmjzzQwYxUCUoSgd7niqQPlSIaOoUrVAZsYGHsnNmEDPj4eD8pO+wEa9JTIcrKdAUNNhZ1lQgkCc2RMLy26qQMuRQYESz7ZAKaXQMkdbwcA4E2\\u002fbzY+8QBtCms3nGKBAF3tvHc9Fg0DOP43J4uTBQCjWYIvyHZ\\u002fAOFDV7CiqrsDDIEDvcvqgQA8eU1u7S69ApSG66jZXsUAfk0hCE8KqQKARjSQqhKNAKvRBJZ0sh8BFpeioLgWkwJw8BhDItZbAgdZmuXozokBft6KNH8SNQH8VDgljuZBAZZnznffvlMAq+GA+3ySVQORH1jdP66dA9v7FPcWRksAhKFp153G+wI5NtgLewrlA0l\\u002fbVvyevcC+N6rRLUGSwIrdv\\u002fBmnbZArmccw+aSksDwueD0MMtvwPRGn35T2XHAECOhLHiEssBErlm5PXKaQFxwvdppZInAiKIA3FIpkcCO6MVC1GicQHsiIRPrDrzA9lacmmmMp8Dixtqjr+KnQMy1AF4+4JJAklLc67hPvEBYTamSEGC0QO7AQV8PEpbAbAuuVdn4uMDCjmGGHjekQLQxZJFApKBAqIG+EUhXnEBwUqzQR528QBCkWaafnsXAKK7XMGxyu0DYrqOIUUi2QMmTbv16KKXAVjEIoYnZrkB4\\u002fJJ1wsa8wJAWcH+QBqVA9QI85ljIskBJjX6VOYehQLQof1+41aVAXZZ3F9cstMBpJX6j9mmswMoanm8OkKTAWsEYFPHsmUCSnb+BNAaHwCKgkhj\\u002feaVAtItY2kSAfMAPslXp6Z2zQIaIdpWba7BA4fV8h2n+o8A+X8lZH2y1QKAPWgRUHFZAMOpQs6oRacCQxcZfHgl8QJIg6fRFkZ3AEgyVaOXNp8AI+iT2kNyzwBKRrVZHHKxARO5Q5YmhlMAmYHhi9n7DwHoOIFjurpJAGCwZenWZcsCmOJIUhxaywAWIrcto4sFAKFYP1blNgcCchcUOJX2fwNaXSqOnxpHAdtCC8f+QmMAovLZe5AizwGT8sh5PiqfAkPhSqaK0tcCfY61bxLa2wMx8nm4N4ppAcC7PaLu8fkABg5LcnVu5QBBKYWLed2lAHQUqP91sosAIHj8o76qwwC4iOP2cdbhAYOY9LHxje8AkCICsyU+GwEVSaYQJHKtAestORw76pkBNQYlOFLu6QMhOdqKUzJXAhkLP+k4SdMDNKbVWBMSiQDBeB9MaLrBA\\u002fIzETTgYlMCFj3yGSIaVwAoILed19bJA16Oqcv6TscC4hBOoLruFwDzm443DEbpAdq0e2t9MpMD5vH0\\u002fppmowCKUcB6ms41Am9lxekwepUDZrN6ObuygQHOmxIQsQq9AANS3aL3rlMBCZiXM\\u002ffCpQADi\\u002fJxtERHAHO433X5QmcDpdPoZfaSywA5iLlHkSKrACpgSxM\\u002fntsDWzM5QsUClwGkIaHOzn6tAygtbPw38kMCGNnr2aN23QNNRFBiXir1AKF4ACWWDrMA26uq+jvhzQPgW0\\u002fERFbhAB0TjhZ5PtUAbGIbRMN2wwES0+jW3TbXAbEpnBXFXuUAw8u25DPKeQFwnAendNZRAYSTLKKMmwkDWkI7DrWOiwAATq0AueprAwXWefCVCs0CQmALiVnqEwNofISTZmI\\u002fACvz4xdkMpUANOZq7VZTBwGYmWWdzH7nA7KqG6TQ+gsAAxRP\\u002figJcQCAveWlBNlFAkJ7eE2zjsEAebUa48tavwA67GZAw+qbACLZTTkaqm8DseOLKSFipwEATQJFNA0rA++o93WgNnkA8iqU+EuSjQBEB+OrJSr7ArIADtIR5n0C9x3aC\\u002fQ+iQDLqjK8ZR5dANpkXr9havUAkAA806IqaQBHui6CXaopAyCOL8q0erEBYVpq98MuFQFSNjUySsY1AtpXS2ktvokDaaDCYNUucwLrvuomJurRAvZsbt70oo0AsjwrantRyQFe6OQFFMqzA4DpeMyKyZUDwJchRlZ5zwJ3VyqOPqp5ApMZHxXNQtsBAu0aF3rWAQAk9\\u002fmMrgZNAgxFpFDhmrEBADKyJgkORQEk23FZoI7fAbCEP8VvtiMBtEkVsLQS+QLAYT9XJGYLAcIQGMHxNoMCSvldSTJevwG4SpIT7RqHAYbUdQQhZy8AjbTEZXN26QOColyMXqsnAuIMGkLfhfcC48cdYB1CtwENWMUG5MppAPl8r81YQlsBzjkBbpmqrQHUygPBFV6\\u002fAHGCPwZ5ksMBKlPtLOgOywBpBnR7purbAuEuMDbSrg8DzKUwf532iQA5iZZuguLnAAgZqUiLKmEDJH4VI\\u002fia6wDQ1W0DsfoLA+u7UGItUrUDwxlsdDGnCQP98+CimHrXAAJdVzC+kr0BcpC6MJWrAQEBDBrYYnaNAY\\u002f\\u002fXqpdSr0AYtsSd4u6zQPHcYam3sqDAvqNe7q5+scB87FbgSXOMQNuxc6Z8urdA1BsCNH0vvcA9oUW0scmbQJCoNJHbHGjAcCeyUvzTsECY2syanbCWwHvjIRdueZdAX1tU\\u002fbqDsEAI+PzymmKtQJF83t9T8ZjAsq64iRgKlcC7G0ZXlie8wLVFGcwXyahA5LZdQReimcDMZB9DCNqfQOB4M2CmgX1AgXzS8hJLuMCQddy5ZytxQMqzVCXxDrPAht1dAdeswsD0C0xgNs+hQAnWv\\u002fymgZRAAaA+NnI\\u002fs8D5ktNoBKO\\u002fwBYrtAJoUbhA4hUKRNY8xkAamlz8slKwQNbLc4VoC57A+i8facjiuMB0GMdr38G8wEpEd\\u002fvJk7JAVmtlm3cgnkDd1HecgwO2QKiSs5faYZDAu5zGRQ17tEDqIPNh4pSpQLWdDYpkIqjAcp09duoYo0AW5\\u002fnVwna8QGZ1e8yK86PATmrYJxKArsAuBLtj+7C4QPRJvtLXoabAMN7XkX\\u002f8fMCS32a18cfDQADWKkInsx1AvhTKnb8VtcDZGDtrhzOcQMIvlhFv9YNAJH8\\u002fCcS\\u002fckDCXOdJs9SUQEAvDTNYIqdA\\u002ffdRCqswkUAa1WvTxd+8wDg1783uzbPAwWVCxm+klMDK+cOE6weqwJglOv1bM1vAABNDUZ0AgUCpv7DOyE+cwFi9J+ede6fAWuOwjirPkkDRb6KKYpa5wEJ55udpcqNAg5AecB6Iw0Dm1dPIHXWzQB8TfL8b37nAqplpSwY3wUDjQfzjNvKtQGzaO+57sqJAbEjqmNWNtsCrBoCS50LCQLi0lnC4XrfA9aZmSoLnp0BozvYWjBG3QJ2Zd0lBUa1AVYXuJlo3oEBOEofrovSgwBsyOGTEfqjAAsZoOTeLucDGj+8jYsSTQPf+1DyClMHAuBediS+8r0A\\u002fVkuaGULCwGbAFr9rUJdATh6pV1dKnEDWN8n4Qx+sQPisl9eo9qXAPBNo20yUt0A1c27MjWKjwADj\\u002fc+OZoPACBSdfCOHrkBDH5Qu+pqwQOjg1sUXk6pAABVCY8ietkCpS846R32XwOYIez8scqdAjA\\u002fZqn31eUCARY2lyAg9wPR+3hLqTorAr9uZluBtpsDUhCHt8RKUwD9asLeQyrTAYAVfxoBAuUDWIGYpxf+zwE133MwPvqTA7KKNxSMcrsCU9LBArLR9wMlPPLIwu6pAflyVeiKlrsDIR0XZJOG1wJx9GWXAs6BAPufjWBBdmkAexGhluNCowC86QLf5LbtACh8jr+NbuMCbozJwQWejQJ7EDynhB7NA0i4Hmd0RkcB3Kx9PbOuuwOjKyJK3763A4FKD5ywDaUCkQgS4fqGIQOMQLoJqGp3A4CVZ0h4akkAiMKZn+QijQEBsyk1ObLpA8zbG9YUKo8AM6aI0OH2kwLA7njsZvbFAk9Vf1GpiocD4Xa21v8aFwLyJIxyxd6BA5Ea5W+oHkMDYvY7RrmyGQIZCHXbsmqTAYuSzeLE7msBwUVvnzUyBQEn+N3XHnLXAubbHhJIxkcDRdfUOT3qoQNi\\u002ft5diIppAJxEGOt\\u002fspUDwmZ5v0BhywAT3AqjUFbHAnFGFyXDsqkBton+5Q7mRwI7yWjpP4KFA6Jg+DW3It8C+SbGiKFyIQBjjrp18B6jAAujI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       ],
       "text/plain": [
        "Figure({\n",
@@ -509,7 +509,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6477edc9",
+   "id": "492b5252",
    "metadata": {},
    "source": [
     "Per-facet shading works in both engines: `shade_p_value` / `shade_confidence_interval`\n",
diff --git a/doc/_build/jupyter_execute/guides/sampling.ipynb b/doc/_build/jupyter_execute/guides/sampling.ipynb
index d86d578..cb1b7af 100644
--- a/doc/_build/jupyter_execute/guides/sampling.ipynb
+++ b/doc/_build/jupyter_execute/guides/sampling.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "16cf0433",
+   "id": "9171bec6",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe865f3b",
+   "id": "3b88a9e8",
    "metadata": {},
    "source": [
     "# Sampling\n",
@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "9c71d6ee",
+   "id": "5ec986a1",
    "metadata": {},
    "outputs": [
     {
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b1329399",
+   "id": "9bcee499",
    "metadata": {},
    "source": [
     "## One virtual sample"
@@ -89,7 +89,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "79bbe22f",
+   "id": "af464f6b",
    "metadata": {},
    "outputs": [
     {
@@ -130,7 +130,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "785876de",
+   "id": "892a49af",
    "metadata": {},
    "source": [
     "## Many samples → a sampling distribution\n",
@@ -141,7 +141,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "8394ffd3",
+   "id": "0caf812e",
    "metadata": {},
    "outputs": [
     {
@@ -189,7 +189,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06e11137",
+   "id": "501cc21f",
    "metadata": {},
    "source": [
     "That `prop_red` column is a **sampling distribution**. Visualize its spread the\n",
@@ -205,7 +205,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "81143aaa",
+   "id": "eb5bde46",
    "metadata": {},
    "outputs": [
     {
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ebb972ee",
+   "id": "a3bbfd8f",
    "metadata": {},
    "source": [
     "## Tactile samples\n",
@@ -263,7 +263,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "d3f3a45a",
+   "id": "ca9878c7",
    "metadata": {},
    "outputs": [
     {
@@ -310,7 +310,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e42c9a2",
+   "id": "4b465f7f",
    "metadata": {},
    "source": [
     "```{seealso}\n",
diff --git a/doc/_build/jupyter_execute/guides/theory-based.ipynb b/doc/_build/jupyter_execute/guides/theory-based.ipynb
index c7f488d..7bc53ea 100644
--- a/doc/_build/jupyter_execute/guides/theory-based.ipynb
+++ b/doc/_build/jupyter_execute/guides/theory-based.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "5606ac5b",
+   "id": "0f0eb6cc",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "337d8d6c",
+   "id": "7498133c",
    "metadata": {},
    "source": [
     "# Theory-based inference\n",
@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "5ef17e1d",
+   "id": "09b25eea",
    "metadata": {},
    "outputs": [
     {
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca3ea1c8",
+   "id": "acdd22fe",
    "metadata": {},
    "source": [
     "`t_stat` and `chisq_stat` return just the test statistic if that's all you need.\n",
@@ -103,14 +103,14 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "24220223",
+   "id": "640c9c2a",
    "metadata": {},
    "outputs": [
     {
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       "text/plain": [
-       "<plotnine.ggplot.ggplot object at 0x1391fe060>"
+       "<plotnine.ggplot.ggplot object at 0x13b462060>"
       ]
      },
      "execution_count": 3,
@@ -137,7 +137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7bd7c71",
+   "id": "0739ef7e",
    "metadata": {},
    "source": [
     "Supported distributions: `\"t\"`, `\"z\"`, `\"F\"` (pass `df=(df1, df2)`), and\n",
@@ -152,7 +152,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "565a48f5",
+   "id": "9dd4a203",
    "metadata": {},
    "outputs": [
     {
@@ -160,7 +160,7 @@
       "image/png": 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",
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{\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"white\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"white\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"#C8D4E3\",\"linecolor\":\"#C8D4E3\",\"minorgridcolor\":\"#C8D4E3\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"#C8D4E3\",\"linecolor\":\"#C8D4E3\",\"minorgridcolor\":\"#C8D4E3\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scattermap\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermap\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"white\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"#C8D4E3\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"white\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\"},\"bgcolor\":\"white\",\"radialaxis\":{\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"},\"yaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"},\"zaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"},\"bgcolor\":\"white\",\"caxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#EBF0F8\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#EBF0F8\",\"zerolinewidth\":2}}},\"title\":{\"text\":\"Theoretical Distribution\"},\"xaxis\":{\"title\":{\"text\":\"mean\"}},\"yaxis\":{\"title\":{\"text\":\"density\"}},\"bargap\":0.02,\"showlegend\":false},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2190be2c",
+   "id": "5086879e",
    "metadata": {},
    "source": [
     "## Population standard deviation\n",
@@ -211,7 +211,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "48c35537",
+   "id": "426c4e7f",
    "metadata": {},
    "outputs": [
     {
diff --git a/doc/_build/jupyter_execute/index.ipynb b/doc/_build/jupyter_execute/index.ipynb
index 54812e3..14b798c 100644
--- a/doc/_build/jupyter_execute/index.ipynb
+++ b/doc/_build/jupyter_execute/index.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "75e555df",
+   "id": "d3eb497d",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc4f7b71",
+   "id": "a93c1238",
    "metadata": {},
    "source": [
     "# moderndive (Python)\n",
@@ -67,7 +67,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "aede4c55",
+   "id": "5012fbc3",
    "metadata": {},
    "outputs": [
     {
@@ -98,7 +98,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "7d76deeb",
+   "id": "c297fe46",
    "metadata": {},
    "outputs": [
     {
@@ -143,7 +143,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "0d749da0",
+   "id": "29571551",
    "metadata": {},
    "outputs": [
     {
@@ -151,7 +151,7 @@
       "image/png": 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       ],
       "text/plain": [
        "Figure({\n",
@@ -199,7 +199,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8c5df1ee",
+   "id": "8f740d70",
    "metadata": {},
    "source": [
     "## Where to next\n",
diff --git a/doc/coming-from-r.md b/doc/coming-from-r.md
index 510cc69..6f3b032 100644
--- a/doc/coming-from-r.md
+++ b/doc/coming-from-r.md
@@ -41,14 +41,13 @@ methods on the pipeline where applicable:
 > `t_test`, `prop_test`, `chisq_test`, `t_stat`, `chisq_stat`,
 > `rep_sample_n`/`rep_slice_sample`, `get_regression_table`,
 > `get_regression_points`, `get_regression_summaries`, `get_correlation`,
-> `pop_sd`, `tidy_summary`, `geom_parallel_slopes`.
+> `pop_sd`, `tidy_summary`, `geom_parallel_slopes`, `geom_categorical_model`.
 
 ## What's actually different
 
 | R | Python | Why |
 | --- | --- | --- |
 | `x %>% f(...)` / `x \|> f(...)` | `x.f(...)` (method chaining) | no pipe operator in Python |
-| `geom_categorical_model()` | `gg_categorical_model(engine=...)` | renamed to match `gg_parallel_slopes` |
 | ggplot2 `geom_*` layers | plotly by default; `engine="plotnine"` for ggplot-style | dual-engine plotting |
 | `lm(y ~ x, data)` object | a fitted **statsmodels** model: `smf.ols("y ~ x", data=df.to_pandas()).fit()` | regression backend |
 | `get_correlation(df, y ~ x)` | `get_correlation(df, "y ~ x")` *or* `get_correlation(df, x="x", y="y")` | formula passed as a string |
@@ -64,4 +63,4 @@ methods on the pipeline where applicable:
 ## Same datasets
 
 Most R `moderndive`/`infer` datasets are bundled here under the same name —
-`load_pennies()`, `load_promotions()`, `load_gss()`, etc. See {doc}`datasets`.
+`load_pennies()`, `load_mythbusters_yawn()`, `load_gss()`, etc. See {doc}`datasets`.
diff --git a/doc/datasets.md b/doc/datasets.md
index ed19957..8137600 100644
--- a/doc/datasets.md
+++ b/doc/datasets.md
@@ -25,8 +25,8 @@ derived tables). Each loads with `load_<name>()` and returns a polars DataFrame.
 import moderndive as md
 
 md.available_datasets()          # sorted list of every dataset name
-md.load_dataset("promotions")    # load by name (string)
-md.load_promotions()             # or via the generated loader
+md.load_dataset("pennies")       # load by name (string)
+md.load_pennies()                # or via the generated loader
 ```
 
 ## By topic
@@ -37,10 +37,9 @@ md.load_promotions()             # or via the generated loader
   `pennies`, `pennies_sample`, `pennies_resamples`, `orig_pennies_sample`
 
 **Hypothesis testing & CIs**
-: `promotions`, `promotions_shuffled`, `mythbusters_yawn`, `movies_sample`,
-  `spotify_by_genre`, `spotify_52_original`, `spotify_52_shuffled`,
-  `spotify_metal_deephouse`, `offshore`, `age_at_marriage`, `zinc_tidy`,
-  `cle_sac`, `gss`
+: `mythbusters_yawn`, `movies_sample`, `spotify_by_genre`, `spotify_52_original`,
+  `spotify_52_shuffled`, `spotify_metal_deephouse`, `offshore`, `age_at_marriage`,
+  `zinc_tidy`, `cle_sac`, `gss`
 
 **Regression**
 : `evals`, `saratoga_houses`, `house_prices`, `coffee_quality`, `coffee_ratings`,
diff --git a/doc/getting-started.md b/doc/getting-started.md
index 2a3e65f..e8c9670 100644
--- a/doc/getting-started.md
+++ b/doc/getting-started.md
@@ -37,8 +37,8 @@ All datasets ship with the package and load with `load_<name>()`:
 ```{code-cell} python
 import moderndive as md
 
-promotions = md.load_promotions()
-promotions.head()
+yawn = md.load_mythbusters_yawn()
+yawn.head()
 ```
 
 
@@ -64,22 +64,22 @@ sentence:
 ```{code-cell} python
 from moderndive import specify, observe, get_p_value
 
-# 1. The observed statistic
+# 1. The observed statistic: do "seeded" people yawn more than the control group?
 obs = observe(
-    promotions, formula="decision ~ gender", success="promoted",
-    stat="diff in props", order=("male", "female"),
+    yawn, formula="yawn ~ group", success="yes",
+    stat="diff in props", order=("seed", "control"),
 )
 
 # 2. A null distribution: specify → hypothesize → generate → calculate
 null = (
-    specify(promotions, formula="decision ~ gender", success="promoted")
+    specify(yawn, formula="yawn ~ group", success="yes")
     .hypothesize(null="independence")
     .generate(reps=1000, type="permute", seed=42)
-    .calculate(stat="diff in props", order=("male", "female"))
+    .calculate(stat="diff in props", order=("seed", "control"))
 )
 
 # 3. Summarize
-get_p_value(null, obs_stat=obs, direction="right")   # ≈ 0.025
+get_p_value(null, obs_stat=obs, direction="right")
 ```
 
 Each verb has a focused guide: {doc}`guides/sampling`,
diff --git a/doc/guides/hypothesis-testing.md b/doc/guides/hypothesis-testing.md
index e57cdcc..a09c22f 100644
--- a/doc/guides/hypothesis-testing.md
+++ b/doc/guides/hypothesis-testing.md
@@ -24,30 +24,30 @@ from it.
 
 ## Two groups: a permutation test
 
-The classic "promotions" experiment — does the (perceived) gender on a résumé
-change the promotion rate?
+Are tracks more likely to be popular in *metal* than in *deep house*? Compare the
+two genres' "popular" rates, then permute the genre labels to build the null.
 
 ```{code-cell} python
 import moderndive as md
 from moderndive import specify, observe, get_p_value, visualize, shade_p_value
 
-promotions = md.load_promotions()
+spotify = md.load_spotify_metal_deephouse()
 
-# Observed difference in promotion proportions, male − female
+# Observed difference in "popular" proportions, metal − deep-house
 obs = observe(
-    promotions, formula="decision ~ gender", success="promoted",
-    stat="diff in props", order=("male", "female"),
-)   # ≈ 0.292
+    spotify, formula="popular_or_not ~ track_genre", success="popular",
+    stat="diff in props", order=("metal", "deep-house"),
+)
 
-# Null: gender is independent of the decision → permute the labels
+# Null: genre is independent of popularity → permute the labels
 null = (
-    specify(promotions, formula="decision ~ gender", success="promoted")
+    specify(spotify, formula="popular_or_not ~ track_genre", success="popular")
     .hypothesize(null="independence")
-    .generate(reps=1000, type="permute", seed=42)
-    .calculate(stat="diff in props", order=("male", "female"))
+    .generate(reps=1000, type="permute", seed=76)
+    .calculate(stat="diff in props", order=("metal", "deep-house"))
 )
 
-get_p_value(null, obs_stat=obs, direction="right")   # ≈ 0.025
+get_p_value(null, obs_stat=obs, direction="right")
 ```
 
 ## Shade the p-value
@@ -97,8 +97,33 @@ null_p = (
 `calculate(stat=...)` supports the full infer vocabulary: `"mean"`, `"median"`,
 `"sum"`, `"sd"`, `"prop"`, `"count"`, `"diff in means"`, `"diff in medians"`,
 `"diff in props"`, `"ratio of means"`, `"ratio of props"`, `"odds ratio"`,
-`"slope"`, `"correlation"`, `"t"`, `"z"`, `"F"`, `"Chisq"`, plus any custom
-callable `stat(response, explanatory) -> float`.
+`"slope"`, `"correlation"`, `"t"`, `"z"`, `"F"`, `"Chisq"`.
+
+## Custom test statistics
+
+Beyond those strings, `stat=` accepts **any function** that takes the response
+(and explanatory) arrays and returns a single number — so you can infer about a
+statistic that isn't built in. Here we bootstrap the interquartile range of
+almond weights and read off a 95% interval:
+
+```{code-cell} python
+import numpy as np
+from moderndive import get_confidence_interval
+
+def iqr(response, explanatory):
+    return float(np.percentile(response, 75) - np.percentile(response, 25))
+
+boot_iqr = (
+    md.load_almonds_sample_100()
+    .specify(response="weight")
+    .generate(reps=1000, type="bootstrap", seed=1)
+    .calculate(stat=iqr)
+)
+get_confidence_interval(boot_iqr, level=0.95, type="percentile")
+```
+
+The function receives `(response, explanatory)` as numpy arrays (`explanatory` is
+`None` for a single-variable `specify`), and must return a scalar.
 
 ```{seealso}
 Prefer a one-line classical test? See the tidy wrappers in {doc}`theory-based`

From 770298c2214787442aa6199f33608de2108f9477 Mon Sep 17 00:00:00 2001
From: Chester Ismay <chester.ismay@gmail.com>
Date: Fri, 19 Jun 2026 08:38:14 -0700
Subject: [PATCH 7/9] docs: use df.specify() flow; note plots are static
 (plotly is interactive)

- Convert examples from specify(df, ...) to df.specify(...) to mirror the R
  df %>% specify(...) pipeline more closely.
- Add a note (landing, getting-started, plotting guide) that documentation plots
  are static images while the default plotly engine is interactive when run.
- Rebuilt HTML.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017CTL1QSTg1DmDUpqYuPEog
---
 doc/_build/html/.doctrees/api.doctree         | Bin 3766775 -> 3766775 bytes
 .../html/.doctrees/coming-from-r.doctree      | Bin 23979 -> 23977 bytes
 doc/_build/html/.doctrees/environment.pickle  | Bin 3509556 -> 3509556 bytes
 .../html/.doctrees/getting-started.doctree    | Bin 29983 -> 30895 bytes
 .../guides/confidence-intervals.doctree       | Bin 63512 -> 63504 bytes
 .../guides/hypothesis-testing.doctree         | Bin 70802 -> 70796 bytes
 .../html/.doctrees/guides/plotting.doctree    | Bin 356028 -> 356967 bytes
 .../html/.doctrees/guides/regression.doctree  | Bin 140614 -> 140608 bytes
 .../.doctrees/guides/theory-based.doctree     | Bin 53331 -> 53329 bytes
 doc/_build/html/.doctrees/index.doctree       | Bin 66991 -> 68593 bytes
 doc/_build/html/_sources/coming-from-r.md.txt |   2 +-
 .../html/_sources/getting-started.md.txt      |   7 +-
 .../guides/confidence-intervals.md.txt        |   8 +-
 .../_sources/guides/hypothesis-testing.md.txt |   6 +-
 .../html/_sources/guides/plotting.md.txt      |  15 ++-
 .../html/_sources/guides/regression.md.txt    |   6 +-
 .../html/_sources/guides/theory-based.md.txt  |   2 +-
 doc/_build/html/_sources/index.md.txt         |   9 +-
 doc/_build/html/coming-from-r.html            |   2 +-
 doc/_build/html/getting-started.html          |   7 +-
 .../html/guides/confidence-intervals.html     |   8 +-
 .../html/guides/hypothesis-testing.html       |   6 +-
 doc/_build/html/guides/plotting.html          |  15 ++-
 doc/_build/html/guides/regression.html        |   6 +-
 doc/_build/html/guides/theory-based.html      |   2 +-
 doc/_build/html/index.html                    |   9 +-
 doc/_build/html/searchindex.js                |   2 +-
 doc/_build/jupyter_execute/datasets.ipynb     |   8 +-
 .../jupyter_execute/getting-started.ipynb     |  43 +++----
 .../guides/confidence-intervals.ipynb         |  38 +++----
 .../guides/hypothesis-testing.ipynb           |  32 +++---
 .../jupyter_execute/guides/plotting.ipynb     | 105 +++++++++---------
 .../jupyter_execute/guides/regression.ipynb   |  40 +++----
 .../jupyter_execute/guides/sampling.ipynb     |  24 ++--
 .../jupyter_execute/guides/theory-based.ipynb |  24 ++--
 doc/_build/jupyter_execute/index.ipynb        |  23 ++--
 doc/coming-from-r.md                          |   2 +-
 doc/getting-started.md                        |   7 +-
 doc/guides/confidence-intervals.md            |   8 +-
 doc/guides/hypothesis-testing.md              |   6 +-
 doc/guides/plotting.md                        |  15 ++-
 doc/guides/regression.md                      |   6 +-
 doc/guides/theory-based.md                    |   2 +-
 doc/index.md                                  |   9 +-
 44 files changed, 286 insertions(+), 218 deletions(-)

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diff --git a/doc/_build/html/.doctrees/coming-from-r.doctree b/doc/_build/html/.doctrees/coming-from-r.doctree
index 7c55c23cd92fa782254cbfe7a10950589762c2df..5d5bff110cf9fe8d397351e729c47bc79db86348 100644
GIT binary patch
delta 94
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delta 96
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diff --git a/doc/_build/html/.doctrees/environment.pickle b/doc/_build/html/.doctrees/environment.pickle
index b9f3c55ca3909000e8c33e8abd0d7166f9d631ce..dc520fd2be2e6eeb00445d2e9b523b0388d304ed 100644
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zcm2PsTGuLbBH@3x9n>wjs&n4~eY$n@t%|PJpiFEVq(<{HSv_Ef{$;Yhc);BK%VY{J
bh}pVp`<H2z{l&!tW|hehT%7%_G8z9L6lz^;

diff --git a/doc/_build/html/.doctrees/getting-started.doctree b/doc/_build/html/.doctrees/getting-started.doctree
index 6357db694a1b7bc13a09dc0de2b93d41789d443e..225d158c0555125a2986d6ab39dd31f75e4f924b 100644
GIT binary patch
delta 1657
zcmcIlZ)h837|&hqx+JArwA#+4Yi~8_;!(0*(^Ttd?YeHYOVf5`-z08H?vnS;<Sx5E
z&J;B6!@xig+>;8U;6Mh03F}IjgK4RL7z{Q<zX*O1K~TgmS`hpoINvwPCGB*&FMc`h
zecs>mdw$RFc`mv9Gqtuwv2U|0SKe4>KNc8;b~xbkrS58M<S5lqNSsqcMqWzE=|T|B
z3<qE?{NrO_zN>yX`kJ@sC^M>NN)@O)cfRswC5YJaJExGu<+GY;a0b%Oshq5GCXx+K
z)KXSXQq6=ZYbuw}C64C}a!PS>E|HN8o)2+Lma58X24_fIN)shdL9=v2%BH!3EM-Ll
z=g6ul>4}snzb+MdzE&z*;L>u&(j|jS7C2E#C#<X)s-Ru*3Yz>Ml@%W??Z1@;QKZa2
zzFCzEmAb_Tm77k70|k*29Sh7O|Gbq{ia65jvtx&=br&M#hoUXE4WOojW6E2E)E|8n
z1<(Mh*RYaU;*)<L$_^>;Yfo>nsi+|c=XoEV+>We91OKSMecOa>^*drM?O)fOfL+u5
zQ;+Px_isDW;=1XClft0KHelTepPk<4wa~N58mU5T@kis;2gk>0>US8QeW6XnKK+Ja
zy@%am!?rEhoc)vTV_@@y<$j96c?Xpz7#NRExptG2KPp0TtP5@k9=I9}(%~e06^;19
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zK*&T~O+4|*u64)usTsl52gfI11o3Vgv<exNqW7>h!+t`8^fN4U$pf^HC63YEEZz%W
z&9VSnv+NhSpIgR4_ptS?u6;ICXxl6?8Ck96mO1AaJ7HNlL}}G)${m_AtNGk3%>I#K
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j{sKx0NiWq>OBV7H@C;lq0^S;98bfM~%4+qtAyIz;MByu;

delta 879
zcmaJ=T}V@57~Xew&N^E#7UmC~xk8(cWx6@1Vz9shL#Jn(me>zN=H~I6o)kM%i>Qc#
zQ$poCFGV59i!SCoR98Vl1wktbBD@eUf-brUz3M9I`{s`8s+;q@@AE$I-@|({J81DI
z;%;;Ehm|b%(#s|pg8{-9%C%tA8N^SH$73Uj85gWKoq#UikN=0aw58^xO<U+}V9*Q5
z1zSx~sTQvq;f>>zIVw4&8scU^?A~QIjUe_Y<}XHrfncAFkn3sZKT_{_R}`$aayWg|
z3@xIIY4yOM=oeZ&L*9hX6G-1n-$~z0yGYiKPS1+x@|}JeAr$i!%k1K2i{ADFouUjL
zhg|<mvKAUaQ8;<l4jtinB*13413AGNaWb_mJn`6JJK#puAja&Fi5y|PEXeJR@F#MN
z8z%h)sOl9NfrV|mgCx)Oc2Fnmtuqf7c1^%qs06C}R+!7Qp37QK*29J101Kh+3Rv_C
zTHyQ!Vy?3ADcE9?N##+NN@kG@%n{0LBZ^qGt$yJkKg;HIl7TGFQNBt>Y~t_hu+>%u
z&ugt(CidFIKh@E!D8M7^=9hF}4f~5ZR4cvTUD`gc)jmW<xL6;6w@J5IXAQvbWDMnq
zXq7NMTE~22;n`>nvr$y;>%MZ|_msOeR%KGiyg3%u+bbCI5Vb5yQIJD@C18;o5Z2n{
zcMNJ=ow&qSHu}=ifh%drKWp?7d+z{9l^dMFFbNYCCGb!Y&<rdq4s;#fD^=VBJu;W7
zBmqat&rTBvR+Lht!dS{};)#nnSV+}G`iUO6kA4sZcZsFUn&rW<__dkw_#{0xOzEoh
tO2rvO3>+jsiHe8F{i&FytNBz_k!+OALFts!mamu~lzc@zpmk2g(O)pY7kK~x

diff --git a/doc/_build/html/.doctrees/guides/confidence-intervals.doctree b/doc/_build/html/.doctrees/guides/confidence-intervals.doctree
index e6477207bd5ceec625c1f58e67ef5fc26da2d970..2010178c24c8e6c800c25ea7f3e16f80bacf2101 100644
GIT binary patch
delta 559
zcmbR7fqB9QW|julsjM4Wnpqj;CwH)}7f8&>&Cg3I)+;VZP0mcK)R^qSmW^8~W%CX8
zJ|@N^lP!2|VrmWKeui7>{$@tLSsE%P78Yry1|~_m$)<*hx+aNf2D%oOW~RDn=7t7F
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zx3VN7skFExwFoH+-e252SwMn~DFU~e&F?R&@Tk8}%Fi!}PsvP6Q?OOg;8FkrtOi=&
PyGVlCQ=9+Ymy!SgJ?*)R

delta 571
zcmbR6fqBLUW|julsk|FmnpqhYCU>x|7c4GFP0mcK)JV+9&Cg3I)=`-3&X$cwE@ksI
z_C6-Yqmxs)Zer2w$NdbK+-5o6N==nCGqY3!i$p_Ri&S$nT@!N)3tdZdOCw!FQ-c&U
zvouSSGy~(wyUk8(5~EN_Q&XXO@<I)f$@iKyh%$OJzr|uNqEyn>5rLkYtIs}SVw9NN
zcrip2774kPB^gPj#U-glpon;RaWh8-d+d}9(+E7uHov^6!lUsaDL=m?J|#0PO~F<{
agG&Jjup6p#|J3Av_t=TnuvzE+6A1wJ>$)`n

diff --git a/doc/_build/html/.doctrees/guides/hypothesis-testing.doctree b/doc/_build/html/.doctrees/guides/hypothesis-testing.doctree
index d4470f592e547cd4fe11fa8c3f997fa583a3cfd7..679dc3e41fbb84150d3315dd0206dd2f919e6a04 100644
GIT binary patch
delta 478
zcmbQVlBH)Q3rhp*RK|@grksp7Hp}xIWaP^)$xN%%D=tV)2GSav-}CY^a%8Z_PRTHh
zz@_50q>7=Lfu*^ng{7`>s-d~ANn)Czu4R&`v958ld5WcpVUnq(Me^i#>ZgfQcvr(w
zjTjS^bS68>NNkSLlnfxwVA{FhxZmb;Clxpu?I*v#RKb&&o(d1b$p^0NhlL=f?B)Yk
z+&CE(C$rzn;7iWW%qvFdtGl-im&E$b(GNDr^JS=K@v|~8Of45-0D{RIZ`)1Y@k3~9
ICZnbZ0RA7Cxc~qF

delta 482
zcmeBK$uemr3rhp*RECW#rksp7H_P!HWMoTC&P=P+n9RrPG+B-B3P%Qe?34`C2n@;1
zYJ7_%Rm_qtjVw&f5_K(%k_>cBjLgz>lZ?~Sbj=NtQc{e}EG-Nz4JN-)KdniOLM2U2
zh3d(UG9r`bS7{Js^kxT5;{c*m($*0V{WhODsldtTF!}AJ3ijdxa2QTjxGXZc{_+m2
zq8m3?Uv1}LRGMsXKZ75tK_fXoGp|@jVRGfYZ8&AtZw`I1L0({Lod^RMWT<EHvobJD
ao+v6ZdG{%e$(w!%P5yeyZ0k(M&%yx1ft!W^

diff --git a/doc/_build/html/.doctrees/guides/plotting.doctree b/doc/_build/html/.doctrees/guides/plotting.doctree
index 3cf1102e0cc4a218bd91b7a82b54b7ca43147791..13c7512381fde3713d38d4b20cbdde9c9f3150f7 100644
GIT binary patch
delta 8249
zcmc&(dvH@#8t*yDY12S~O4>r&l(eO^4NXa!x2&|VRC$c$v8;kv=uL7%651v<ErJRK
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hN#W>dOky~D=x<^;Lo_-uoFI*kh7)d2+}w$H@PB9@c|ZUF

delta 7705
zcmd5>dvF`Y8PD$dup}He!L}Sbwj$Y%Wjnq^k|j&Zlf*dS389I3#Ka~<vQCzdSaK|j
z)I1W027<xnwIVxEQie`vc!W|+I%uE{kPMVE2_z0lOACR9p)GBpG<`A@Xm{^!EuAG%
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diff --git a/doc/_build/html/_sources/coming-from-r.md.txt b/doc/_build/html/_sources/coming-from-r.md.txt
index 6f3b032..94ad9aa 100644
--- a/doc/_build/html/_sources/coming-from-r.md.txt
+++ b/doc/_build/html/_sources/coming-from-r.md.txt
@@ -18,7 +18,7 @@ pennies %>%
 ```python
 # Python — verbs are methods on the returned objects
 (
-    specify(md.load_pennies(), response="year")
+    md.load_pennies().specify(response="year")
     .hypothesize(null="point", mu=1995)
     .generate(reps=1000, type="bootstrap", seed=1)
     .calculate(stat="mean")
diff --git a/doc/_build/html/_sources/getting-started.md.txt b/doc/_build/html/_sources/getting-started.md.txt
index e8c9670..09b43da 100644
--- a/doc/_build/html/_sources/getting-started.md.txt
+++ b/doc/_build/html/_sources/getting-started.md.txt
@@ -72,7 +72,7 @@ obs = observe(
 
 # 2. A null distribution: specify → hypothesize → generate → calculate
 null = (
-    specify(yawn, formula="yawn ~ group", success="yes")
+    yawn.specify(formula="yawn ~ group", success="yes")
     .hypothesize(null="independence")
     .generate(reps=1000, type="permute", seed=42)
     .calculate(stat="diff in props", order=("seed", "control"))
@@ -90,6 +90,11 @@ Each verb has a focused guide: {doc}`guides/sampling`,
 Plots default to **plotly** (interactive). Pass `engine="plotnine"` for
 grammar-of-graphics output. The composition syntax is identical:
 
+```{note}
+The plots shown in this documentation are **static images**. Running the code
+yourself yields **interactive** plotly figures by default.
+```
+
 ```{code-cell} python
 from moderndive import visualize, shade_p_value
 
diff --git a/doc/_build/html/_sources/guides/confidence-intervals.md.txt b/doc/_build/html/_sources/guides/confidence-intervals.md.txt
index b507a51..f6b279d 100644
--- a/doc/_build/html/_sources/guides/confidence-intervals.md.txt
+++ b/doc/_build/html/_sources/guides/confidence-intervals.md.txt
@@ -30,7 +30,7 @@ from moderndive import specify, get_confidence_interval, visualize, shade_confid
 almonds = md.load_almonds_sample_100()
 
 boot = (
-    specify(almonds, response="weight")
+    almonds.specify(response="weight")
     .generate(reps=1000, type="bootstrap", seed=1)
     .calculate(stat="mean")
 )
@@ -44,7 +44,7 @@ get_confidence_interval(boot, level=0.95, type="percentile")
 # shape: (1, 2) → lower_ci ≈ 3.61, upper_ci ≈ 3.75
 
 # Standard-error method (needs the point estimate)
-point = float(specify(almonds, response="weight").calculate(stat="mean"))
+point = float(almonds.specify(response="weight").calculate(stat="mean"))
 get_confidence_interval(boot, level=0.95, type="se", point_estimate=point)
 
 # Bias-corrected
@@ -80,7 +80,7 @@ import polars as pl
 mythbusters = md.load_mythbusters_yawn()
 
 boot_prop = (
-    specify(mythbusters, response="yawn", success="yes")
+    mythbusters.specify(response="yawn", success="yes")
     .generate(reps=1000, type="bootstrap", seed=1)
     .calculate(stat="prop")
 )
@@ -93,7 +93,7 @@ Add an explanatory variable and an `order=` to compare two groups:
 
 ```{code-cell} python
 boot_diff = (
-    specify(mythbusters, formula="yawn ~ group", success="yes")
+    mythbusters.specify(formula="yawn ~ group", success="yes")
     .generate(reps=1000, type="bootstrap", seed=1)
     .calculate(stat="diff in props", order=("seed", "control"))
 )
diff --git a/doc/_build/html/_sources/guides/hypothesis-testing.md.txt b/doc/_build/html/_sources/guides/hypothesis-testing.md.txt
index a09c22f..e4c3302 100644
--- a/doc/_build/html/_sources/guides/hypothesis-testing.md.txt
+++ b/doc/_build/html/_sources/guides/hypothesis-testing.md.txt
@@ -41,7 +41,7 @@ obs = observe(
 
 # Null: genre is independent of popularity → permute the labels
 null = (
-    specify(spotify, formula="popular_or_not ~ track_genre", success="popular")
+    spotify.specify(formula="popular_or_not ~ track_genre", success="popular")
     .hypothesize(null="independence")
     .generate(reps=1000, type="permute", seed=76)
     .calculate(stat="diff in props", order=("metal", "deep-house"))
@@ -69,7 +69,7 @@ age = md.load_age_at_marriage()
 obs_t = observe(age, response="age", stat="t", null="point", mu=23)
 
 null_t = (
-    specify(age, response="age")
+    age.specify(response="age")
     .hypothesize(null="point", mu=23)
     .generate(reps=1000, type="bootstrap", seed=1)
     .calculate(stat="t")
@@ -85,7 +85,7 @@ import polars as pl
 coins = pl.DataFrame({"flip": ["heads"] * 30 + ["tails"] * 70})
 
 null_p = (
-    specify(coins, response="flip", success="heads")
+    coins.specify(response="flip", success="heads")
     .hypothesize(null="point", p=0.5)
     .generate(reps=1000, type="draw", seed=1)   # "simulate" is an alias
     .calculate(stat="prop")
diff --git a/doc/_build/html/_sources/guides/plotting.md.txt b/doc/_build/html/_sources/guides/plotting.md.txt
index 2492c09..08a1c1c 100644
--- a/doc/_build/html/_sources/guides/plotting.md.txt
+++ b/doc/_build/html/_sources/guides/plotting.md.txt
@@ -24,8 +24,13 @@ Every plotting function in `moderndive` takes an `engine=` argument:
 - `engine="plotnine"` — grammar-of-graphics figures (`plotnine.ggplot`).
 
 The composition syntax is identical across engines, so you can switch a whole
-analysis by changing one argument. (In this documentation, plotly figures are
-shown as static images; in a notebook they're fully interactive.)
+analysis by changing one argument.
+
+```{note}
+The figures on this page are **static images** so they render in the docs. In a
+notebook or script, the default `engine="plotly"` produces **interactive**
+figures (hover, zoom, pan).
+```
 
 ```{code-cell} python
 import moderndive as md
@@ -35,7 +40,7 @@ from moderndive import (
 )
 
 boot = (
-    specify(md.load_almonds_sample_100(), response="weight")
+    md.load_almonds_sample_100().specify(response="weight")
     .generate(reps=1000, type="bootstrap", seed=1)
     .calculate(stat="mean")
 )
@@ -73,7 +78,7 @@ spotify = md.load_spotify_metal_deephouse()
 obs = observe(spotify, formula="popular_or_not ~ track_genre", success="popular",
               stat="diff in props", order=("metal", "deep-house"))
 null = (
-    specify(spotify, formula="popular_or_not ~ track_genre", success="popular")
+    spotify.specify(formula="popular_or_not ~ track_genre", success="popular")
     .hypothesize(null="independence")
     .generate(reps=1000, type="permute", seed=76)
     .calculate(stat="diff in props", order=("metal", "deep-house"))
@@ -100,7 +105,7 @@ from moderndive.infer.viz import visualize_fit
 
 houses = md.load_saratoga_houses()
 boot_fit = (
-    specify(houses, formula="price ~ living_area + bedrooms")
+    houses.specify(formula="price ~ living_area + bedrooms")
     .generate(reps=1000, type="bootstrap", seed=1)
     .fit()
 )
diff --git a/doc/_build/html/_sources/guides/regression.md.txt b/doc/_build/html/_sources/guides/regression.md.txt
index 3971971..2d40648 100644
--- a/doc/_build/html/_sources/guides/regression.md.txt
+++ b/doc/_build/html/_sources/guides/regression.md.txt
@@ -98,16 +98,16 @@ from moderndive.infer.viz import visualize_fit
 from moderndive import shade_confidence_interval, shade_p_value
 
 f = "price ~ living_area + bedrooms"
-obs_fit = specify(houses, formula=f).fit()
+obs_fit = houses.specify(formula=f).fit()
 
 # Bootstrap distribution of each coefficient → per-term CIs
-boot_fit = specify(houses, formula=f).generate(reps=1000, type="bootstrap", seed=1).fit()
+boot_fit = houses.specify(formula=f).generate(reps=1000, type="bootstrap", seed=1).fit()
 boot_fit.get_confidence_interval(level=0.95)          # one row per term
 visualize_fit(boot_fit) + shade_confidence_interval(boot_fit.get_confidence_interval())
 
 # Null distribution → per-term p-values, each facet shaded at its own estimate
 null_fit = (
-    specify(houses, formula=f).hypothesize(null="independence")
+    houses.specify(formula=f).hypothesize(null="independence")
     .generate(reps=1000, type="permute", seed=1).fit()
 )
 null_fit.get_p_value(obs_stat=obs_fit, direction="two-sided")
diff --git a/doc/_build/html/_sources/guides/theory-based.md.txt b/doc/_build/html/_sources/guides/theory-based.md.txt
index 51f6247..1318c71 100644
--- a/doc/_build/html/_sources/guides/theory-based.md.txt
+++ b/doc/_build/html/_sources/guides/theory-based.md.txt
@@ -77,7 +77,7 @@ curve, or both:
 from moderndive import specify, visualize
 
 boot = (
-    specify(age, response="age")
+    age.specify(response="age")
     .generate(reps=1000, type="bootstrap", seed=1)
     .calculate(stat="mean")
 )
diff --git a/doc/_build/html/_sources/index.md.txt b/doc/_build/html/_sources/index.md.txt
index dda2806..fdde1ba 100644
--- a/doc/_build/html/_sources/index.md.txt
+++ b/doc/_build/html/_sources/index.md.txt
@@ -74,7 +74,7 @@ obs
 ```{code-cell} python
 # Null distribution under "genre doesn't matter" (permutation)
 null = (
-    specify(spotify, formula="popular_or_not ~ track_genre", success="popular")
+    spotify.specify(formula="popular_or_not ~ track_genre", success="popular")
     .hypothesize(null="independence")
     .generate(reps=1000, type="permute", seed=76)
     .calculate(stat="diff in props", order=("metal", "deep-house"))
@@ -87,6 +87,13 @@ get_p_value(null, obs_stat=obs, direction="right")
 visualize(null) + shade_p_value(obs_stat=obs, direction="right")
 ```
 
+```{note}
+Plots throughout this documentation are rendered as **static images**. When you
+run the code yourself, the default `engine="plotly"` produces **interactive**
+figures (hover, zoom, pan); `engine="plotnine"` gives static grammar-of-graphics
+plots.
+```
+
 ## Where to next
 
 - New here? Start with {doc}`getting-started`.
diff --git a/doc/_build/html/coming-from-r.html b/doc/_build/html/coming-from-r.html
index e18e318..9886b5e 100644
--- a/doc/_build/html/coming-from-r.html
+++ b/doc/_build/html/coming-from-r.html
@@ -269,7 +269,7 @@ <h2>The infer pipeline<a class="headerlink" href="#the-infer-pipeline" title="Li
 </div>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Python — verbs are methods on the returned objects</span>
 <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">md</span><span class="o">.</span><span class="n">load_pennies</span><span class="p">(),</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;year&quot;</span><span class="p">)</span>
+    <span class="n">md</span><span class="o">.</span><span class="n">load_pennies</span><span class="p">()</span><span class="o">.</span><span class="n">specify</span><span class="p">(</span><span class="n">response</span><span class="o">=</span><span class="s2">&quot;year&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;point&quot;</span><span class="p">,</span> <span class="n">mu</span><span class="o">=</span><span class="mi">1995</span><span class="p">)</span>
     <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">)</span>
diff --git a/doc/_build/html/getting-started.html b/doc/_build/html/getting-started.html
index 835f13f..e2a4957 100644
--- a/doc/_build/html/getting-started.html
+++ b/doc/_build/html/getting-started.html
@@ -330,7 +330,7 @@ <h2>The inference pipeline<a class="headerlink" href="#the-inference-pipeline" t
 
 <span class="c1"># 2. A null distribution: specify → hypothesize → generate → calculate</span>
 <span class="n">null</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">yawn</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;yawn ~ group&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;yes&quot;</span><span class="p">)</span>
+    <span class="n">yawn</span><span class="o">.</span><span class="n">specify</span><span class="p">(</span><span class="n">formula</span><span class="o">=</span><span class="s2">&quot;yawn ~ group&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;yes&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;independence&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;permute&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
     <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;seed&quot;</span><span class="p">,</span> <span class="s2">&quot;control&quot;</span><span class="p">))</span>
@@ -358,6 +358,11 @@ <h2>The inference pipeline<a class="headerlink" href="#the-inference-pipeline" t
 <h2>Visualizing — choose your engine<a class="headerlink" href="#visualizing-choose-your-engine" title="Link to this heading">¶</a></h2>
 <p>Plots default to <strong>plotly</strong> (interactive). Pass <code class="docutils literal notranslate"><span class="pre">engine=&quot;plotnine&quot;</span></code> for
 grammar-of-graphics output. The composition syntax is identical:</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The plots shown in this documentation are <strong>static images</strong>. Running the code
+yourself yields <strong>interactive</strong> plotly figures by default.</p>
+</div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">visualize</span><span class="p">,</span> <span class="n">shade_p_value</span>
diff --git a/doc/_build/html/guides/confidence-intervals.html b/doc/_build/html/guides/confidence-intervals.html
index 69e2860..7d78c51 100644
--- a/doc/_build/html/guides/confidence-intervals.html
+++ b/doc/_build/html/guides/confidence-intervals.html
@@ -268,7 +268,7 @@ <h2>A bootstrap distribution for a mean<a class="headerlink" href="#a-bootstrap-
 <span class="n">almonds</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_almonds_sample_100</span><span class="p">()</span>
 
 <span class="n">boot</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">almonds</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span><span class="p">)</span>
+    <span class="n">almonds</span><span class="o">.</span><span class="n">specify</span><span class="p">(</span><span class="n">response</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">)</span>
 <span class="p">)</span>
@@ -286,7 +286,7 @@ <h2>Confidence intervals — three methods<a class="headerlink" href="#confidenc
 <span class="c1"># shape: (1, 2) → lower_ci ≈ 3.61, upper_ci ≈ 3.75</span>
 
 <span class="c1"># Standard-error method (needs the point estimate)</span>
-<span class="n">point</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">specify</span><span class="p">(</span><span class="n">almonds</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">))</span>
+<span class="n">point</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">almonds</span><span class="o">.</span><span class="n">specify</span><span class="p">(</span><span class="n">response</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">))</span>
 <span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">boot</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;se&quot;</span><span class="p">,</span> <span class="n">point_estimate</span><span class="o">=</span><span class="n">point</span><span class="p">)</span>
 
 <span class="c1"># Bias-corrected</span>
@@ -353,7 +353,7 @@ <h2>A confidence interval for a proportion<a class="headerlink" href="#a-confide
 <span class="n">mythbusters</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_mythbusters_yawn</span><span class="p">()</span>
 
 <span class="n">boot_prop</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">mythbusters</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;yawn&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;yes&quot;</span><span class="p">)</span>
+    <span class="n">mythbusters</span><span class="o">.</span><span class="n">specify</span><span class="p">(</span><span class="n">response</span><span class="o">=</span><span class="s2">&quot;yawn&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;yes&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;prop&quot;</span><span class="p">)</span>
 <span class="p">)</span>
@@ -378,7 +378,7 @@ <h2>A confidence interval for a difference<a class="headerlink" href="#a-confide
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">boot_diff</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">mythbusters</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;yawn ~ group&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;yes&quot;</span><span class="p">)</span>
+    <span class="n">mythbusters</span><span class="o">.</span><span class="n">specify</span><span class="p">(</span><span class="n">formula</span><span class="o">=</span><span class="s2">&quot;yawn ~ group&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;yes&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;seed&quot;</span><span class="p">,</span> <span class="s2">&quot;control&quot;</span><span class="p">))</span>
 <span class="p">)</span>
diff --git a/doc/_build/html/guides/hypothesis-testing.html b/doc/_build/html/guides/hypothesis-testing.html
index e776442..0e90273 100644
--- a/doc/_build/html/guides/hypothesis-testing.html
+++ b/doc/_build/html/guides/hypothesis-testing.html
@@ -278,7 +278,7 @@ <h2>Two groups: a permutation test<a class="headerlink" href="#two-groups-a-perm
 
 <span class="c1"># Null: genre is independent of popularity → permute the labels</span>
 <span class="n">null</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">spotify</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;popular_or_not ~ track_genre&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;popular&quot;</span><span class="p">)</span>
+    <span class="n">spotify</span><span class="o">.</span><span class="n">specify</span><span class="p">(</span><span class="n">formula</span><span class="o">=</span><span class="s2">&quot;popular_or_not ~ track_genre&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;popular&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;independence&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;permute&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">76</span><span class="p">)</span>
     <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;metal&quot;</span><span class="p">,</span> <span class="s2">&quot;deep-house&quot;</span><span class="p">))</span>
@@ -324,7 +324,7 @@ <h2>One mean / one proportion (point null)<a class="headerlink" href="#one-mean-
 <span class="n">obs_t</span> <span class="o">=</span> <span class="n">observe</span><span class="p">(</span><span class="n">age</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">,</span> <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;t&quot;</span><span class="p">,</span> <span class="n">null</span><span class="o">=</span><span class="s2">&quot;point&quot;</span><span class="p">,</span> <span class="n">mu</span><span class="o">=</span><span class="mi">23</span><span class="p">)</span>
 
 <span class="n">null_t</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">age</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">)</span>
+    <span class="n">age</span><span class="o">.</span><span class="n">specify</span><span class="p">(</span><span class="n">response</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;point&quot;</span><span class="p">,</span> <span class="n">mu</span><span class="o">=</span><span class="mi">23</span><span class="p">)</span>
     <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;t&quot;</span><span class="p">)</span>
@@ -351,7 +351,7 @@ <h2>One mean / one proportion (point null)<a class="headerlink" href="#one-mean-
 <span class="n">coins</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;flip&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;heads&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">30</span> <span class="o">+</span> <span class="p">[</span><span class="s2">&quot;tails&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">70</span><span class="p">})</span>
 
 <span class="n">null_p</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">coins</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;flip&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;heads&quot;</span><span class="p">)</span>
+    <span class="n">coins</span><span class="o">.</span><span class="n">specify</span><span class="p">(</span><span class="n">response</span><span class="o">=</span><span class="s2">&quot;flip&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;heads&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;point&quot;</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
     <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;draw&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>   <span class="c1"># &quot;simulate&quot; is an alias</span>
     <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;prop&quot;</span><span class="p">)</span>
diff --git a/doc/_build/html/guides/plotting.html b/doc/_build/html/guides/plotting.html
index b759d8e..fe3d372 100644
--- a/doc/_build/html/guides/plotting.html
+++ b/doc/_build/html/guides/plotting.html
@@ -262,8 +262,13 @@ <h1>Plotting: plotly &amp; plotnine<a class="headerlink" href="#plotting-plotly-
 <li><p><code class="docutils literal notranslate"><span class="pre">engine=&quot;plotnine&quot;</span></code> — grammar-of-graphics figures (<code class="docutils literal notranslate"><span class="pre">plotnine.ggplot</span></code>).</p></li>
 </ul>
 <p>The composition syntax is identical across engines, so you can switch a whole
-analysis by changing one argument. (In this documentation, plotly figures are
-shown as static images; in a notebook they’re fully interactive.)</p>
+analysis by changing one argument.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The figures on this page are <strong>static images</strong> so they render in the docs. In a
+notebook or script, the default <code class="docutils literal notranslate"><span class="pre">engine=&quot;plotly&quot;</span></code> produces <strong>interactive</strong>
+figures (hover, zoom, pan).</p>
+</div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">md</span>
@@ -273,7 +278,7 @@ <h1>Plotting: plotly &amp; plotnine<a class="headerlink" href="#plotting-plotly-
 <span class="p">)</span>
 
 <span class="n">boot</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">md</span><span class="o">.</span><span class="n">load_almonds_sample_100</span><span class="p">(),</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span><span class="p">)</span>
+    <span class="n">md</span><span class="o">.</span><span class="n">load_almonds_sample_100</span><span class="p">()</span><span class="o">.</span><span class="n">specify</span><span class="p">(</span><span class="n">response</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">)</span>
 <span class="p">)</span>
@@ -342,7 +347,7 @@ <h2>Shading p-values<a class="headerlink" href="#shading-p-values" title="Link t
 <span class="n">obs</span> <span class="o">=</span> <span class="n">observe</span><span class="p">(</span><span class="n">spotify</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;popular_or_not ~ track_genre&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;popular&quot;</span><span class="p">,</span>
               <span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;metal&quot;</span><span class="p">,</span> <span class="s2">&quot;deep-house&quot;</span><span class="p">))</span>
 <span class="n">null</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">spotify</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;popular_or_not ~ track_genre&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;popular&quot;</span><span class="p">)</span>
+    <span class="n">spotify</span><span class="o">.</span><span class="n">specify</span><span class="p">(</span><span class="n">formula</span><span class="o">=</span><span class="s2">&quot;popular_or_not ~ track_genre&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;popular&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;independence&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;permute&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">76</span><span class="p">)</span>
     <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;metal&quot;</span><span class="p">,</span> <span class="s2">&quot;deep-house&quot;</span><span class="p">))</span>
@@ -388,7 +393,7 @@ <h2>Faceted regression-coefficient plots<a class="headerlink" href="#faceted-reg
 
 <span class="n">houses</span> <span class="o">=</span> <span class="n">md</span><span class="o">.</span><span class="n">load_saratoga_houses</span><span class="p">()</span>
 <span class="n">boot_fit</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">houses</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;price ~ living_area + bedrooms&quot;</span><span class="p">)</span>
+    <span class="n">houses</span><span class="o">.</span><span class="n">specify</span><span class="p">(</span><span class="n">formula</span><span class="o">=</span><span class="s2">&quot;price ~ living_area + bedrooms&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="o">.</span><span class="n">fit</span><span class="p">()</span>
 <span class="p">)</span>
diff --git a/doc/_build/html/guides/regression.html b/doc/_build/html/guides/regression.html
index c3b3698..5e39a28 100644
--- a/doc/_build/html/guides/regression.html
+++ b/doc/_build/html/guides/regression.html
@@ -392,16 +392,16 @@ <h2>Inference for regression coefficients<a class="headerlink" href="#inference-
 <span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">shade_confidence_interval</span><span class="p">,</span> <span class="n">shade_p_value</span>
 
 <span class="n">f</span> <span class="o">=</span> <span class="s2">&quot;price ~ living_area + bedrooms&quot;</span>
-<span class="n">obs_fit</span> <span class="o">=</span> <span class="n">specify</span><span class="p">(</span><span class="n">houses</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+<span class="n">obs_fit</span> <span class="o">=</span> <span class="n">houses</span><span class="o">.</span><span class="n">specify</span><span class="p">(</span><span class="n">formula</span><span class="o">=</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
 
 <span class="c1"># Bootstrap distribution of each coefficient → per-term CIs</span>
-<span class="n">boot_fit</span> <span class="o">=</span> <span class="n">specify</span><span class="p">(</span><span class="n">houses</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+<span class="n">boot_fit</span> <span class="o">=</span> <span class="n">houses</span><span class="o">.</span><span class="n">specify</span><span class="p">(</span><span class="n">formula</span><span class="o">=</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
 <span class="n">boot_fit</span><span class="o">.</span><span class="n">get_confidence_interval</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">)</span>          <span class="c1"># one row per term</span>
 <span class="n">visualize_fit</span><span class="p">(</span><span class="n">boot_fit</span><span class="p">)</span> <span class="o">+</span> <span class="n">shade_confidence_interval</span><span class="p">(</span><span class="n">boot_fit</span><span class="o">.</span><span class="n">get_confidence_interval</span><span class="p">())</span>
 
 <span class="c1"># Null distribution → per-term p-values, each facet shaded at its own estimate</span>
 <span class="n">null_fit</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">houses</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;independence&quot;</span><span class="p">)</span>
+    <span class="n">houses</span><span class="o">.</span><span class="n">specify</span><span class="p">(</span><span class="n">formula</span><span class="o">=</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;independence&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;permute&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
 <span class="p">)</span>
 <span class="n">null_fit</span><span class="o">.</span><span class="n">get_p_value</span><span class="p">(</span><span class="n">obs_stat</span><span class="o">=</span><span class="n">obs_fit</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s2">&quot;two-sided&quot;</span><span class="p">)</span>
diff --git a/doc/_build/html/guides/theory-based.html b/doc/_build/html/guides/theory-based.html
index 5360edf..7eb5c5c 100644
--- a/doc/_build/html/guides/theory-based.html
+++ b/doc/_build/html/guides/theory-based.html
@@ -332,7 +332,7 @@ <h2>Overlaying theory on a simulation<a class="headerlink" href="#overlaying-the
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">moderndive</span><span class="w"> </span><span class="kn">import</span> <span class="n">specify</span><span class="p">,</span> <span class="n">visualize</span>
 
 <span class="n">boot</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">age</span><span class="p">,</span> <span class="n">response</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">)</span>
+    <span class="n">age</span><span class="o">.</span><span class="n">specify</span><span class="p">(</span><span class="n">response</span><span class="o">=</span><span class="s2">&quot;age&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">)</span>
 <span class="p">)</span>
diff --git a/doc/_build/html/index.html b/doc/_build/html/index.html
index a25a3c6..0f12881 100644
--- a/doc/_build/html/index.html
+++ b/doc/_build/html/index.html
@@ -318,7 +318,7 @@ <h2>30-second example<a class="headerlink" href="#second-example" title="Link to
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Null distribution under &quot;genre doesn&#39;t matter&quot; (permutation)</span>
 <span class="n">null</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">specify</span><span class="p">(</span><span class="n">spotify</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="s2">&quot;popular_or_not ~ track_genre&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;popular&quot;</span><span class="p">)</span>
+    <span class="n">spotify</span><span class="o">.</span><span class="n">specify</span><span class="p">(</span><span class="n">formula</span><span class="o">=</span><span class="s2">&quot;popular_or_not ~ track_genre&quot;</span><span class="p">,</span> <span class="n">success</span><span class="o">=</span><span class="s2">&quot;popular&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">hypothesize</span><span class="p">(</span><span class="n">null</span><span class="o">=</span><span class="s2">&quot;independence&quot;</span><span class="p">)</span>
     <span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">reps</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;permute&quot;</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">76</span><span class="p">)</span>
     <span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">stat</span><span class="o">=</span><span class="s2">&quot;diff in props&quot;</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;metal&quot;</span><span class="p">,</span> <span class="s2">&quot;deep-house&quot;</span><span class="p">))</span>
@@ -348,6 +348,13 @@ <h2>30-second example<a class="headerlink" href="#second-example" title="Link to
 <img alt="_images/57bcfc0de676a5294afac27836489f257436cfdc208aba5a2907c8b2aa95d9f8.png" src="_images/57bcfc0de676a5294afac27836489f257436cfdc208aba5a2907c8b2aa95d9f8.png" />
 </div>
 </div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Plots throughout this documentation are rendered as <strong>static images</strong>. When you
+run the code yourself, the default <code class="docutils literal notranslate"><span class="pre">engine=&quot;plotly&quot;</span></code> produces <strong>interactive</strong>
+figures (hover, zoom, pan); <code class="docutils literal notranslate"><span class="pre">engine=&quot;plotnine&quot;</span></code> gives static grammar-of-graphics
+plots.</p>
+</div>
 </section>
 <section id="where-to-next">
 <h2>Where to next<a class="headerlink" href="#where-to-next" title="Link to this heading">¶</a></h2>
diff --git a/doc/_build/html/searchindex.js b/doc/_build/html/searchindex.js
index ebca82a..ff980dc 100644
--- a/doc/_build/html/searchindex.js
+++ b/doc/_build/html/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/doc/_build/jupyter_execute/datasets.ipynb b/doc/_build/jupyter_execute/datasets.ipynb
index 78fb45f..c5c5915 100644
--- a/doc/_build/jupyter_execute/datasets.ipynb
+++ b/doc/_build/jupyter_execute/datasets.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "eb0874e7",
+   "id": "6bc9e1d1",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3860619",
+   "id": "3c2f7e86",
    "metadata": {},
    "source": [
     "# Datasets\n",
@@ -31,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "ed09d3d4",
+   "id": "4c03ef56",
    "metadata": {},
    "outputs": [
     {
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a3349063",
+   "id": "65590d67",
    "metadata": {},
    "source": [
     "## By topic\n",
diff --git a/doc/_build/jupyter_execute/getting-started.ipynb b/doc/_build/jupyter_execute/getting-started.ipynb
index c22eb33..49f810d 100644
--- a/doc/_build/jupyter_execute/getting-started.ipynb
+++ b/doc/_build/jupyter_execute/getting-started.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "713ab722",
+   "id": "2329b818",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4b9b797",
+   "id": "e21e788c",
    "metadata": {},
    "source": [
     "# Getting started\n",
@@ -44,7 +44,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "03a0299c",
+   "id": "f0b1d8b2",
    "metadata": {},
    "outputs": [
     {
@@ -88,7 +88,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8c6e0ce4",
+   "id": "39f01525",
    "metadata": {},
    "source": [
     "List everything that's available with `md.available_datasets()` (58 datasets), and\n",
@@ -103,7 +103,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "01b3afca",
+   "id": "260e7425",
    "metadata": {},
    "outputs": [
     {
@@ -142,7 +142,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "99e2eb9f",
+   "id": "7b253c5d",
    "metadata": {},
    "source": [
     "## The inference pipeline\n",
@@ -154,7 +154,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "cdcd4d55",
+   "id": "f80d95c5",
    "metadata": {},
    "outputs": [
     {
@@ -196,7 +196,7 @@
     "\n",
     "# 2. A null distribution: specify → hypothesize → generate → calculate\n",
     "null = (\n",
-    "    specify(yawn, formula=\"yawn ~ group\", success=\"yes\")\n",
+    "    yawn.specify(formula=\"yawn ~ group\", success=\"yes\")\n",
     "    .hypothesize(null=\"independence\")\n",
     "    .generate(reps=1000, type=\"permute\", seed=42)\n",
     "    .calculate(stat=\"diff in props\", order=(\"seed\", \"control\"))\n",
@@ -208,7 +208,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b209acd5",
+   "id": "f3a03948",
    "metadata": {},
    "source": [
     "Each verb has a focused guide: {doc}`guides/sampling`,\n",
@@ -217,20 +217,25 @@
     "## Visualizing — choose your engine\n",
     "\n",
     "Plots default to **plotly** (interactive). Pass `engine=\"plotnine\"` for\n",
-    "grammar-of-graphics output. The composition syntax is identical:"
+    "grammar-of-graphics output. The composition syntax is identical:\n",
+    "\n",
+    "```{note}\n",
+    "The plots shown in this documentation are **static images**. Running the code\n",
+    "yourself yields **interactive** plotly figures by default.\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "aabd5f6b",
+   "id": "7f4a4ab2",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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",
       "text/plain": [
-       "<plotnine.ggplot.ggplot object at 0x1380d1f30>"
+       "<plotnine.ggplot.ggplot object at 0x133bd1f30>"
       ]
      },
      "execution_count": 5,
@@ -255,7 +260,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d04bd0cc",
+   "id": "ca3b71d2",
    "metadata": {},
    "source": [
     "See {doc}`guides/plotting` for shading, confidence-interval overlays, theoretical\n",
@@ -267,7 +272,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "d7cc0ce8",
+   "id": "681e85ae",
    "metadata": {},
    "outputs": [
     {
@@ -311,7 +316,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e324ad3f",
+   "id": "5de5f402",
    "metadata": {},
    "source": [
     "Full details in {doc}`guides/regression`."
@@ -350,10 +355,10 @@
    57,
    64,
    83,
-   93,
-   101,
-   108,
-   115
+   98,
+   106,
+   113,
+   120
   ]
  },
  "nbformat": 4,
diff --git a/doc/_build/jupyter_execute/guides/confidence-intervals.ipynb b/doc/_build/jupyter_execute/guides/confidence-intervals.ipynb
index 9087839..41c8f31 100644
--- a/doc/_build/jupyter_execute/guides/confidence-intervals.ipynb
+++ b/doc/_build/jupyter_execute/guides/confidence-intervals.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "6db8d2e6",
+   "id": "90c4c72c",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21ef3e5f",
+   "id": "d2d21e97",
    "metadata": {},
    "source": [
     "# Bootstrapping & confidence intervals\n",
@@ -33,7 +33,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "e03f85f1",
+   "id": "565a66b9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,7 +43,7 @@
     "almonds = md.load_almonds_sample_100()\n",
     "\n",
     "boot = (\n",
-    "    specify(almonds, response=\"weight\")\n",
+    "    almonds.specify(response=\"weight\")\n",
     "    .generate(reps=1000, type=\"bootstrap\", seed=1)\n",
     "    .calculate(stat=\"mean\")\n",
     ")"
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5398a005",
+   "id": "0e8ab101",
    "metadata": {},
    "source": [
     "## Confidence intervals — three methods"
@@ -60,7 +60,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "661d9537",
+   "id": "826b185b",
    "metadata": {},
    "outputs": [
     {
@@ -97,7 +97,7 @@
     "# shape: (1, 2) → lower_ci ≈ 3.61, upper_ci ≈ 3.75\n",
     "\n",
     "# Standard-error method (needs the point estimate)\n",
-    "point = float(specify(almonds, response=\"weight\").calculate(stat=\"mean\"))\n",
+    "point = float(almonds.specify(response=\"weight\").calculate(stat=\"mean\"))\n",
     "get_confidence_interval(boot, level=0.95, type=\"se\", point_estimate=point)\n",
     "\n",
     "# Bias-corrected\n",
@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c5a8df6f",
+   "id": "0a0ad7c0",
    "metadata": {},
    "source": [
     "Distribution objects also expose the getter as a method:"
@@ -115,7 +115,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "e7e34fbb",
+   "id": "f9ef37bb",
    "metadata": {},
    "outputs": [
     {
@@ -152,7 +152,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "278a565b",
+   "id": "ad157a9b",
    "metadata": {},
    "source": [
     "## Visualize the interval"
@@ -161,7 +161,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "ca8fb836",
+   "id": "eb989df6",
    "metadata": {},
    "outputs": [
     {
@@ -169,7 +169,7 @@
       "image/png": 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NWfSRjqTndZeJ5k+3roAFJCDBD0N6pV0zBANfvQqrV+t1mSb9EZCOQ0kaVGkbNSBKKlPGbKr9d8ugx3N+JGm3H9KsK6R6ZVc/dFzR4lxTlg40uLrx2kvNcfCldeVUl5WU9OvujdbVumC+WjWqyc1drgjOljjWK+j6oUuD6+pW3zXI/iGkngMJgQTRD5L6VfjcxR9KwOoHLXDthi06ykkaTN1o9UVSUpaL1qHr1ZeY9Wt+/N2MSzPQq8a6vV5x1nFRqbQWWq5+oLj4grNE21C/Xi0pa1091eWFpUYNT5KLrQ8BwXV6rqmn+mz+e3twcVTG+qFPC9YPqnp+6LSeY7d3a6uTstT6xsdMZA8ubHy66Dchwbz6Oqr9pt80ZGdhhAACDhSgSrETcFzAq02/+oqLZPnMseYq6MN33yTXWVf69IqSfu15Xa+HzJu05gslVbSuqmr+9IwMHeUkvdqqMxrw6Li0Kd0KzvRxS/pINb1aqLdWDBs9RfTrUC07+Kt1nbab1q7PPszUfgAAEABJREFUCkBO/OdRBTYJ3s+8fuPWAutyL6hSuaKZ1a/RzYQ1eG3muzLOuhKZO3329Q/WmkP/NUjSK8Sa9Eq5XiWcY12p1P4Y8fTrok/V0Nx2293ywrM1u0x6dZ40sa4WDxw2Qaa9vczcW21WWIPf/lhvDUX0vtLTW/SUYNLbAXTFpuxHcP2efSVSf+Sny0uTBvW6TrSNmvRq8ZeLnzM/ltR7M/XJIFq23X7YlB2gnlzIj6COO+ZILUr0UVRmooiBBittWp1nbh+47Pq7zL3menvDj/nuNy9i85zFyRUq5EznnnjPCpiadugn+kFSP7zo1/xLrQ9nmicjM+vJFTpdVAp+8Nq45e+isthevmFzVhlH1a1V6DbJFcpLZCzKFxvgFrrzfAv/+Y+s/tuc3cf5Vkds9r/WN1p67unvFnIXGrx//n9rC16Fz50vEAiIbq8fvHMvZxoBBBDwq4AjA17tjMTEBHOFVp9UMGRAN1n8xuM5Twd4P4wHwidYbwBabv6UmJCQf1Gp5p99ea553JJe3dOvGGdOfkhWzBorua/0hbqD/QcPmk2CVx7NTPYgKfuHY/qjnuxFhY4SEwq2U79uf+vZByR36nRls0K3z73wOOtN/+Yubcyi5dk/rLPb7uOPOVKWvDVarrr0ArP94uWrRB/9dcl1/5bg/Y56G4WufHDQjVJYurJVE10tO3dn3f/6j/p1zHwkB3qVuM+NV5lbEPSKswb0dvshGGQU1l9lsvvrgPUNQEn1feSem2XIgG7mGwm911x/wKY/XNJ7m0vatrj1eh/4nUPGmcf83dWns+hX9XqrxP39uxa3WZ51waA4MfuKfZ6VIc6syf7WRq86F7VptCyK2l9JywOBQElZSrV+9559Eryinrug4LKSzvfc2zCNAAIIICBSMAqKs0qa9XVwYVXQF/pg0PhzEff6FbZduMvCveIb/Cry1WcGm68YT/xnfdGrNMFAJ3990otob+58R9apaWYLuyoYvDpWp9bhJk8oAw1cTzvpGMmdct+XW1xZZawPJLpef5yn41DarU9A0ABm1YJnzVVV/VCjQe68JR9rUXLCsVlXsi9vca50bNO0QNKv5zVjsK7RegZtIBDQ3ZhbEDIlU+z2g/64TDcs7P7WjZu36SqpWydvfxV2HJSxgkn9dkO/htf7SMcM622u2ukTF3bu3mvKCQ7SrW8WgtMljT/I/gt0Y4b1ke6dWpvbYvQrfg3yS9o2uD7YtqKuygbzlTTWH6f+al3R19sF9Cv7ovJHy6Ko/RW1PPhNSvB+8+xDxPrwcKCoTQost9NX+rzrTdZVZL2NKHcBweOntO65y2QaAdcIUFEESiHguIC3+dX9zWOCCvtRiN5rqG3VXyHrOFrpiNqHm6+S9Q0nuA/96l7vdwzOFzXWR1HputxXlPX+2t//yvsVpAbwms/OPXbBr8GnzVsmud8AN1pf7S9e/rno7QU1D6+qxcUk6VMk3pyz1OzrlBP+YcZ2273SCrZ0e90oEAiIXgnX+411/j/ZT5vQp1vo/MRX5uooT9KAI/i1vgbsunJevr+IpU9D0OWlTXqPs37w0T+AogGX3X7QwE2vVurtEBrMBeuRZn240ds3dD54K0qVysk6K7nvrdYF+gFg5WdrdNIk/Vq/dbN/yZnZ9zmrg66oVqWSjmRj9m0BZqaEQfAHmklJWfdTa3atW9BV54tL+jSDl6ctMllOOzGr/81MCAM9jjXY1VsptG0P3dWzyK2jaVHkTgtZoUG+nm96H3T1all/pEI/zGrW734+9KQYfcrIZ9lPHNF1wRRKX51z+glmM/1hoJnIHsx8Z4WZOv2kY82YAQIIIICAPQHHBbz65qY/UmvT7R7zzFB97qQ+f1WfRamP6NE3Gztfu9trfuG5gs8Avv3uJ0Qfl6X34Dbv2N88zaHwLQ4t1R+P6Jw+R1S31ed/XnHD3aJfSevy3Ekfo6U/zNNHHekTHJ6YOC336pxpfVO9reuV5odrPfqPNI+10l/w618700x3W19LBwJZVyN1PpJJ+0Pv1dWk9dTnwJ7f7g5joVfc9ccxuj+77Z67+CNpdd0g8/QNfcbp1Lnvy0NPvqJFyPUdWplxz+suN1cy1U8dp81bLq/Pek/0uNBtv1qT9QPDVk3PMfk0iBz00LPy/OvzReun96SagkIY6K/itY16rOljzvR4eyD7sVuD77zBlBRKPwy4tZPZRh8tpX2rgcutg0YbN71qG7xCpz+c0oBPH7n2rBXg6w8BNf/2HbtEjz99rqreyrB4+SrRJ1LofbYagJ9wbH1Tvn6DoBN6nOm5os9e1VsWdFlRqdEZWcHU0MdfkmdenG1uwel0y1BjXNg2GvSr65Spi8yPHK+++QFzLOpfQKx5eLXCNimw7IvVP4va6vNvtV0XtOtrHg+mGZ97/N8S/OGazudP0bTIv6/c8yutD2d6Tmrf6WvPofOtS0624489ytz2osexPjJM+7BLn4dNW3MyZU+E0lc9rr3UbKXPTZ4wZY55NJze+qPngX5IvKxFY7OeAQIIIICAPQHHBbyP3neL6C+hNRDUoE7fRDQY0Pku7VvIK0/fJxWTy+dpnf7aPLggIfte1UAgEFxkxoFA1nzuvNkrzCj3QH+Bfn6jU01won8QYfr85VYw1lJ0mebLKkmnslLuMu+5o4t5nJX+uVTdVn/wdN7Zp0gwMAwEDm19r5W3dbNGMmvBBzLcCvo00NESA4GsPIFA1liX9e5xlfS5sb0JnPW5wfpGqPf56X3Cra0rf5pHU1Ht13Wagut12m7SIEyT1lMDd/36Wfc7wuqrYBl2261Py9CrY/r0DX3GqQa7//19nehfcjv37JNNcfpDwpmTh0ubVueZx5M9+MQUGfH0a+bKvzoGryrqI+YmP3GXucK98P3PTECoT5kI/gWvQOCQnym4sEF2ns++/tEK+JaIHmvvLP1UyiQmSqc2zUQfXRcMVHRzu/2gx8pTD91hfdWdavpWg3Xdhz7tQD+gaFmaNNjVR5apqQafI55+XfSZsYcfVtU8Mk+3eWz8mzJw2AQT0OuHpBH33iwJCVlt0ydb6CPD9H5hPVf0cX5rN26RQCBrve4jf7q6TVPRe6j1aQMaoOmPLMuXL5dzj3xhm/61brPoH/TQHzjqhyDd5203tM1fdIH5YFm6L7Wds+hDc16dYAWK/W66WpZOHyPa9gIbWguC51VpLayiiv0frGPw3AgEsuzKl0syjxLUvtPAV9s9avBtok9HCRZYtXJF0f7Qef3AoX2oz9kN9nEgkFWWri+qr3SdpmB7dfrww6rI7BcfFr3nfbwV8Gr/6wc7fW188cm7zfGp+QKBQ+XrfO6UmJggelwI/3wqQLMRQCC3gOMC3raXnC8TR/1bVi99wTxDVu9f1Gfarnn/JRl8Z1dzP2ywARXKl5Xvl08RDSyCyzTQ0GX6/MrgMh3rm5Qu1ydA6HwwXXz+maYMvY80uEwDrkmP/Vvem/qEzJz8kOgv9u/rd4Pos0u1jMqVkk3Wwvav90K++ewD5tm/WveP3x4v+uMwfZPSbatWqWi21cGxR9czz6H9ZP4E0ScDfPz2M7pYNPDTvBrgmwXWQAOw3t3byTdLJsvbUx4x5X9qbaePIrJW5/wvqv1aLy1z6MDuOXlLmtD7RnWb3GmleZbrYNH9ap2CZWj5dtqt25m2zhtv3tDffWu0fGxNB6/u5i5Pgws9DjTo1CdEaD+o4+m5HremgZM+x1fX6zOK1UT/gpfWWZ/wESyvqPE1VzYz/a/5cyd9WsOwQT0keCU2uL222U4/aP5WF50j+lxUfd6rBi9fvfu8aKCY/1FYemwGfzimx/pIK6iqVrWSOTa0/Wqk26uTPms4f500iFYjTR/OHWcCdf1QqO0ZM6y3ViVP0jboPdT6QzV9HrQGnW9OGCJ6bOg2zZucmSe/BuXqoWVr32mbdJ/BoDtP5nwzeoxrmbmTtlHbq99aBO/Dzr1Z/vOqtBZatj4/WI9nnc6f9IOk1k+PJV0XtNNjXdusTyXRc077Qj+EaZ7cSfv584WTZNYLw81rhhrpc3e1zOCV/mB+ddN+0qRl64eq/O0N5tVgV/tdf/SqryX6OqGvjfpNQzBPsK6F9bOeE1qXYF7GCCCAgJ8FHBfwBjtD35T13tSTjmsgOrbz5hrcNhLjQCAgei+vXt0L5cc8um+tq956oXXPHeDqusJSFSuA1ufIJiVlPee0sDzBZZpHgwgtX6/gBJeHM470NqG0W6+M6Ru6Pps2+KSJwuqjx4EGeNreovohuN9/1D9CYmVitx+0bnrbgrZVr0gX1sbgMv3QkP9Y1/arkW6vZsG8+ceBQMAE5/q85/zriprX/Z1ywtFSWNBZ2DZath6n2qbC1kd7WTQtiqq79pneK67nnO6/qHzJFcqZH1vqa0ZReYLLA4HQ+koDXH0t0deJYBmMEUAAAQRCE3BswBtaM8iNAAIIIIBA2AJsiAACHhcg4PV4B9M8BEojcFu3tvLAgG6lKYJtEUAAAQQQiLsAAa/dLiAfAj4U0Hvc9b5rHzadJiOAAAIIeEiAgNdDnUlTEEAAgVgIsA8EEEDAbQIEvG7rMeqLAAIIIIAAAgggEJJAlALekOpAZgQQQAABBBBAAAEEoiZAwBs1WgpGAAEERAQEBBBAAIG4CxDwxr0LqAACCCCAAAIIIOB9gXi2kIA3nvrsGwEEEEAAAQQQQCDqAgS8USdmBwggYF+AnAgggAACCERegIA38qaUiAACCCCAAAIIlE6ArSMqQMAbUU4KQwABBBBAAAEEEHCaAAGv03qE+iBgX4CcCCCAAAIIIGBDgIDXBhJZEEAAAQQQQMDJAtQNgeIFCHiL92EtAggggAACCCCAgMsFCHhd3oFU374AORFAAAEEEEDAnwIEvP7sd1qNAAIIIOBfAVqOgO8ECHh91+U0GAEEEEAAAQQQ8JcAAa+/+tt+a8mJAAIIIIAAAgh4RICA1yMdSTMQQAABBKIjQKkIIOB+AQJe9/chLUAAAQQQQAABBBAoRoCAtxgc+6vIiQACCCCAAAIIIOBUAQJep/YM9UIAAQTcKECdEUAAAQcKEPA6sFOoEgIIIIAAAggggEDkBOIR8Eau9pSEAAIIIIAAAggggEAJAgS8JQCxGgEEEIieACUjgAACCMRCgIA3FsrsAwEEEEAAAQQQQKBogSivIeCNMjDFI4AAAggggAACCMRXgIA3vv7sHQEE7AuQEwEEEEAAgbAECHjDYmMjBBBAAAEEEEAgXgLsN1QBAt5QxciPAAIIIIAAAggg4CoBAl5XdReVRcC+ADkRQAABBBBAIEuAgDfLgSECCCCAAAIIeFOAViEgBLwcBAgggAACCCCAAAKeFiDg9XT30jjbAmREAAEEEEAAAc8KEPB6tmtpGAIIIIAAAqELsAUCXhQg4PVir9ImBBBAAAEEEEAAgRwBAt4cCibsC5ATAQQQQAABBBBwjwABr3v6ihb9hYMAABAASURBVJoigAACCDhNgPoggIArBAh4XdFNVBIBBBBAAAEEEEAgXAEC3nDl7G9HTgQQQAABBBBAAIE4ChDwxhGfXSOAAAL+EqC1CCCAQHwECHjj485eEUAAAQQQQAABBGIk4LiAN0btZjcIIIAAAggggAACPhEg4PVJR9NMBBBwnQAVRgABBBCIkAABb4QgKQYBBBBAAAEEEEAgGgKlL5OAt/SGlIAAAggggAACCCDgYAECXgd3DlVDAAH7AuREAAEEEECgKAEC3qJkWF5AYPH3G+Xoe96R9uM/kvd+3ETCgGOAY8B3x0DfN782r4NPLvmlwGskCxBwiADVKESAgLcQFBYhgAACCCCAAAIIeEeAgNc7fUlLELAvQE4EEEAAAQR8JEDA66POpqkIIIAAAgggkFeAOX8IEPD6o59pJQIIIIAAAggg4FsBAl7fdj0Nty9ATgQQQAABBBBwswABr5t7j7ojgAACCCAQSwH2hYBLBQh4XdpxVBsBBBBAAAEEEEDAngABrz0nctkXICcCCCCAAAIIIOAoAQJeR3UHlUEAAQQQ8I4ALUEAAacIEPA6pSeoBwIIIIAAAggggEBUBAh4o8Jqv1ByIoAAAggggAACCERXgIA3ur6UjgACCCBgT4BcCCCAQNQECHijRkvBCCCAAAIIIIAAAk4QcFfA6wQx6oAAAggggAACCCDgKgECXld1F5VFAAEEsgQYIoAAAgjYFyDgtW9FTgQ8L5CRkSFr1qxxTdL6er5TaCACCCCAQHECttYR8NpiIhMC/hDIzMx0TbCrgbk/eoVWIoAAAgiUVoCAt7SCbI8AAs4XoIYIIIAAAr4WIOD1dffTeAQQQAABBBDwk4Bf20rA69eep90IIIAAAggggIBPBAh4fdLRNBMB+wLkRAABBBBAwFsCBLze6k9agwACCCCAAAKREqAczwgQ8HqmK2kIAggggAACCCCAQGECBLyFqbAMAfsC5EQAAQQQQAABhwsQ8Dq8g6geAggggAAC7hCglgg4V4CA17l9Q80QQAABBBBAAAEEIiBAwBsBRIqwL0BOBBBAAAEEEEAg1gIEvLEWZ38IIIAAAgiIYIAAAjEUIOCNITa7QgABBBBAAAEEEIi9AAFv7M3t75GcCCCAAAIIIIAAAqUWIOAtNSEFIIAAAghEW4DyEUAAgdIIEPCWRo9tEUAg7gJpaWmyYcMG1yStb9zRqAACCCDgMwEPBbw+6zmaiwACRiAQCMiyZctckxISeNk1HccAAQQQiKEAr7wxxGZXCCCAQEwE2AkCCCCAQB4BAt48HMwggAACCCCAAAIIeEUg2A4C3qAEYwQQQAABBBBAAAFPChDwerJbaRQCCNgXICcCCCCAgNcFCHi93sO0DwEEEEAAAQQQsCPg4TwEvB7uXJqGAAIIIIAAAgggIELAy1GAAAKhCJAXAQQQQAAB1wkQ8Lquy6gwAggggAACCMRfgBq4SYCA1029RV0RQAABBBBAAAEEQhYg4A2ZjA0QsC9ATgQQQAABBBCIvwABb/z7gBoggAACCCDgdQHah0BcBXwT8O7bf1DWb9wqGRmZhYLr8o1btklaenqh63fvSZHtO3cXuo6FCCCAAAIIIIAAAs4V8EXA23fwWDnn0lul1XWDpGmHfvLExGl5emTFJ6ul8RW9pEWngXJGi5tk2rzlOetT9u0X3f7cNr3lgnZ9pXPv4bJ1286c9UxEUICiEEAAAQQQQACBKAj4IuA94dj6MvvFh+XLxc/J8LtukhffWiBrfvzNcOqV30EPPSt39Gwvq5e+IGOH95UHn5giazdsMevfmL1UfvltrSyb8ZR8On+CJCYkyNjJM806BggggAACCERDgDIRQCCyAr4IeDWYPf6YI6V8ubLSrElDqV3zMPnky++N5KqvfxS9itu53cVSJjFRWl54tjQ4sras+OQbs37RslXSsU1TqVWjmlSulCxdO7aSWQs+kMzMwm+NMBsxQAABBBBAAAEEEHCMgC8C3tzaf6zdJJu2bBe96qvLN23dbgLcsmWTdNakYxvUlY2bt5tpzV+/Xm0zrYOj6tbSkezak2LG8RuwZwQQQAABBBBAAAE7Ar4KePem7Jf+D4yTs047Xi7412nGZ9fuvZJcobyZDg7KWVeC9UdqehVXr/7qleGcddmBcYpVli7buz9N/JL2H8z6QV96RqboNCndcw56TJOiL8C5E+Fzx3ptipVpWnqGOUBS0zJ889rvl/c4p7fTHHgMwhbwTcCr9+oOGPqMpFsvVuMe7ieJiVlNr1K5ormlIbfggQMHze0LgUDABMMHDqbmrA5OJydnBckJVh4/JYWwmix+arOf2qr9S4q+gJ+OKa+1NSABc4AEAgFeBzGI6TEg/CuVQFbUV6oinL/xrj0p0uueMbJz11555en7pFrVSjmVrl3jMNHbFlJT03KW6Y/U6tQ6zMzr/bx/rttkpnXw1/rNOpIqlZLNuEK5RPFLKpuUdbjoG5hOkxLEawbmoGYQdQGvHTd+ak9iYsAcH2WssV9e+2mnM97nzYHHIGyBrAgm7M2dv2HKvgNyQ5+HZfPW7fLQXT1l7779sm7jVtmweZupfKOGJ5rxm3OWmmfwvrfyS/OEhqbnNTTLWzdrJNPnLbe23yF79u6TV2cskQ6XXySBQNaLnsnEAAEEEHCNABVFAAEE/Cfg+YBX78X99Y/15ipuh5uGyCXXDTLpmluHmt5OrlBOxj1yp4wa/6Z5Bu+dQ8bJ/f27ypFH1DTru7RvKcc0qCvNO/Y3z+rVK8F9e3Yw6xgggAACCCCAAAIIOF+g0IDX+dW2X0N9BNn3y6dI/rRyzricQi4+/0z5dumL8u5bo+WbJZOl81UtctZVTC4vz44cIB/PGy8rZo2VqZOGmkeU5WRgAgEEEEAAAQQQQMDRAp4PeO3q64/Y6tWpIUlJZQrdpGrlilKjetVC17EQAQQ8K0DDEEAAAQQ8IEDA64FOpAkIIIAAAggggEB0BdxdOgGvu/uP2iOAAAIIIIAAAgiUIEDAWwIQqxFAwL4AORFAAAEEEHCiAAGvE3uFOiGAAAIIIICAmwWou8MECHgd1iFUBwEEEEAAAQQQQCCyAgS8kfWkNATsC5ATAQQQQAABBGIiQMAbE2Z2ggACCCCAAAJFCbAcgWgLEPBGW5jyEUAAAQQQQAABBOIqQMAbV352bl+AnAgggAACCCCAQHgCBLzhubEVAggggAAC8RFgrwggELIAAW/IZGyAAAIIIIAAAggg4CYBAl439Zb9upITAQQQQAABBBBAIFuAgDcbghECCCCAgBcFaBMCCCAgQsDLUYAAAggggAACCCDgaQECXhHxdA/TOAQQQAABBBBAwOcCBLw+PwBoPgIIIJBLgEkEEEDAkwIEvJ7sVhqFAAIIIIAAAgggEBQIPeANbskYAQQQQAABBBBAAAEXCBDwuqCTqCICCDhTgFohgAACCLhDgIDXHf1ELRFAAAEEEEAAAacKOL5eBLyO7yIqiAACCCCAAAIIIFAaAQLe0uixLQII2BcgJwIIIIAAAnESIOCNEzy7RQABBBBAAAF/CtDq2AsQ8MbenD0igAACCCCAAAIIxFCAgDeG2OwKAfsC5EQAAQQQQACBSAkQ8EZKknIQQAABBBBAIPIClIhABAQIeCOASBEIIIAAAggggAACzhUg4HVu31Az+wLkRAABBBBAAAEEihQg4C2ShhUIIIAAAgi4TYD6IoBAYQIEvIWpsAwBBBBAAAEEEEDAMwIEvJ7pSvsNIScCCCCAAAIIIOAnAQJeP/U2bUUAAQQQyC3ANAII+ESAgNcnHU0zEUAAAQQQQAABvwoQ8JbU86xHAAEEEEAAAQQQcLUAAa+ru4/KI4AAArETYE8IIICAWwUIeN3ac9QbAQQQQAABBBBAwJZAhANeW/skEwIIIIAAAggggAACMRMg4I0ZNTtCAAFfCdBYBBBAAAHHCBDwOqYrqAgCCCCAAAIIIOA9ASe0iIDXCb1AHRBAAAEEEEAAAQSiJkDAGzVaCkYAAfsC5EQAAQQQQCB6AgS80bOlZAQQQAABBBBAIDQBckdFgIA3KqwUigACCCCAAAIIIOAUAQJep/QE9UDAvgA5EUAAAQQQQCAEAQLeELDIigACCCCAAAJOEqAuCNgTIOC150QuBBBAAAEEEEAAAZcKEPC6tOOotn0BciKAAAIIIICAvwUIeP3d/7QeAQQQQMA/ArQUAd8KEPD6tutpOAIIIIAAAggg4A8BAl5/9LP9VpITAQQQQAABBBDwmAABr8c6lOYggAACCERGgFIQQMA7AgS83ulLWoIAAggggAACCCBQiAABbyEo9heREwEEEEAAAQQQQMDpAgS8Tu8h6ocAAgi4QYA6IoAAAg4WIOB1cOdQNQQQQAABBBBAAIHSC8Qy4C19bUtZQkZGpqSnZ4RVyu49KbJ95+6wtmUjBBBAILfAgQMHZP/+/a5KuevPNAIIIOA2Ad8EvJmZmfLgmCny0JMvF+ijtt3vk1Oa9ciTJkyZY/Kl7NsvfQePlXPb9JYL2vWVzr2Hy9ZtO806BggggEA4AmXKlJEvv/zSNals2bLhNJNtEEAAAccI+CLgXbx8lVzUvp/MmL+iSPh+N10tC14blZO6tG9p8r4xe6n88ttaWTbjKfl0/gRJTEiQsZNnmnUMEEAAgXAF/vjjD3FLCreNbIcAAgiUKBCjDL4IeC9sfIZMf/5BadPqvCJZax5eVRocWTsnVataSfTfomWrpGObplKrRjWpXClZunZsJbMWfCB6xVjXkxBAAAEEEEAAAQScLeCLgDe5QjmpU7O6VEyuUGRvTLeu/t4/6gWZMGWO/LluU06+P9Zukvr1aufMH1W3lpnetSfFjBkggEDUBdgBAggggAACpRLwRcBbklDrZo3konNPl7p1asj7H30tV9881AS9ehVX7+EtX+7Q/WvlyiaZ4lJS9ptxalqG+CWlZf/gLyMzU3SalOE5B3NQM0CgEAHO96zzXX/8rDw69strP+10xvu8HnciDMMVIOC15Prc2F56dWsnvbu3kzcnDJHKlSrI0pVfSSAQkOQK5eXAwVQrV9b/4HRycnmzIOVAuvglHUzNesJFRqbIAWualOE5B3NQM0CgEAHO96zzPV1fAC2f1PRM37z2++U9zunttA47/pdCgIA3H15SUhmpWb2a7Dtw0KzR+3pz3+Lw1/rNZnmVSslmXLVikvglJZcvY9pcJiEgFa1pUhlHOpSmX0wHM0CgEIHSHFde2japTILRKZeU4JvXfr+8xzm9nebAYxC2QNaZG/bm7thQn72bmpom6enpkpaWLjqtX0dp7TWYnTJtkWzcsk1SrXXzl3wi3/38uzQ+8yRdLXq7w/R5y2Xz1h2yZ+8+eXXGEulw+UXm6q/JwAABBBBAAAEEnChAnRDIEfBFwDvznRXSsNXN5rFkcxZ9aKbnLFqZg/DK9MXSotNAadjyJrn7kUlyd5/zpizvAAAQAElEQVTOcvbpx5v1+niyYxrUleYd+0vjK3qZYLlvzw5mHQMEEEAAAQQQQAAB5wv4IuC9pm1z+X75lDxJr9Jq9+gTGJZOGyMfzH5aFr3xmKxe+oJ069RaV5lUMbm8PDtygHw8b7ysmDVWpk4aah5RZlYycL8ALUAAAQQQQAABzwv4IuAtqRcDgYAcflgV0UeOlUlMlML+Va1cUWpUr1rYKpYhgAACCCDgegEagICXBQh4vdy7tA0BBBBAAAEEEEBACHg5CEIQICsCCCCAAAIIIOA+AQJe9/UZNUYAAQQQiLcA+0cAAVcJEPC6qruoLAIIIIAAAggggECoAgS8oYrZz09OBBBAAAEEEEAAAQcIEPA6oBOogjcFMjIy5Ntvv3VN0vp6sydoVfwFqAECCCAQXwEC3vj6s3cPC2RmZsp3333nmuThrqBpCCCAAAI+F3BMwOvzfqD5CCCAAAIIIIAAAlESIOCNEizFIoAAAmEKsBkCCCCAQIQFCHgjDEpxCCCAAAIIIIAAApEQiFwZBLyRs6QkBBBAAAEEEEAAAQcKEPA6sFOoEgII2BcgJwIIIIAAAiUJEPCWJMR6BBBAAAEEEEDA+QLUsBgBAt5icFiFAAIIIIAAAggg4H4BAl739yEtQMC+ADkRQAABBBDwoQABrw87nSYjgAACCCDgdwHa7y+BuAS8q77+SX7+9a8C0lv+3iHz3v1Y0tLTC6xjAQIIIIAAAggggAAC4QjEJeB9dcZief+jrwrUNzU1Te4Z8Zz8tW5zgXUsQCD2AuwRAQQQQAABBLwgEJeAtyi4Hbv2mFWJiY6qlqkTAwQQQAABBHwrQMMRcLlATCNLvXp7+91PyKdf/ShzFn4oOh1MPQeMkk63DpOTjmsg9evVdjkr1UcAAQQQQAABBBBwikBMA96kMmWkbNkkKV8uSfQqrk4HU43qVWXYoB4y4dEBTrGhHqEJkBsBBBBAAAEEEHCkQEwD3uF39ZSnh/eTgbddI0P6dzPTOq/psSG3S6c2zaRWjWqOhKJSCCCAAAII2BMgFwIIOE0gpgFvsPHtL7tQzjvnFMnIyJS9KfsLpGA+xggggAACCCCAAAIIlFYgLgHv5q07ZPiTr0jTDv3kX5ffXiDt3L23tO1y/PZUEAEEEEAAAQQQQCA2AnEJeCe/MV/emvu+dG7fUkbce4vo7Qy5U3L5crFpPXtBAAEEEIi3APtHAAEEoi4Ql4B34fufyW1dr5Te3dtJu9bnyxUtzs2TkpLKRL3h7AABBBBAAAEEEEDAHwJxCXhPOeFo2b0nxb4wORFAAAEEEEAAAQQQCFMgLgFv146tZc6ij2Trtp1hVpvNEEAAAX8K0GoEEEAAgdAF4hLwzl64UlL27ZemHe6UU5r1KJD40VroHckWCCCAAAIIIICAjwRCampcAt7LmjeW/+t1XZGpfLmyITWCzAgggAACCCCAAAIIFCUQl4C3xYVnSY9rLy0ylSubVFR9WY4AAgjYFyAnAggggAAClkCClWL+PzMzU4pLMa8QO0QAAQQQQAABBDws4PemxSXgvfOBcXJq8xuLTNzD6/fDkvYjgAACCCCAAAKRE4hLwNupTTMZOrB7gVS9WmW5sPFpUoF7eCPXw5SEgG0BMiKAAAIIIOBNgbgEvBc2Pl2uadu8QBp42zXy5bf/8aY0rUIAAQQQQAABdwhQS88JxCXgLUrx7NOPN48r++//1hWVheUIIIAAAggggAACCIQk4JiANyMjUz5c9Z2pfKWKyWbMAAEHC1A1BBBAAAEEEHCJQFwC3iGPvSgXXtU3Tzrt4hvlkbGvSutmjaR+vVou4aOaCCCAAAII+F2A9iPgfIG4BLznnn2yXNfu4jxJ/xDF7BcfljHD+jhfjRoigAACCCCAAAIIuEYgLgHvFS3OlT43ts+T9A9RHH/Mka6Bo6KhCZAbAQQQQAABBBCIl0BcAl5t7IGDqTJ74UoZ+cwbMnT0S/L86/Plr/WbdRUJAQQQQAABrwrQLgQQiINAXALerdt2Stvu98n9o16QV2e8KwuWfiZPPT9DLu1ylyxe/nkcGNglAggggAACCCCAgFcF4hLwjp08U7bt2C0vj71Xvnr3efl84USZ98qj0qxJQxk4bLzs23/Qq9722kUuBBBAAAEEEEAAgYgJxCXg/ejzNdL5qovlnDNOkHJlk0xjjql/hPTt2cFM//oHz+E1EAwQQAABnwvQfAQQQCASAnEJeKtWrii7dqcUqH9hywpkYgECCCCAAAIIIIAAAiEIxCXg1T8tPH3+ctH0x9pNsnP3Xnn/o6/liYlTpXq1ynLCsfVDaAJZEUAAAQQQQAABBBAoWiAuAW+v7lfJ+Y1OlWGjp8jlN9wtTa7sI30Hj5X1m7bK2OH9JKlMYtE1Zg0CCCCAQOECLEUAAQQQKFQgLgFvhfJl5bnHB8lrzwyWoQO7i/7RibHD+8riNx+Xs047rtCKshABBBBAAAEEEEAAATsC+fPEJeD98T9/yJhJ0+Tww6rKNW2bi/7RiZYXni2vzVxins2bv5LMI4AAAggggAACCCAQrkBcAl4NbFd+9q3Uq1MjT70rVUw2z+blsWR5WJhBAIGoCFAoAggggIBfBOIS8K758Te5ouV5kpiYd/etLjrbuP+5bpMZM0AAAQQQQAABBBCIsoAPis8bccaowRXKl5N1G7cW2Nuf6zabZUlJZcyYAQIIIIAAAggggAACpRWIS8B71unHy7S3l8nqH36V9PQM04bNW3fI86/Pk+QK5eWoI2qaZQwQQMAxAlQEAQQQQAAB1wrEJeC9vWtbqV3zMOnSe7ic26a3tO95vzTv2F9WfrZGHhjQTbjC69rjiYojgAACCCDgcQGa50aBuAS8VatUlPmvPCqDbr9W9I9Q1Kl1uHTr1FqmTRomV17SxI2O1BkBBBBAAAEEEEDAoQJxCXjVQm9duPG6y2TMsN7y7MgBcnefznLKCUfrKhICrhegAQgggAACCCDgHIG4BbzxIMjIyMy5Zzj//nXdxi3bJC09Pf8qM797T4ps37nbTDNAAAEEEEAAAVsCZELAEQK+CXgzMzPlwTFT5KEnXy4Av+KT1dL4il7SotNAOaPFTTJt3vKcPCn79kvfwWPNvcYXtOsrnXsPl63bduasZwIBBBBAAAEEEEDA2QK+CHgXL18lF7XvJzPmryjQG/pHLgY99Kzc0bO9rF76guifOH7wiSmydsMWk/eN2Uvll9/WyrIZT8mn8ydIYkKCjJ0806xjECEBikEAAQQQQAABBKIo4IuA98LGZ8j05x+UNq3OK0C56usfRa/idm53sZRJTBT9E8cNjqwtKz75xuRdtGyVdGzTVGrVqCaVKyVL146tZNaCD0SvGJsMDBBAAAEEEIiQAMUggEB0BHwR8CZXKCd1alaXiskVCihu2rpdNMAtWzYpZ92xDerKxs3bzfwfazdJ/Xq1zbQOjqpbS0eya0+KGWvg66ekjc60Bn5qc7httZj4j4BnBMI9D7y2nXW1I6dPvdY22pNpda9zU86Bx0RYAr4IeIuT2bV7r/ljF7nzlCtXVvRHanry69Xf8tZ8cH257MA4JWW/WfT3roMS2xS//e3el2banJaeIbtT0kjFGOyx1ukHAwPmkoHWV5NLqmuqqfXVZGZcMtD6anJJdU01tb6c81mveQdSs/5YUsqBdN+89vvlPc7p7TQnI4OwBXwf8FapXNHc0pBb8MCBg+b2hUAgYILhAwdTc1YHp5OTy5tlNaqWE7+kKslZV8GTEhOkSsUkUjEGla11AXOEuGeg9dXknhqLaH01iYv+aX01uajKxtmR57x1nsW6XuXKJpquSy6X6JvXfr+8xzm9nebAYxC2QELYW3pkw9o1DhO9bSE1NS2nRfojtTq1DjPzervDn+s2mWkd/LV+s46kSqVkM2aAAAIIIIAAAggg4GwBrwe8Rj/d+gpeA9r09HRJS0sXndbn7urKRg1P1JG8OWeppFnr31v5pXlCQ9PzGprlrZs1kunzlsvmrTtkz9598uqMJdLh8oskEHDbNRrTHAYIIIAAAggggIDvBHwR8M58Z4U0bHWzeSzZnEUfmuk5i1aaztYftI175E4ZNf5N8wzeO4eMk/v7d5Ujj6hp1ndp31KOaVBXmnfsb57Vq8Fy354dzDoGCCCAgHsEqCkCCCDgXwFfBLzXtG0u3y+fkifpVdpgt198/pny7dIX5d23Rss3SyZL56taBFdJxeTy8uzIAfLxvPGyYtZYmTppqHlEWU4GJhBAAAEEEEAAAQQcLZAn4HV0TaNcucTEBKlXp4YkJZUpdE9VK1eUGtWrFrqOhQgggAACCCCAAALOFSDgdW7fUDMEEIifAHtGAAEEEPCQAAGvhzqTpiCAAAIIIIAAApEV8EZpBLze6EdagQACCCCAAAIIIFCEAAFvETAsRgAB+wLkRAABBBBAwMkCBLxO7h3qhgACCCCAAAJuEqCuDhUg4HVox1AtBBBAAAEEEEAAgcgIEPBGxpFSELAvQE4EEEAAAQQQiKkAAW9MudkZAggggAACCAQFGCMQKwEC3lhJsx8EEEAAAQQQQACBuAgQ8MaFnZ3aFyAnAggggAACCCBQOgEC3tL5sTUCCCCAAAKxEWAvCCAQtgABb9h0bIgAAggggAACCCDgBgECXjf0kv06khMBBBBAAAEEEEAgnwABbz4QZhFAAAEEvCBAGxBAAIFDAgS8hyyYQgABBBBAAAEEEPCggK8DXg/2p2eblJGRIWvWrHFN0vp6tjNoGAIIIIAAAi4TIOB1WYf5tbqZmZmuCXY1MPdrP9Fu1wpQcQQQQMDTAgS8nu5eGocAAghERiA1NVXS0tJck7S+kWk5pSCAgBcE7Ae8XmgtbUAAAQQQCEsgISFBlixZ4pqUmJgYVjvZCAEEvClAwOvNfqVVCCAQRQG/Fr19+3ZxS/JrH9FuBBAoXICAt3AXliKAAAIIIIAAAggUL+CatQS8rukqKooAAggggAACCCAQjgABbzhqbIMAAvYFyIkAAggggECcBQh449wB7B4BBBBAAAEE/CFAK+MnQMAbP3v2jAACCCCAAAIIIBADAQLeGCCzCwTsC5ATAQQQQAABBCItQMAbaVHKQwABBBBAAIHSC1ACAhEUIOCNICZFIYAAAggggAACCDhPgIDXeX1CjewLkBMBBBBAAAEEEChRgIC3RCIyIIAAAggg4HQB6ocAAsUJEPAWp8M6BBBAAAEEEEAAAdcLEPC6vgvtN4CcCCCAAAIIIICAHwUIeP3Y67QZAQQQ8LcArUcAAZ8JEPD6rMNpLgIIIIAAAggg4DcBAt6iepzlCCCAAAIIIIAAAp4QIOD1RDfSCAQQQCB6ApSMAAIIuF2AgNftPUj9EUAAAQQQQAABBIoViFDAW+w+WIkAAggggAACCCCAQNwECHjjRs+OEUDAkwI0CgEEEEDAcQIEvI7rEiqEAAIIIIAAAgi4X8BJLSDgdVJvBiHV8wAAEABJREFUUBcEEEAAAQQQQACBiAsQ8EaclAIRQMC+ADkRQAABBBCIvgABb/SN2QMCCCCAAAIIIFC8AGujKkDAG1VeCkcAAQQQQAABBBCItwABb7x7gP0jYF+AnAgggAACCCAQhgABbxhobIIAAggggAAC8RRg3wiEJkDAG5oXuRFAAAEEEEAAAQRcJkDA67IOo7r2BciJAAIIIIAAAgioAAGvKpAQQAABBBDwrgAtQ8D3AgS8vj8EAEAAAQQQQAABBLwtQMDr7f613zpyIoAAAggggAACHhUg4PVox9IsBBBAAIHwBNgKAQS8J0DA670+pUUIIIAAAggggAACuQQIeHNh2J8kJwIIIIAAAggggIBbBAh43dJT1BMBBBBwogB1QgABBFwgQMBrs5N270mR7Tt328xNNgQQQAABBBBAAAGnCMQi4HVKW4usR9vu98kpzXrkSROmzDH5U/btl76Dx8q5bXrLBe36Sufew2Xrtp1mHQMEEEAAAQQQQAAB5wsQ8Gb3Ub+brpYFr43KSV3atzRr3pi9VH75ba0sm/GUfDp/giQmJMjYyTPNOgYIIIBAaALkRgABBBCIhwABb7Z6zcOrSoMja+ekalUrif5btGyVdGzTVGrVqCaVKyVL146tZNaCDyQzM1NXkxBAAAEEEEAAAQRCFYhxfgLebPDp81fI/aNekAlT5sif6zZlLxX5Y+0mqV+vds78UXVrmelde1LMmAECCCCAAAIIIICAswUIeK3+ad2skVx07ulSt04Nef+jr+Xqm4eaoFev4uo9vOXLlbVyZf0vVzbJTKSk7DfjHXsOil/S3v1pps1p6Zmyd19aTJPZMQO/CdBeBEolEI3XqdTUDFOn/QfTffPa75f3OKe30xx4DMIWIOC16Prc2F56dWsnvbu3kzcnDJHKlSrI0pVfSSAQkOQK5eXAwVQrV9b/4HRycnmzoGL5MuKXVC4p63BJsEblyiZILJPBZoAAAgiEIBCN16jExICpQdkyCb557ffLe5yz21nGHHcMwhewQpfwN/bilklJZaRm9Wqy78BB0zy9rzf3LQ5/rd9slleplGzGSdaLnl9SmcSswyXB+iCg07FMBpsBAgggEIJANF6jEhKyAl4d++W1n3YmiBMMhH+lEsiKYEpVhLs31mB2yrRFsnHLNklNS5f5Sz6R737+XRqfeZJpmN7uMH3ectm8dYfs2btPXp2xRDpcfpG5+msyMEDAIQJUAwEEEEAAAQQKF/B9wKssr0xfLC06DZSGLW+Sux+ZJHf36Sxnn368rhJ9PNkxDepK8479pfEVvSQ1NU369uxg1jFAAAEEEEAAAccJUCEECgj4PuDVJzAsnTZGPpj9tCx64zFZvfQF6dapdQ5UxeTy8uzIAfLxvPGyYtZYmTppqHlEWU4GJhBAAAEEEEAAAQQcLeD7gFd7JxAIyOGHVRF95FiZxEQp7F/VyhWlRvWqha1imRsFqDMCCCCAAAII+EaAgNc3XU1DEUAAAQQQKCjAEgT8IEDA64depo0IIIAAAggggICPBQh4fdz59ptOTgQQQAABBBBAwL0CBLzu7TtqjgACCCAQawH2hwACrhQg4HVlt1FpBBBAAAEEEEAAAbsCBLx2peznIycCCCCAAAIIIICAgwQIeB3UGVQFAQQQ8JYArUEAAQScIUDA64x+oBYIIIAAAggggAACURKIe8AbpXZRLAIIIIAAAggggAACRoCA1zAwQAABBOIuQAUQQAABBKIkQMAbJViKRQABBBCIr0BGRoZ8+umnEU1r1641jfrpp59kzpw5EU1aX1M4AwR8LxB5AALeyJtSIgIIIICAAwQyMzPlt99+i2javn27adnGjRvlm2++iWjS+prCGSCAQMQFCHgjTur8AlNSUiQ1NTXklJaWZhqXYb2J6HSs0oEDB8x+GSCQW4BpBBBAAAEE7AoQ8NqV8lC+smXLypNPPhlymjdvnlHYsGGDzJ07N2YpKSnJ7JcBAggggAACCBQQYIENAQJeG0hezKJXeUNNwSutGRnpotOxSl70p00IIIAAAgggEDsBAt7YWbMnBOInwJ4RQAABBBDwsQABr487n6YjgAACCCDgNwHa608BAl5/9jutRgABBBBAAAEEfCNAwOubrqah9gXIiQACCCCAAAJeEiDg9VJv0hYEEEAAAQQiKUBZCHhEgIDXIx1JMxBAAAEEEEAAAQQKFyDgLdyFpfYFyIkAAggggAACCDhagIDX0d1D5RBAAAEE3CNATRFAwKkCBLxO7RnqhQACCCCAAAIIIBARAQLeiDDaL4ScCCCAAAIIIIAAArEVIOCNrTd7QwABBBDIEmCIAAIIxEyAgDdm1OwIAQQQQAABBBBAIB4Czg544yHCPhFAAAEEEEAAAQQ8JUDA66nupDEIIOBVAdqFAAIIIBC+AAFv+HZsiQACCCCAAAIIIBBbgbD2RsAbFhsbIYAAAggggAACCLhFgIDXLT1FPRFAwL4AORFAAAEEEMglQMCbC4NJBBBAAAEEEEDASwK0JUuAgDfLgSECCCCAAAIIIICARwUIeD3asTQLAfsC5EQAAQQQQMDbAgS83u5fWocAAggggAACdgXI51kBAl7Pdi0NQwABBBBAAAEEEFABAl5VICFgX4CcCCCAAAIIIOAyAQJel3UY1UUAAQQQQMAZAtQCAfcIEPC6p6+oKQIIIIAAAggggEAYAgS8YaCxiX0BciKAAAIIIIAAAvEWIOCNdw+wfwQQQAABPwjQRgQQiKMAAW8c8dk1AggggAACCCCAQPQFCHijb2x/D+REAAEEEEAAAQQQiLgAAW/ESSkQAQQQQKC0AmyPAAIIRFKAgDeSmpSFAAIIIIAAAggg4DgBFwe8jrOkQggggAACCCCAAAIOFCDgLWWn7Nu3T9LT012TUlJSStliNkcAAccJUCEEEEAAgWIFCHiL5Sl5ZVJSkowaNco1qVy5ciU3ihwIIIAAAggggIALBYqqMgFvUTIhLD948KC4JYXQLLIigAACCCCAAAKeECDg9UQ30ggEELAvQE4EnCugt8lt2LBB3JK0vs7VpGYIHBIg4D1kwRQCCCCAAAJxFdDb5CZNmiRuSWXLlo2rFzsvpYCPNifg9VFn01QEEEAAAQQQQMCPAgS8fux12oyAfQFyIoAAAggg4HoBAl7XdyENQAABBBBAIH4Ceh/v5s2bxS1J6xueFlu5WYCA12bv7d6TItt37raZm2wIIIAAAgj4Q0DvO54wYYK4JXHfsT+Oy/ytJODNL5JvPmXffuk7eKyc26a3XNCur3TuPVy2btuZLxezCGQJMEQAAQQQQAAB5wkQ8JbQJ2/MXiq//LZWls14Sj6dP0ESExJk7OSZJWzFagQQQAABBHwtQOMRcJQAAW8J3bFo2Srp2Kap1KpRTSpXSpauHVvJrAUfSGZmZglbshoBBBBAAAEEEEDACQIEvCX0wh9rN0n9erVzch1Vt5aZ3rUnxYwZlEKATRFAAAEEEIiDgP511LS0NHFLOnDgQByUvLVLAt5i+lOv4uo9vOXLHXqwdrmySWaLlJT9ZiyBgDRt2tQ1SeurKZw6n3LKKabNVapUkVNPPTVmyezUco7lPku7L+ocm+MDZ5yLOlejdWzUqpV10ePoo4+O6Ou+vibra7MmnXZL0vpqckt9tZ5a34TERPnyyy9dk/SHgeaYZhC2AAFvMXSBQECSK5SXAwdTc3IFp5OTy5tlO/akyWlnNXFN2r47VcKtc4/LLpIv7m0p03q3kG6XXhSzdNaR1eVsK8Vyn6XdF3WOzfGBM85FnavROjYm3NjcvA4OuaZpxF/3S/P6HK/3ITfX+dgTG4pb0jbrvdsEHQzCFiDgLYGuwZG15c91m3Jy/bV+s5muUinZjGtULSexSewHZ44BjgGOAY4BjgG/HgMm6GAQtgABbwl0rZs1kunzlsvmrTtkz9598uqMJdLh8ousb0QCJWzJagQQQACBqAhQKAIIIBCiAAFvCWBd2reUYxrUleYd+0vjK3pJamqa9O3ZoYSt4rs6LT1dNm3ZLhs2/S3p6Rm2K6NtW7dxqxzMdQuH7Y3JWCoBvVVm7YYtsm1HaH/c5O/tu0RTqXbOxhEXSAvzHIx4RSjQtkC456DtHZAxpgLhnIP6m52du/bGtJ7sLHYCXg14IyZYMbm8PDtygHw8b7ysmDVWpk4aah5RFrEdRLigqXPflzNa3CQXdxogLa/9t7S67t/y3c+/F7uX3//cIF37jpCGrW6WS64bJLMWriw2PysjK3D/qBfkrEtukdad/08uvKqv6YsdO/cUuZOMjEx5/vX5Ju9F7fvJpV3uysmrAfMpzXpI/vTpVz/k5GEiugKhnoPa5/n7S+f/+/u66FaU0nMEQjkHZ1uvj9o/+dPAYRNMeZyDhiGug1DPQb1ApH9gqmmH/uZ9U98Pf/zPH3FtAzuPvAABr03TqpUrSo3qVW3mjl82/ZHdxFED5fOFk+ST+RPkn0fXkzETpxVZIT3R23S7V2rXPExeHXeffLHoOdHbOIrcgBURF9BH3U1/bph8894LsvD1UfK/vzbItHnLitzPk89Nl1emL5bbu7WTlXPGybxXRuTk1SeL6MzEUf+WBa+NyklnnPxPXUyKgUCo5+AbE4bk9JP22egHepla6nO/zUSpBxRQkkAo52Cri87J01/aZ6effKwcflhlsxvOQcMQ10Go5+ATE6eKXuH/+O1nrPfN8XL0UXVk7OQZcW0DO4+8AAFv5E3jWuKVlzSRCxufLskVyon+sK6KFahXq5r1QiyF/Ht52iKpXq2yjBx8q5x12vFSoXxZOayY/IUUwaJSCtzW9Uo5+fijJalMohxR63BTWrUqlcw4/2DL3zvkxbcWyMDbrpHrO7Q0fVenZvX82eTII2qI/uAymLRfC2RiQVQEQj0HNdgK9pOO5y7+0PxOQD+ERqWCFFpAIJRzsFLFCnnOrZ279si3P/wqXTu2zlMu52AejpjOhHoOrt/0t9Q8vJokJZWRMomJ1nvhceYvrMa00uws6gIm4I36XthBzAXefvcj6f/AM/LDL/+TW29oU+T+P1y1RurWriGDHnxWrr3tQRk2eops3LKtyPysiI6A3jc98ZW3pdudj8qZpx0nl7c4Vwr79+0Pv5nF3/30u7n14eZBj4v2tVmYazBm0jTRr2n1SvDO3XtzrWEyVgLaL3bOwdz1+fybn2TlZ2ukV7e2uRczHQMBu+dg/qqMsb5xua7dxVK/XtbzeYPrOQeDEvEb2z0He153mcxZ9KH0G/K0LPv4a3PLWO/uV8Wv4uw5KgIEvFFhjX+hv/2xwfyYKT09Q3btLvqvwv36x3rR+5RbXHCW9Ox8mbnft+eAUebHefFvhX9qkJ6Raa4o6NUi7a/de/cV2vjgh5Eah1eVG6+9VM4+/Xi5d8Tz8s7ST01+/cMoXdq3EP2KVa/c672+PawgWt/MTQYGJQlEbL3dczC4Q/0qfPSzUyZifOgAAA0lSURBVKVbp9ZSt06N4GLGMRKwew7mro5+ONEPKbfecGXOYs7BHIq4T9g9B0/4Z31z1T4hkCB3DZ8ku/ekSMNT/hn3+lOByAoQ8EbW0zGl9b+lo7knVx+h9u8Hxxdbr+s7tBL9Cqh1s3/J40Nulz/WbpLf/txQ7DasjKyA3nIwZlhveefVkVKmTKKMf2l2kTs4tkFd6wpgO7nY+pDSq1s703dLVnxh8uvXrYPv7Cq3XN/G3PbwytP3mUD6p//+adYziJ1AKOeg1mrph1+ZD5w3db5cZ0kxFgjlHNSq6cWEMZOmivZX7ttPOAdVxxnJ7jk4cOh4adOqiTz10B3y/vQx0qjhSdK593BJS093RkPiVgtv7ZiA11v9WaA1/6h/hHnUVVEn7knHNcjzhzUyMrIeY3YwNa1AWSyIvkAgEJBjrD4LXsnNv8cjj6gpelU+Ne3QC3GaNZ2aVnh/1apxmCli34GDZswg9gIlnYNaIz0/9Svw27u1dcWPY7XOXk2BQPHnYLDdi5avMh8mb7S+Dg8uK2zMOViYSmyXFXcO7k3Zbz5onnjsUaZS+mNR/RCjjyjTJxiZhQw8IUDA64luPNSICVPmyOoffpX9VoCjz9R9aepCaXzmSeZGfM01cNgEGT1xqk6adHmLxuZHUJpX7/XUP6xRvVpl83QHk4FBVAX0j5looKMvrKlW4Kp9N3vhh9LojBPNfvWrtY63DJWF739m5s887TjRXyBPeuVt84xlza/rzm90mlm/4pPVsth6I9a+1BfssZNnmvwnWl/ZmQwRHlBcQYFQz0Et4e3FH8mWv3ea2xl0nhQ7gVDPQa2Z3iKkv+zvc2P7Aj/y5RxUofimUM5BvaVPLyTok3H0dVOfR79g6afmFgcNlOPbEvYeSQEC3khqOqAsDVy7WF/FnN36VvNM3cSEBHnorp45Nfv9z/WyfuPWnPkbOrSSxmedbPI2ubKPfPDZahk/or95WkNOJiaiJhAIBOTjL74XfTRcw5Y3ifbdJU3PkR7XXmr2mZGRKfo8yB279ph5ffLG08P7ysvTF8vpLXqa/HrP7jVtm5n1B1NT5f5RL4r2ZaPLbrcC5U9l3MP9RB+rZzIwiLpAqOegPg5JHzV36w1t6Keo907BHQQCoZ2DWoI+q3z3nn3S9epWOpsncQ7m4YjLTKjnoN7KULZsknndbNL2DnNL36jBt4k+sSGEBpDV4QIEvA7voFCr98g9N8vX7z4vi998XD6a+4y89sxg0U+vwXJmv/iwjBnWJzgrepLrcz/1mb3vTX1Clk4bY37wlJOBiagK6NWFGc8/KKsWTDTP4NXnJ2sf6g9fdMdVq1SU75dPkc5XtdBZk8475xTzrMh33xotny+cKHrPbvCFWZ8R+sn88fL+9CdN+mD203Lu2Seb7RjERuCREM9B7euVc8aJ3ncdmxqyl9wC4ZyD+lQGPff06+/cZek056AqxDeFeg7qrX1PD+9nXof1fVD/2NRpJx0T30aw94gLEPBGnDT+BWoQq0FutaqFP8u1sBrqlcMjah8ugUCgsNUsi4RAMWXom279erVFn59cTLacVRrg1qtTw8pfPmdZcELX6Y9oNAUC9GfQJZbjcM7BWNaPfRUUCPUcLFjCoSWcg4cs4jUVzjmox4BeZIhXndlvdAUIeKPrS+kIIIAAAgggkE+AWQRiLUDAG2tx9ocAAggggAACCCAQUwEC3physzP7AuREAAEEEEAAAQQiI0DAGxlHSkEAAQQQQCA6ApSKAAKlFiDgLTUhBSCAAAIIIIAAAgg4WYCA18m9Y79u5EQAAQQQQAABBBAoQoCAtwgYFiOAAAIIuFGAOiOAAAIFBQh4C5qwBAEEEEAAAQQQQMBDAr4MeD3UfzQFAQQQQAABBBBAoAQBAt4SgFiNAAIIeFiApiGAAAK+ECDg9UU300gEEEAAAQQQQMC/AiUHvP61oeUIIIAAAggggAACHhAg4PVAJ9IEBBCIjQB7QQABBBBwpwABrzv7jVojgAACCCCAAALxEnDdfgl4XddlVBgBBBBAAAEEEEAgFAEC3lC0yIsAAvYFyIkAAggggIBDBAh4HdIRVAMBBBBAAAEEvClAq+IvQMAb/z6gBggg4AKB1T/8Kjfc8YjMX/KJGZ/SrIf06D9S/ly3SRYs/Uyuve1BaXTZ7TLi6ddlw6a/c1qUnp4hL09fLG273ye6Tfue98vi5Z/nrP/ki++l4y1DzbbB9W+/+1HO+n37D5r9vTX3fRn00LMmX9e+I+TdFV/k5GECAQQQQKB4AQLe4n1Yi0CMBNiN0wV27d4rX3/3H7n7kUly1mnHyX39rpdf/7dOLrv+bhk6+iU5/1+nSp8br5LZC1eaADfYnrGTZ8gzL86WZk0aytjhfeXYo+vJwGHjRQNozbNrz1457aRj5P7+XWXMsD5y/LFHyb0jnpev1vxHV0taWprZ7/AnX5GEQEAG3tZJKiaXkwFDn5Fde1JMHgYIIIAAAsULEPAW78NaBBBAII/ArBeGW0HnNXJ9h1Zy43WXmXXzXhkh/W66Wnpcc6l0ad9Cln30tVn+9/Zd8sK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Bootstrap Distribution\"},\"xaxis\":{\"title\":{\"text\":\"mean\"}},\"yaxis\":{\"title\":{\"text\":\"count\"}},\"shapes\":[{\"fillcolor\":\"#1f77b4\",\"line\":{\"width\":0},\"opacity\":0.3,\"type\":\"rect\",\"x0\":3.606975,\"x1\":3.7499999999999996,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"},{\"line\":{\"color\":\"#1f77b4\",\"width\":2},\"type\":\"line\",\"x0\":3.606975,\"x1\":3.606975,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"},{\"line\":{\"color\":\"#1f77b4\",\"width\":2},\"type\":\"line\",\"x0\":3.7499999999999996,\"x1\":3.7499999999999996,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"}]},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -233,7 +233,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5528a704",
+   "id": "fe464be1",
    "metadata": {},
    "source": [
     "## A confidence interval for a proportion"
@@ -242,7 +242,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "11dfc037",
+   "id": "f9cba51d",
    "metadata": {},
    "outputs": [
     {
@@ -279,7 +279,7 @@
     "mythbusters = md.load_mythbusters_yawn()\n",
     "\n",
     "boot_prop = (\n",
-    "    specify(mythbusters, response=\"yawn\", success=\"yes\")\n",
+    "    mythbusters.specify(response=\"yawn\", success=\"yes\")\n",
     "    .generate(reps=1000, type=\"bootstrap\", seed=1)\n",
     "    .calculate(stat=\"prop\")\n",
     ")\n",
@@ -288,7 +288,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d99896f",
+   "id": "43ec658b",
    "metadata": {},
    "source": [
     "## A confidence interval for a difference\n",
@@ -299,7 +299,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "f8c8e1f4",
+   "id": "81751a46",
    "metadata": {},
    "outputs": [
     {
@@ -332,7 +332,7 @@
    ],
    "source": [
     "boot_diff = (\n",
-    "    specify(mythbusters, formula=\"yawn ~ group\", success=\"yes\")\n",
+    "    mythbusters.specify(formula=\"yawn ~ group\", success=\"yes\")\n",
     "    .generate(reps=1000, type=\"bootstrap\", seed=1)\n",
     "    .calculate(stat=\"diff in props\", order=(\"seed\", \"control\"))\n",
     ")\n",
@@ -341,7 +341,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c4b0debc",
+   "id": "a259911f",
    "metadata": {},
    "source": [
     "```{seealso}\n",
diff --git a/doc/_build/jupyter_execute/guides/hypothesis-testing.ipynb b/doc/_build/jupyter_execute/guides/hypothesis-testing.ipynb
index fa387a2..681b84f 100644
--- a/doc/_build/jupyter_execute/guides/hypothesis-testing.ipynb
+++ b/doc/_build/jupyter_execute/guides/hypothesis-testing.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "fd24ec8c",
+   "id": "46c26eba",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c5db58fb",
+   "id": "071d4511",
    "metadata": {},
    "source": [
     "# Hypothesis testing\n",
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "0ab670c6",
+   "id": "45ea42ae",
    "metadata": {},
    "outputs": [
     {
@@ -82,7 +82,7 @@
     "\n",
     "# Null: genre is independent of popularity → permute the labels\n",
     "null = (\n",
-    "    specify(spotify, formula=\"popular_or_not ~ track_genre\", success=\"popular\")\n",
+    "    spotify.specify(formula=\"popular_or_not ~ track_genre\", success=\"popular\")\n",
     "    .hypothesize(null=\"independence\")\n",
     "    .generate(reps=1000, type=\"permute\", seed=76)\n",
     "    .calculate(stat=\"diff in props\", order=(\"metal\", \"deep-house\"))\n",
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "757ab4ba",
+   "id": "a0ef462e",
    "metadata": {},
    "source": [
     "## Shade the p-value"
@@ -102,7 +102,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "10687007",
+   "id": "5b11db5a",
    "metadata": {},
    "outputs": [
     {
@@ -110,7 +110,7 @@
       "image/png": 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Null Distribution\"},\"xaxis\":{\"title\":{\"text\":\"diff in props\"}},\"yaxis\":{\"title\":{\"text\":\"count\"}},\"shapes\":[{\"line\":{\"color\":\"#d62728\",\"dash\":\"solid\",\"width\":2},\"type\":\"line\",\"x0\":0.03399999999999992,\"x1\":0.03399999999999992,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"},{\"fillcolor\":\"#d62728\",\"line\":{\"width\":0},\"opacity\":0.3,\"type\":\"rect\",\"x0\":0.03399999999999992,\"x1\":0.07509999999999994,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"}]},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -157,7 +157,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78dc9c6e",
+   "id": "05406ed1",
    "metadata": {},
    "source": [
     "`direction` is one of `\"right\"`/`\"greater\"`, `\"left\"`/`\"less\"`, or `\"two-sided\"`.\n",
@@ -171,7 +171,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "9bc5f431",
+   "id": "cfba73b4",
    "metadata": {},
    "outputs": [
     {
@@ -208,7 +208,7 @@
     "obs_t = observe(age, response=\"age\", stat=\"t\", null=\"point\", mu=23)\n",
     "\n",
     "null_t = (\n",
-    "    specify(age, response=\"age\")\n",
+    "    age.specify(response=\"age\")\n",
     "    .hypothesize(null=\"point\", mu=23)\n",
     "    .generate(reps=1000, type=\"bootstrap\", seed=1)\n",
     "    .calculate(stat=\"t\")\n",
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "07ba6c30",
+   "id": "d1e0d753",
    "metadata": {},
    "source": [
     "For a one-proportion test you can also *simulate* draws directly:"
@@ -227,7 +227,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "77b331b4",
+   "id": "81e1bc3e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -236,7 +236,7 @@
     "coins = pl.DataFrame({\"flip\": [\"heads\"] * 30 + [\"tails\"] * 70})\n",
     "\n",
     "null_p = (\n",
-    "    specify(coins, response=\"flip\", success=\"heads\")\n",
+    "    coins.specify(response=\"flip\", success=\"heads\")\n",
     "    .hypothesize(null=\"point\", p=0.5)\n",
     "    .generate(reps=1000, type=\"draw\", seed=1)   # \"simulate\" is an alias\n",
     "    .calculate(stat=\"prop\")\n",
@@ -245,7 +245,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f586c384",
+   "id": "527c35eb",
    "metadata": {},
    "source": [
     "## Available statistics\n",
@@ -266,7 +266,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "3151f227",
+   "id": "7e204d86",
    "metadata": {},
    "outputs": [
     {
@@ -315,7 +315,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56d38353",
+   "id": "d3c3c6ce",
    "metadata": {},
    "source": [
     "The function receives `(response, explanatory)` as numpy arrays (`explanatory` is\n",
diff --git a/doc/_build/jupyter_execute/guides/plotting.ipynb b/doc/_build/jupyter_execute/guides/plotting.ipynb
index 89bec08..b9e83dc 100644
--- a/doc/_build/jupyter_execute/guides/plotting.ipynb
+++ b/doc/_build/jupyter_execute/guides/plotting.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "6980f148",
+   "id": "29fc1175",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9f6cbe57",
+   "id": "9ca7d6fa",
    "metadata": {},
    "source": [
     "# Plotting: plotly & plotnine\n",
@@ -30,14 +30,19 @@
     "- `engine=\"plotnine\"` — grammar-of-graphics figures (`plotnine.ggplot`).\n",
     "\n",
     "The composition syntax is identical across engines, so you can switch a whole\n",
-    "analysis by changing one argument. (In this documentation, plotly figures are\n",
-    "shown as static images; in a notebook they're fully interactive.)"
+    "analysis by changing one argument.\n",
+    "\n",
+    "```{note}\n",
+    "The figures on this page are **static images** so they render in the docs. In a\n",
+    "notebook or script, the default `engine=\"plotly\"` produces **interactive**\n",
+    "figures (hover, zoom, pan).\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "da325bf6",
+   "id": "32be16b0",
    "metadata": {},
    "outputs": [
     {
@@ -76,7 +81,7 @@
     ")\n",
     "\n",
     "boot = (\n",
-    "    specify(md.load_almonds_sample_100(), response=\"weight\")\n",
+    "    md.load_almonds_sample_100().specify(response=\"weight\")\n",
     "    .generate(reps=1000, type=\"bootstrap\", seed=1)\n",
     "    .calculate(stat=\"mean\")\n",
     ")\n",
@@ -86,7 +91,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe97e607",
+   "id": "47815da8",
    "metadata": {},
    "source": [
     "## visualize() returns an InferPlot\n",
@@ -99,7 +104,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "f6a547db",
+   "id": "713fb87c",
    "metadata": {},
    "outputs": [
     {
@@ -107,7 +112,7 @@
       "image/png": 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       ],
       "text/plain": [
        "Figure({\n",
@@ -163,14 +168,14 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "b13f011a",
+   "id": "39b992b7",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "<plotnine.ggplot.ggplot object at 0x134933950>"
+       "<plotnine.ggplot.ggplot object at 0x138733950>"
       ]
      },
      "execution_count": 4,
@@ -190,7 +195,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1134270e",
+   "id": "65534b9c",
    "metadata": {},
    "source": [
     "### Keyword form\n",
@@ -201,7 +206,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "6d59b471",
+   "id": "2162766f",
    "metadata": {},
    "outputs": [
     {
@@ -209,7 +214,7 @@
       "image/png": 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       ],
       "text/plain": [
        "Figure({\n",
@@ -264,7 +269,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69b5f4de",
+   "id": "2c51e26b",
    "metadata": {},
    "source": [
     "## Shading p-values"
@@ -273,7 +278,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "9c5757d4",
+   "id": "82033a17",
    "metadata": {},
    "outputs": [
     {
@@ -281,7 +286,7 @@
       "image/png": 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9XNbOHyddO7Yu6fCW8tIy8v7oRmxMTLHysflfHCvvj1RERxXv5zOP3iFvvfyfQuma7u2LHaNoxknHNZEBffOu2K3J/2Kd1X5rMLnS/bHxVZdfbKpdvmaDjBjzmlx23b9E7zfVzP0Hk3QmI4beUmLq7r6iqwUOJSXrTI5rmhdMmZVKTqKiXO6rvFebWqbY4iqvaUqJk1827zD5555xspmXNLHiXdJ+/s5zuVymSper+HloNvhp4pfXSnReW/3UJKpBAAEEELCBQGB/+lSgg1klPExfq4mNjfEGjT/9tk2zApoqesVXb1/Qhs18aZh5hNqp7qtv+nSJmPzAVLcVTNml9LdgmSYN6ppVfdC/WSgw+XNP3hfbGtQ7ukCutUUNXFv843gpmDwf7ZZXQ4z7FxIto1/O07kv/W7UoI48+eAA2bDkZdErx/pLjQa5i1au16rklBPyrmR3cf+S0LtbOymazjvzFFPO09ai3/43Gysx6XDhWeaje73yvHlryX9woqLnRyWaAzr7sQAAEABJREFUVWhX/ZLguKlzTZ7e1mIWSpmU5+3Zzcq56Cnr6/zP3XnnqedKr2d/XxyttC9QrxVPe5kjgEAIBTg0ApUQsF3A2+HqIeZLavrt7KL9Wpv/WK3TTzmu6Ca/rjesf7T5gtvuvQe89epH91Zupfgz/76/qPwrWlqB3l/7+7a8j991XZMG8Dq38tSEk9wfsWvZ2YtWi95Hq8ua9CPt5Ws+M7cX1D26pmYFJelTJGYtXGWO1Tx/LKz2e92n34jurzu7XC5zJVzvN9b1X/KfNqFPt9D1STPe0VmhpLcZ6BeoNFMDdp0vKvJUhsp+2cjlcon+wROtW311XjBV5vwoWE9Flzdv/VPuefRFc5tM354dpaxbOqx4+3IuVrTNni9sFgzOrTr60j67vVYq6sV+CCCAAAL+FbBdwKtX+vRLat1ufNA8x1OfJavPX+1ywwPmubrNmtQXKx+7V4bJ8wxgfbSSPi5L78Ht0HuI6NMcyqtX/xqWltHne+q++qeQu7rbro8/0/yCSR+jpV/M02eRakDw/KTZBTd7l/UK8e39upsvrt085Gl5Z/lHoo980r92poX0D2+4XC5d9HvS8dB7dTVpO/X5wRf1uMtY6G0a+iVDPajVfmvbO1031Dx9Qx8f9vY7H8jjL8zQKsyX8nSh/3VdzFMt1E8dZy9aI2/Mf1/0vNB9N+Z/wVC/XKT3GOuV2KGPvyxT3lhs/gKZfqFO66lMuvj8FnJGgS/9FayrMudHwXqsLr80bb4xuu+xCeZ5w/p4sE+//EE6tT1PHrirb5nVWPHWCqyei1rWSnpy3OvmPmh9vOB9+fdp/+OkZnJp/j3cWocvjlbbF8rXivaJhAACCCBgTwHbBbxPPXyr6LNZNRDUoE6fJatfINJ1vZo1Y/zDUjUxoZBmVIH7AqOi8rrkcrkKlXG58tYLljUF8vPNcv6k39WXyUUtTzdBnf5BhDmL17iDsUtNnhbJq0mX8lLBOh90ByB6BVqfjar76iOuLji3uXnUkZZ2uf7e+yF32c7tW8r8JR/KSHfQp0FvwTIu199lB918ldx5S0/vc4P1of5JyanmkV2d8/9KmO5bWv91mybPdl22mvRpDJq0nRq461U6vT95lHusPHVY7bc+LUODVH36xgNPTjaB3K+/7zB/ya31uaeZ6vSLhPOmjpRunS4wjycb8fx0GTX+dXPlXwPsFqfmXeHXR8xNff5+c4V76Qefij6iTJ8yMeTW3qYel+tvP5NR0iS/jMtVuKzL5ZK785/Lq7u5V3VmUmXOD5cr7zguV97cVFjKJCoqr8x7qz4R/cVg3affSmpauuh4Txp9nzw3fKDE5D+FwVOFp1rPOFvx1n1LOxc99bhceW3RskVTwfPf5cort//AYfOHQh4Z/ap55rK+pic/8y/x9Enr8MWxtPblH85db97rXuu1+lpxufLa6hKX7lYsefpebAMZCISFAI1EAIGCAn//lCiYG8LlKy+7SCaN/pd8vepV8wxZfT6tPtP22w/+K8Pu6Sd6BcfTvCoJcbJpzXQZ+/hdniwTlGretVd28Obpgj7CS/P1CRC67kn6fFDN1/tIPXkacOkP5/fffl7mTX1cvlj+ijsgu0FeeXaoOV71aommaEnHr1enlsx6+T/m2b/a9vXvThD9cpg+H1aPU7NG3tMStIITjm1snkP78eKJ5tmx6999SbNFAz8tqwG+yXBPNLAZdFMP+WrlVHl3+pOm/k/c++kfZXBv9v7XQF33Ldp/bZfmD7/vJm/Z8hY+WzrJ9Ff386R1C1+U1/PvT9Y2eerQ+q30W9urz8xdv2iC97mnunx9r06eqsxc6xs97HZzHujzkvUJEToO6ljwyqve76vP8dXt+oxiNbn1+m6m3fqED1NZGZNru7c3ZdW8aDG9qujpt46VZ3tlzg89jtZZcGw99RadP/efgaZtWl6Tjofe8zzmsUHuXwrPKBbs6v76S5GWVRddt+qt/Rvz2J1S9Fws7XzSuks6//WXDT2+tnP13LHmSSt6zuhrWh9npvt5klVHLV9a+4r2V8vqeWnlteJp64nHNdbdvKmz+xdI7UOXjq28eSwggAACCIS3gO0CXg+n/tCqX/co8+UhnRe8MuQpE8i5y+USvcdQv3Tm63Ndta1664V+hFswwC2tvTXcAbQ+R9Zzr2Jp5TRfy+gPf61fn2SheRVN/t7Pl37XrF5V9AkCjRvUEc+TJkpqj54HxzSqZ/5aXmnj4DnucU0bSrBMXK6Knx8l9TPQeVa9fTkXy2uz/tLS/JRjze0ppZV1uXxz9KV9dn6tlOZBPgIIIIBAYARsG/AGprvUigACCCCAQDEBMhBAIMIFCHgjfIDpHgIIIIAAAggg4HQBAl6rZwDlEEAAAQQQQAABBMJSgIA3LIeNRiOAAAKhE+DICCCAQLgJEPCG24jRXgQQQAABBBBAAAGfBAIU8PrUBgojgAACCCCAAAIIIBAwAQLegNFSMQIIICAiICCAAAIIhFyAgDfkQ0ADEEAAAQQQQACByBcIZQ8JeEOpz7ERQAABBBBAAAEEAi5AwBtwYg6AAALWBSiJAAIIIICA/wUIeP1vSo0IIIAAAggggEDlBNjbrwIEvH7lpDIEEEAAAQQQQAABuwkQ8NptRGgPAtYFKIkAAggggAACFgQIeC0gUQQBBBBAAAEE7CxA2xAoW4CAt2wftiKAAAIIIIAAAgiEuQABb5gPIM23LkBJBBBAAAEEEHCmAAGvM8edXiOAAAIIOFeAniPgOAECXscNOR1GAAEEEEAAAQScJUDA66zxtt5bSiKAAAIIIIAAAhEiQMAbIQNJNxBAAAEEAiNArQggEP4CBLzhP4b0AAEEEEAAAQQQQKAMAQLeMnCsb6IkAggggAACCCCAgF0FCHjtOjK0CwEEEAhHAdqMAAII2FCAgNeGg0KTEEAAAQQQQAABBPwnEIqA13+tpyYEEEAAAQQQQAABBMoRIOAtB4jNCCCAQOAEqBkBBBBAIBgCBLzBUOYYCCCAAAIIIIAAAqULBHgLAW+AgakeAQQQQAABBBBAILQCBLyh9efoCCBgXYCSCCCAAAIIVEiAgLdCbOyEAAIIIIAAAgiESoDj+ipAwOurGOURQAABBBBAAAEEwkqAgDeshovGImBdgJIIIIAAAgggkCdAwJvnwBQBBBBAAAEEIlOAXiEgBLycBAgggAACCCCAAAIRLUDAG9HDS+csC1AQAQQQQAABBCJWgIA3YoeWjiGAAAIIIOC7AHsgEIkCBLyROKr0CQEEEEAAAQQQQMArQMDrpWDBugAlEUAAAQQQQACB8BEg4A2fsaKlCCCAAAJ2E6A9CCAQFgIEvGExTDQSAQQQQAABBBBAoKICBLwVlbO+HyURQAABBBBAAAEEQihAwBtCfA6NAAIIOEuA3iKAAAKhESDgDY07R0UAAQQQQAABBBAIkoDtAt4g9ZvDIIAAAggggAACCDhEgIDXIQNNNxFAIOwEaDACCCCAgJ8ECHj9BEk1CCCAAAIIIIAAAoEQqHydBLyVN6QGBBBAAAEEEEAAARsLEPDaeHBoGgIIWBegJAIIIIAAAqUJEPCWJkM+AggggAACfhLI2rtXfjjlVPnp/PMlefVqEgY+nwM+nIoULUGAgLcEFLIQQAABBBBAAAEEIkeAgDdyxpKeIGBdgJIIIIAAAgg4SICA10GDTVcRQAABBBBAoLAAa84QIOB1xjjTSwQQQAABBBBAwLECBLyOHXo6bl2AkggggAACCCAQzgIEvOE8erQdAQQQQACBYApwLATCVICAN0wHjmYjgAACCCCAAAIIWBMg4LXmRCnrApREAAEEEEAAAQRsJUDAa6vhoDEIIIAAApEjQE8QQMAuAgS8dhkJ2oEAAggggAACCCAQEAEC3oCwWq+UkggggAACCCCAAAKBFSDgDawvtSOAAAIIWBOgFAIIIBAwAQLegNFSMQIIIIAAAggggIAdBMIr4K2kWE5OrmRn55RZy18HDoumooWSklPkwKGkotmsI4AAAggggAACCNhcwDEBb25urowYM10ef+G1YkOigfCUNxZLm6sGS9ued8vlfe/3lklJTZPBw8ZJ626D5OIeg6XPoJGyb/8h73YWEEAAgVAIcEwEEEAAAesCjgh4l6/ZYALZuYvXlijzwitzZMac5XLHjT1k3cIXZdGMUd5yby5YJT9v3i6r546VTxZPlOioKBk3dZ53OwsIIBA5Atu2bZM1a9aENG3atEnS09MjB5WeIIAAAoEVsFS7IwLeNq3OlDlTRki3ThcUQ9n710GZ9tYSue/2a+X6XpdK7VrVpUHd2t5yy1ZvkN7d2km9OrWkerVE6de7k8xf8qHoFWNvIRYQQCAiBHbu3BnSYHeNO9j+7rvvIsKSTiCAAAJ2EnBEwJtYJd4EsVUTqxSz/+b7zSbvux9/l36DR8mAoc/Kuys+Mnk62bJ9tzRtXF8XTTqmUT0zP5ycYuZMEEAgDARoIgIIIICAowUcEfCWNcK79u43m+scXVNu+eflcu4ZJ8tDo6bIe6s+MVdx9R7ehPg4U0Yn8XGxOpOUlDQzP3QkU0gYcA6E/zmQlpFtXtN2mGRm5/C+EmHvrYdTMvNOrVyRI2lZJAx8PgfyTqDKT51ag+MDXh34E5o1koE39pBLLj7HzLtfdqGsXPu5uFwuSaySIOkZ+W9U7sKe5cTEBPeaSGJ8NAkDzoEIOAdiY+zzdhit7z0RYMr7498/H6rERZufGeISiY+NImHg8zmQdwIxraiAfd7hK9qDSu7XpGFd+W3LTsnM+vvqTpZ7OTMry9TcrEl92bpjt1nWybade3QmNaolmrn+kCRFCQaRZODMvkRHucQu/6LcbeE1FRVx7yuS/y8mOkpIGPh6Dgj/KiXgiIBXn72bmZkl2dnZYoJZ97I+ikzlzm5xkrmKO3nGu+7tOfL197/J0g8+lYtattDN0rl9S5mzaI3s2XdQko+kysy5K6VXl7bictnnh6NpKBMEEEAAAQQQ8K8AtUWMgCMC3nnvrZWzOg0QfSzZwmX/M8sLl60zg6hXasePHCyvzVkuZ3TsL30HjZS+PTvKtVe2N9v79rxUjm/WSDr0HiKtug4UDZwH9+9ltjFBAAEEEEAAAQQQsL+AIwLea6/sIJvWTC+U9CqtZ3guOK+5fLx4gqx46zn5bOkkGXZPP/fHTdFmc9XEBHn56Xtl/aIJsnb+OHl78nDziDKzkQkCIhgggAACCCCAgM0FHBHwWhmDmOhoadygjrm9oaTyNatXlTq1a5a0iTwEEEAAAQQQEAgQsK8AAa99x4aWIYAAAggggAACCPhBgIDXD4hUYV2AkgggUL5ATk6OHDhwQHbt2hXSdOjQofIbSwkEEEAgDAQIeMNgkGgiAgg4T2Dt2rUyadKkkKaNGzc6Dz54PeZICCAQRAEC3iBicygEEEAAAQQQQACB4AsQ8Abf3PoRKYkAAggggAACCCBQaQEC3tLwtrcAABAASURBVEoTUgECCCCAQKAFqB8BBBCojAABb2X02BcBBBBAAAEEEEDA9gIRFPDa3poGIoAAAmEnkJ6eLqtXr5aFCxeGNO3Zsyfs7GgwAgjYR4CA1z5jQUsQcKRAamqqpKWlhTxlZWVFjr+fe/LTTz/JV199FdKk54mfu0V1CCDgIAECXgcNNl1FwI4CR44ckdmzZ4c0LVmyRFwulx15aBMCCCCAQCUEPLsS8HokmCOAQEgE9Mrq5s2bJZRp27ZtIek7B0UAAQQQCI4AAW9wnDkKAgjYVoCGIYAAAghEugABb6SPMP1DAAEEEEAAAQSsCERwGQLeCB5cuoYAAggggAACCCAgQsDLWYAAAr4IUBYBBBBAAIGwEyDgDbsho8EIIIAAAgggEHoBWhBOAgS84TRatBUBBBBAAAEEEEDAZwECXp/J2AEB6wKURAABBBBAAIHQCxDwhn4MaAECCCCAAAKRLkD/EAipAAFvSPk5OAIIIIAAAggggECgBQh4Ay1M/dYFKIkAAggggAACCARAgIA3AKhUiQACCCCAQGUE2BcBBPwrQMDrX09qQwABBBBAAAEEELCZAAGvzQbEenMoiQACCCCAAAIIIGBFgIDXihJlEEAAAQTsK0DLEEAAgXIEHBXw5uTkSnZ2TjkkJW9OSk6RA4eSSt5ILgIIIIAAAggggIBtBZwS8Epubq6MGDNdHn/htVIHY8eufdLyijtkzOTZ3jIpqWkyeNg4ad1tkFzcY7D0GTRS9u0/5N3OAgIIIIAAAggggIC9BRwR8C5fs0Ha9rxb5i5eW+po6BXcgQ+MEQ1wCxZ6c8Eq+Xnzdlk9d6x8sniiREdFybip8woWYRkBBBAIIwGaigACCDhPwBEBb5tWZ8qcKSOkW6cLShzhrOxs+ffIl+WcFidL5/bnFyqzbPUG6d2tndSrU0uqV0uUfr07yfwlH5orxoUKsoIAAggggAACCCBgS4ESA15btrQSjUqsEi8N6taWqolVSqzlmQlvSUZGlgy754Zi27ds3y1NG9f35h/TqJ5ZPpycYuZMEEAAAQQQQAABBOwt4IiAt6whmLVwlaz9+Ct5YcRdEhsbU6io3vertzgkxMd58+PjYs1ySkqamWdm5QgJA86Bip0DWRX8Eql58fl5kptbYoWOz9T3Qbsg6PkSzq81j6P2g5QjGPhm4Dl/mFdMwPEB7/S3l0mzJvVl8sx35ZkJs2TTT7/L+s83yZQ3FovL5ZLEKgmSnpHp1fUsJyYmmLwjaVlCwoBzoGLnQFpGttgnzrRRS+zTFPM+Z4eJ/kKSnpETtu+3KelZeYzusdV+kHLcP1tJvpwHeSdQKKfhfWzHB7z9r7tCzjvzFKlVs5pJ0dFRold0a1RLNCOrwfDWHbvNsk627dyjM/Fsr1UtTkgYcA5U7ByoViVWXOYVFfqJy2WXlojYqCnuttjDRU2qVokJ2/fbmlXjxPxzc2o/SDGCgW8G5vxhUmEBRwS82e6PTTMzsyQ7O1uysrJFl/WZvKr2zx6XyG03dPemU09sJue0OEk0X7d3bt9S5ixaI3v2HZTkI6kyc+5K6dWlrW1+CGgbSQjYRYB2IIAAAgggYEcBRwS8895bK2d1GmAeS7Zw2f/M8sJl6yyNR9+el8rxzRpJh95DpFXXgSZYHty/l6V9KYQAAggggAACjhSg0zYTcETAe+2VHWTTmumFkl6lLWksxjw2SO67/VrvpqqJCfLy0/fK+kUTZO38cfL25OHmEWXeAiwggAACCCCAAAII2FrAEQGvP0agZvWqUqd2TX9URR0I5AkwRQABBBBAAIGgCBDwBoWZgyCAAAIIIIBAaQLkIxBoAQLeQAtTPwIIIIAAAggggEBIBQh4Q8rPwa0LUBIBBBBAAAEEEKiYAAFvxdzYCwEEEEAAgdAIcFQEEPBZgIDXZzJ2QAABBBBAAAEEEAgnAQLecBot622lJAIIIIAAAggggEC+AAFvPgQzBBBAAIFIFKBPCCCAgAgBL2cBAggggAACCCCAQEQLEPCKSESPMJ1DAAEEEEAAAQQcLkDA6/ATgO4jgAACBQRYRAABBCJSgIA3IoeVTiGAAAIIIIAAAgh4BHwPeD17MkcAAQQQQAABBBBAIAwECHjDYJBoIgII2FOAViGAAAIIhIcAAW94jBOtRAABBBBAAAEE7Cpg+3YR8Np+iGggAggggAACCCCAQGUECHgro8e+CCBgXYCSCCCAAAIIhEiAgDdE8BwWAQQQQAABBJwpQK+DL0DAG3xzjogAAggggAACCCAQRAEC3iBicygErAtQEgEEEEAAAQT8JUDA6y9J6kEAAQQQQAAB/wtQIwJ+ECDg9QMiVSCAAAIIIIAAAgjYV4CA175jQ8usC1ASAQQQQAABBBAoVYCAt1QaNiCAAAIIIBBuArQXAQRKEiDgLUmFPAQQQAABBBBAAIGIESDgjZihtN4RSiKAAAIIIIAAAk4SIOB10mjTVwQQQACBggIsI4CAQwQcFfDm5ORKdnZOiUN7KOmI7Nl3sMRtmpmUnCIHDiXpIgkBBBBAAAEEEEAgjAQcE/Dm5ubKiDHT5fEXXis0PPv2H5IuNzwgF3a/Uzr0HiJX3vSwLFqx3lsmJTVNBg8bJ627DZKLewyWPoNGiu7jLcACAggggAACCCCAgK0FHBHwLl+zQdr2vFvmLl5bbDD0qu9Vl18sq+aMkU8WT5TLO5zvDopnSGpahin75oJV8vPm7bJ67lizPToqSsZNnWe2MUEAAQScJEBfEUAAgXAVcETA26bVmTJnygjp1umCYuNUr04tue2G7tKgbm2pXi1Rrux8kaS4r+r+8Msfpuyy1Rukd7d2ouV0e7/enWT+kg9FrxibAkwQQAABBBBAAAEEbC3g54DXnn1NrBJvAtqqiVXKbeBnX/1oyhx7TEMz37J9tzRtXN8s6+SYRvV0JoeTU8ycCQIIIIAAAggggIC9BRwR8Fodgl9+3y6jxr8hA2/sIbVrVTdXcfVqb0J8nLeK+LhYs5ySkmbmh45kCgmD8DgHMtznqr1Scmqm5JpXUugnfv/UphJdsouJdsEuLrlulCNpWbY7hw8dsfaaOpySd5ucnvDaD1KWYOCbgb4eSRUXIODNt9uxa5/cfv/zcsnFZ8vAm3qYXJfLJYlVEiQ9I9Os68SznJiYoKtSJS6KhEGYnAMx7nbaK8XHRotL7PKPlthlJEpqh/vtWOJj9P02xnbncZW48tuUEBsj5p/7NIt394MUZcYTB+sO5vwJ04kdmk3A6x6FX3/fIdfdMULatDpDnnxwgERH/83SrEl92bpjt7tU3v9tO/eYhRrVEs08zv0DmxQtGISDQZR7nOyVYt0/+MUm/zSgsklTbNUMl8tlm/bEuM+XuNgo253HVtvkgdR+kKIEA98MhH+VEvg7sqtUNfbeWZ+9m5mZJdnZ2ZKVlS26rE9n0Fb/9Ns26XHLMLng3OYyoG9X2b33gOjVXs8zdzu3bylzFq0xz+hNPpIqM+eulF5d2orL5dLdSQgg4BcBKkEAAQQQQCBwAo4IeOe9t1bO6jRA9LFkC5f9zywvXLbOqG7estPM31v1iVze93657LqhJo2eMMvk9+15qRzfrJF5Rm+rrgNNsDy4fy+zjQkCCCCAAAIIIOBXASoLiIAjAt5rr+wgm9ZML5T0Kq2KXnFJq0L5nnJPP3ybbpaqiQny8tP3yvpFE2Tt/HHy9uTh5hFlZiMTBBBAAAEEEEAAAdsLOCLg9cco1KxeVerUrumPqqgDgcoKsD8CCCCAAAII+CBAwOsDFkURQAABBBBAwE4CtAUBawIEvNacKIUAAggggAACCCAQpgIEvGE6cDTbugAlEUAAAQQQQMDZAgS8zh5/eo8AAggg4BwBeoqAYwUIeB079HQcAQQQQAABBBBwhgABrzPG2XovKYkAAggggAACCESYAAFvhA0o3UEAAQQQ8I8AtSCAQOQIEPBGzljSEwQQQAABBBBAAIESBAh4S0CxnkVJBBBAAAEEEEAAAbsLEPDafYRoHwIIIBAOArQRAQQQsLEAAa+NB4emIYAAAggggAACCFReIJgBb+VbSw0IIIAAAggggAACCPgoQMDrIxjFEUAAgcoLUAMCCCCAQDAFCHiDqc2xEEAAAQQQQAABBP4WCNISAW+QoDkMAggggAACCCCAQGgECHhD485REUDAugAlEUAAAQQQqJQAAW+l+NgZAQQQQAABBBAIlgDHqagAAW9F5dgPAQQQQAABBBBAICwECHjDYphoJALWBSiJAAIIIIAAAoUFCHgLe7CGAAIIIIAAApEhQC8Q8AoQ8HopWEAAAQQQQAABBBCIRAEC3kgcVfpkXYCSCCCAAAIIIBDxAgS8ET/EdBABBBCIHIEVK1ZIqFN6enrkgBboCYsIRLIAAW8kjy59QwABBCJMYP369RLqlJ2dHWGqdAeByBcg4I38MfZjD6kKAQQQQAABBBAIPwEC3vAbM1qMAAIIIBBqAY6PAAJhJeCogDcnJ1eys3NKHCDdtmvvfskq5aOqpOQUOXAoqcR9yUQAAQQQQAABBBCwr4BjAt7c3FwZMWa6PP7Ca8VGY+3HX0urrgOl4zX3yZkd/09mL1rjLZOSmiaDh42T1t0GycU9BkufQSNl3/5D3u1lLLAJAQQQQAABBBBAwAYCjgh4l6/ZIG173i1zF68tRp6aliFDH39Z7urfU75e9aqMGzlYRjw/Xbb/udeUfXPBKvl583ZZPXesfLJ4okRHRcm4qfPMNiYIIIAAAlYEKIMAAgiEVsARAW+bVmfKnCkjpFunC4ppb/jyB9GruH16XCIx0dFyaZtzpVmT+rL2469M2WWrN0jvbu2kXp1aUr1aovTr3UnmL/lQ9IqxKcAEAQQQQAABBBBAwNYCtgl4A6mUWCVeGtStLVUTqxQ7zO59B0yAGxcX6912QrNGsmvPAbO+Zftuadq4vlnWyTGN6ulMDienmHm23hdMEhxyMajg68C8kGwwybVBG+zYhFxgShyWirzneSrS74yQcgUD3ww85w/zigk4IuAti+Zw0hFJrJJQqEh8fJzol9T0Kq5e/U1wr3sKxOcHxikpaSbr8JFMIWHAOVCxcyAlLUtsE0/ZJ7Jzf4Jk3l5sMrHHCNloeMz4JKf4ds4nucubAXVzpqRnCwkDX88Bc/4wqbCA4wPeGtWrmlsaCgqmp2eY2xdcLpcJhtMzMr2bPcuJiXlB8lHV44SEAedAxc6B6omx4vK+ukK74HLZpSUiNmqKuy32cLGXiUjNar6d87Xc5UX/uTmrVYkREga+ngN6+jgv+a/HUf6rKjxrql/nKNHbFjIzs7wd0C+pNah3lFnX+3m37thtlnWybecenUmNaolmzgQBBBBAAAEEEEDA3gKOCHizs3NEA9rs7GzJyso2y3rvkA5Ny7NO1ZnMWrhKstzb31/3hXlCQ7sLzjL5ndu3lDmL1siefQcl+UiqzJy7Unp1aWuOf+Q1AAAQAElEQVSbqx6mkUwQcLAAXUcAAQQQQKA8AUcEvPPeWytndRpgHku2cNn/zPLCZeuMjX6h7cUn75HRE2aZZ/De8+iL8siQftKkYV2zvW/PS+X4Zo2kQ+8h5lm9GjgP7t/LbGOCAAIIIIAAAgjYRIBmlCHgiID32is7yKY10wslvUrrcbnkorPlm1XTZMVbz8lXK6dKn6s6ejZJ1cQEefnpe2X9ogmydv44eXvycPOIMm8BFhBAAAEEEEAAAQRsLeCIgNfKCERHR0njBnUkNjamxOI1q1eVOrVrlriNTATCRoCGIoAAAggg4EABAl4HDjpdRgABBBBAwOkC9N9ZAiEJeDd8+aP89Nu2YtJ7/zooi1asN18eK7aRDAQQQAABBBBAAAEEKiAQkoB35tzl8sFHG4s1V78Q9uCoV2TbjrxHfxUrQAYCQRXgYAgggAACCCAQCQIhCXhLgzt4ONls0vtpzQITBBBAAAEEEAi9AC1AIMwFghrw6tXbOx54Xj7Z+IMsXPo/0WVP6n/vaLnmtsfkHyc1k6aN64c5K81HAAEEEEAAAQQQsItAUAPe2JgYiYuLlYT4WNGruLrsSfoEhMeG3iwTn7rXLja0wzcBSiOAAAIIIIAAArYUCGrAO/L+/jJ+5N1y3+3XyqNDbjTLuq7pmUfvkGu6tecZt7Y8TWgUAggggIB1AUoigIDdBIIa8Ho63/OKNnLBec0lJydXjqSkFUuecswRQAABBBBAAAEEEKisQEgC3j37DsrIF2ZIu153y/ld7iiWDiUdqWy/bL8/DUQAAQQQQAABBBAIjkBIAt6pby6Wt975QPr0vFRGPXSr6O0MBVNiQnxwes9REEAAAQRCLcDxEUAAgYALhCTgXfrBp3J7v+4y6KYe0qPzRdK1Y+tCKTY2JuAd5wAIIIAAAggggAACzhAIScDb/JRjJSk5xbowJRFAAAEEEEAAAQQQqKBASALefr07y8JlH8m+/Ycq2Gx2QwABBJwpQK8RQAABBHwXCEnAu2DpOklJTZN2ve6R5u1vLpb40prvA8keCCCAAAIIIICAgwR86mpIAt4rOrSSfw+8rtSUEB/nUycojAACCCCAAAIIIIBAaQIhCXg7tjlHbv7n5aWm+LjY0tpLPgIIIGBdgJIIIIAAAgi4BaLcKej/c3NzpawU9AZxQAQQQAABBBBAIIIFnN61kAS89/znRTm9wy2lJu7hdfppSf8RQAABBBBAAAH/CYQk4L2mW3sZft9NxVLtWtWlTasWUoV7eP03wtSEgGUBCiKAAAIIIBCZAiEJeNu0OkOuvbJDsXTf7dfKF9/8EpnS9AoBBBBAAAEEwkOAVkacQEgC3tIUzz3jZPO4sl//2FFaEfIRQAABBBBAAAEEEPBJwDYBb05Orvxvw3em8dWqJpo5EwRsLEDTEEAAAQQQQCBMBEIS8D76zDRpc9XgQqnFJbfIk+NmSuf2LaVp43phwkczEUAAAQQQcLoA/UfA/gIhCXhbn3uaXNfjkkJJ/xDFgmlPyJjH7rS/Gi1EAAEEEEAAAQQQCBuBkAS8XTu2ljtv6Vko6R+iOPn4JmEDR0N9E6A0AggggAACCCAQKoGQBLza2fSMTFmwdJ08/dKbMvy5/8qUNxbLtp17dFPI0p979oveS1xSA5KSU+TAoaSSNpGHAAIIIICAVQHKIYBACARCEvDu239IrrzpYXlk9Ksyc+4KWbLqUxk7Za5c3vd+Wb7ms6AzzJizXDr3+bdcf+dIadfrbhkzeba3DSmpaTJ42Dhp3W2QXNxjsPQZNFK0/d4CLCCAAAIIIIAAAgjYWiAkAe+4qfNk/8EkeW3cQ7JxxRT5bOkkWTTjKWl/4Vly32MTJDUtI2hom376Q0ZPmCUj7/8/+WDOC/La+Ifl1VlL5OvvfzNteHPBKvl583ZZPXesfLJ4okRHRYm232wM1IR6EUAAAQQQQAABBPwmEJKA96PPvpU+V10i5515isTHxZrOHN+0oQzu38ss/7YleM/hTU5JNcdsUO8oM9d26F9827p9t1lftnqD9O7WTurVqSXVqyVKv96dZP6SDyU3N9dsZ4IAAgggEDgBakYAAQT8IRCSgLdm9apyOCmlWPtLyitWyM8ZZzc/Uc5pcbJcPWC4ub1i0ox3pU7tmtLOfbVZD7XFHfg2bVxfF006plE9Mz+cXLz9ZgMTBBBAAAEEEEAAAVsJhCTg1T8tPGfxGtGkAeWhpCPywUdfyvOT3pbatarLKSc09QGpckXj3FeY27Y+QxrWqy2LV34sL06bL21bnymJVeLNVVy9hzchPs57EM8V6ZSUNJOXlJolJAw4Byp2DqSkZ5vXkR0mdvrUxk6fH9nFxVYm7hM2Oc23cz7Z/bPCvZv5n5qRLSQMfD0HzMnDpMICIQl4B950lVzU8nR57Lnp0uWGB+TC7neaL4bt3L1Pxo28W2JjoivcIV93XPfpN6JfmHt9wiPy9uTh8sqzQ0Xv2523eK24XC534Jsg+kQJT72e5cTEBJMVF+0SEgacAxU7B2KizMuISREBV5F1y6sRXNBOJtqWuCiXT+/9se6fFZ7hiXHvS3IJBr4ZeM4f5hUTiKrYbpXbq0pCnAksX39pmAy/7ybRPzoxbuRgWT7rWTmnxUmVq9zHvb/45mc5oVkjqVEt0ex5kTsQP//sU0XzNaNZk/qydUfe/by67nl0mqd8fFy0kDDgHKjYORAXGy12+edyaRhjl9bYpx0uFy4ljUZcBd77PfXExkS5L+yQcPDtHBD++SRQtHBIAt4fftliHv119FE15dorO4j+0YlL25wrr89baZ7NW7SRgVw/+fhj5LctO0Wv9OpHd1t37JE167+S004+1hy2c/uWMmfRGtmz76AkH0mVmXNXSq8ubc3VX1OACQIIIIAAAggggICtBUIS8GpgqwFm4wZ1CuFUq5pons2bGsTHkl3e4Xy5q39PeWLsTDm/y0C5echTcuv13eT6qzuJ/uvb81I53n0FuEPvIdKq60DJzMzyPk1Ct5MQQCBcBWg3AggggIBTBEIS8H77w2bpeukFEh1d+PCd2p5r3AveQmAyAjiJinLJwBt7mNsp3vnvE/L+22NkyK293R835X3UWjUxQV5++l5Zv2iCrJ0/ztznq48oC2CTqBoBBBBAAAEEEAiegAOOVDjiDFKHqyTEy45d+4odTW8n0MzY2BidBT01cl9x1gC4pAPro9T0cWUlbSMPAQQQQAABBBBAwL4CIQl4zznjZJn97mrz18yys3OMjt4jO+WNReapCMc0rGvymCCAgG0EaAgCCCCAAAJhKxCSgPeOfldK/bpHSd9BI6V1t0HSs/8jovfIrvv0W/nPvTdKqK7whu0o0nAEEEAAAQQQCJIAhwlHgZAEvDVrVJXFM56SoXf8U/SPUDSod7TceE1nmT35Mel+2YXh6EibEUAAAQQQQAABBGwqEJKAVy0SqyTILdddIWMeG2S+FPbAnX2k+Sl5jwLT7SQEwlmAtiOAAAIIIICAfQRCFvDah4CWIIAAAggggECABKgWAVsIEPDaYhhoBALBFUhPT5dff/015CkzMzO4HedoCCCAAAKOFCDgdeSw26zTNCfoAlFRUfL666+HPGk7gt55DogAAggg4DgBAl7HDTkdRgABBBCwqwDtQgCBwAgQ8AbGlVoRQAABBBBAAAEEbCJAwGuTgbDeDEoigAACCCCAAAII+CJAwOuLFmURQAABBOwjQEsQQAABiwIEvBahKIYAAggggAACCCAQngKRHvCG56jQagQQQAABBBBAAAG/CRDw+o2SihBAAAE7C9A2BBBAwLkCBLzOHXt6jgACCCCAAAIIOEKgUMDriB7TSQQQQAABBBBAAAFHCRDwOmq46SwCCFgUoBgCCCCAQAQJEPBG0GDSFQQQQAABBBBAwL8CkVEbAW9kjCO9QAABBBBAAAEEEChFgIC3FBiyEUDAugAlEUAAAQQQsLMAAa+dR4e2IYAAAggggEA4CdBWmwoQ8Np0YGgWAggggAACCCCAgH8ECHj940gtCFgXoCQCCCCAAAIIBFWAgDeo3BwMAQQQQAABBDwCzBEIlgABb7CkOQ4CCCCAAAIIIIBASAQIeEPCzkGtC1ASAQQQQAABBBConAABbxG/vw4cFk1FsiUpOUUOHEoqms06AggggAACwRHgKAggUGEBAl43XU5Orkx5Y7G0uWqwtO15t1ze9353bt7/lNQ0GTxsnLTuNkgu7jFY+gwaKfv2H8rbyBQBBBBAwJEC6enpkpWV5VPyQGVnZ4u/UmZmpqda5gggUIYAAa8b54VX5siMOcvljht7yLqFL8qiGaPcuXn/31ywSn7evF1Wzx0rnyyeKNFRUTJu6ry8jfab0iIEEEAAgQALuFwuycjIkIkTJ1pO06dPN63SQHnJkiXij7RixQqJjo429TJBAIGyBRwf8O7966BMe2uJ3Hf7tXJ9r0uldq3q0qBuba/astUbpHe3dlKvTi2pXi1R+vXuJPOXfCi5ubneMiwggAACCNhNILDt0Z8B+/fvF6vp4MGDpkG57k8Uk5KSxB8pOTnZ1MkEAQTKF3B8wPvN95uN0nc//i79Bo+SAUOflXdXfGTydLJl+25p2ri+Lpp0TKN6Zn44OcXMNe4lifsXAFI4nQfm5GWCAAIRIRBO7z20teI/KyPiZA1hJxwd8Kr7rr37dSZ1jq4pt/zzcjn3jJPloVFT5L1Vn7iDuFzRe3gT4uNMGZ3Ex8XqTFJS0sx8f1K6kDAIp3PgQHK6ObfNCWyDiV0+K9ErdjbgME3QoMAs2GBiFxc7meiw2MklOTVTSJFvoOcdqeICjg94le6EZo1k4I095JKLzzHz7pddKCvXfi4ul0sSqyRIesbfXwrwLCcmJuiucnSNeBIGYXUO1K4eb85tcwLbYOKyQRu0CS6XXVoi7vGRYP8r9Xgulz1cbNIMr5PLZR+X6omxQop8A+/Jx0KFBBwf8DZpWFd+27JTMrOyvYBZ7uXMrCyz3qxJfdm6Y7dZ1sm2nXt0JjWqJZo5EwQQQAABBBBAAAF7C1gPeO3djwq37uwWJ5mruJNnvCvZ2Tny9fe/ydIPPpWLWrYwdXZu31LmLFoje/YdlOQjqTJz7krp1aWt+wqMPX67N41kggACCCCAAAIIIFCqgOMDXr1SO37kYHltznI5o2N/6TtopPTt2VGuvbK9Qevb81I5vlkj6dB7iLTqOlAyM7NkcP9eZhsTBBBwpgC9RgABBBAILwHHB7w6XBec11w+XjxBVrz1nHy2dJIMu6efxOQ/27BqYoK8/PS9sn7RBFk7f5y8PXm4eUSZ7kdCAAEEEEAAAQQcLBA2XSfgzR8qDXAbN6hjbm/Izyo0q1m9qtSpXbNQHisIIIAAAggggAAC9hcg4LX/GNFCBMJbgNYjgAACCCAQYgEC3hAPAIdHAAEEEEAAAWcI0MvQCRDwhs6eIyOAAAIIIIAAAggEQYCANwjI15xR5gAAEABJREFUHAIB6wKURAABBBBAAAF/CxDw+luU+hBAAAEEEECg8gLUgIAfBQh4/YhJVQgggAACCCCAAAL2EyDgtd+Y0CLrApREAAEEEEAAAQTKFSDgLZeIAggggAACCNhdgPYhgEBZAgS8ZemwDQEEEEAAAQQQQCDsBQh4w34IrXeAkggggAACCCCAgBMFCHidOOr0GQEEEHC2AL1HAAGHCRDwOmzA6S4CCCCAAAIIIOA0AQLe0kacfAT8LJCeni5bt26V33//PaQpOTnZzz2jOgQQQAABBOwtQMBr7/GhdREkEBUVJXPnzpXXXnstpEkD7whipStBEOAQCCCAQLgLEPCG+wjSfgQQQAABBBBAAIEyBfwU8JZ5DDYigAACCCCAAAIIIBAyAQLekNFzYAQQiEgBOoUAAgggYDsBAl7bDQkNQgABBBBAAAEEwl/ATj0g4LXTaNAWBBBAAAEEEEAAAb8LEPD6nZQKEUDAugAlEUAAAQQQCLwAAW/gjTkCAggggAACCCBQtgBbAypAwBtQXipHAAEEEEAAAQQQCLUAAW+oR4DjI2BdgJIIIIAAAgggUAEBAt4KoLELAggggAACCIRSgGMj4JsAAa9vXpRGAAEEEEAAAQQQCDMBAl6LA5aUnCIHDiVZLE0xOwjQBgQQQAABBBBAQAUIeFUhP+3YtU9aXnGHjJk8Oz9HJCU1TQYPGyetuw2Si3sMlj6DRsq+/Ye821lAAAEEEEDA5gI0DwHHCxDw5p8CegV34ANjTICbn2Vmby5YJT9v3i6r546VTxZPlOioKBk3dZ7ZxgQBBBBAAAEEEEDA/gIEvO4xysrOln+PfFnOaXGydG5/vjvn7//LVm+Q3t3aSb06taR6tUTp17uTzF/yoeTm5v5dKBKW6AMCCCCAAAIIIBChAgS87oF9ZsJbkpGRJcPuucG9Vvj/lu27pWnj+t7MYxrVM8uHk1PMnAkCCCCAQGQJ0BsEEIg8AccHvLMWrpK1H38lL4y4S2JjYwqNsF7F1Xt4E+LjvPnxcbFmOSUlzcyPpGUJCQMr54CeT+akCfFE28EHFMUHQV2K55JjFxe7faZmJ5e0jGwhRb4B70aVE3B8wDv97WXSrEl9mTzzXXlmwizZ9NPvsv7zTTLljcXicrkksUqCpGdkepXzlkUSExNMXpS7DMklGJRvIPxDAIEKC7gqvGdk76guvP+6HPEzSPhXKQHHB7z9r7tCzjvzFKlVs5pJ0dFRold0a1RLNLAaDG/dsdss62Tbzj06E8/2KvHRQsLAyjngcumPJnP6hHTicrncv8yFtAm2PLjL5bJlu0LdKJerHJdQNzBEx3e57OMSFxslpMg3CNGpHjGHdXzA+88el8htN3T3plNPbCbntDhJNF9HuXP7ljJn0RrZs++gJB9JlZlzV0qvLm3dAYNLN5MQQAABBBBAAAEEbC4QjIDX5gRlN69vz0vl+GaNpEPvIdKq60DJzMySwf17lb0TWxFAAAEEEEAAAQRsI0DAW2Qoxjw2SO67/VpvbtXEBHn56Xtl/aIJsnb+OHl78nDziDJvARYQQAABywIURAABBBAIhQABr0X1mtWrSp3aNS2WphgCCCCAAAIIIIBAqQJB3kDAG2RwDocAAggggAACCCAQXAEC3uB6czQEELAuQEkEEEAAAQT8IkDA6xdGKkEAAQQQQAABBAIlQL2VFSDgrawg+yOAAAIIIIAAAgjYWoCA19bDQ+MQsC5ASQQQQAABBBAoWYCAt2QXciNEIC0tTXbv3i1bt24Nadq/f3+EiNINBBBAwPYCNBCBYgIEvMVIyIg0gVWrVsm0adNCmjTgjjRX+oMAAggggEC4CBDwhstI0U7/ClAbAggggAACCDhGgIDXMUNNRxFAAAEEECguQA4CThAg4HXCKNNHBBBAAAEEEEDAwQIEvA4efOtdpyQCCCCAAAIIIBC+AgS84Tt2tBwBBBBAINgCHA8BBMJSgIA3LIeNRiOAAAIIIIAAAghYFSDgtSplvRwlEUAAAQQQQAABBGwkQMBro8GgKQgggEBkCdAbBBBAwB4CBLz2GAdagQACCCCAAAIIIBAggZAHvAHqF9UigAACCCCAAAIIIGAECHgNAxMEEEAg5AI0AAEEEEAgQAIEvAGCpVoEEEAAAQSCIZCamipHjhwJaUpJSZH09PRgdJdjOELA/50k4PW/KTUigAACCCAQNIF9+/bJO++8E9K0fv16cblcQeszB0LAVwECXl/FKI8AArYQoBEIIIAAAghYFSDgtSpFOQQQQAABBBBAwH4CtMiCAAGvBSSKIIAAAggggAACCISvAAFv+I4dLUfAugAlEUAAAQQQcLAAAa+DB5+uI4AAAggg4DQB+utMAQJeZ447vUYAAQQQQAABBBwjQMCbP9SHko7Inn0H89eKz5KSU+TAoaTiG8iJQAG6hAACCCCAAAKRJOD4gHff/kPS5YYH5MLud0qH3kPkypselkUr1nvHOCU1TQYPGyetuw2Si3sMlj6DRoru4y3AAgIIIIAAApEqQL8QiBABxwe8OTm5ctXlF8uqOWPkk8UT5fIO58vjL8yQ1LQMM8RvLlglP2/eLqvnjjXbo6OiZNzUeWYbEwQQQAABBBBAAAH7Czg+4K1Xp5bcdkN3aVC3tlSvlihXdr5I9KruD7/8YUZv2eoN0rtbO9Fyur1f704yf8mHkpuba7YzEQgQQAABBBBAAAFbCzg+4C06Op999aPJOvaYhma+Zftuadq4vlnWyTGN6ulMDienmDkTBBBAAAEE8gSYIoCAXQUIeAuMzC+/b5dR49+QgTf2kNq1qpuruHq1NyE+zlsqPi7WLKekpJn5vkPpQrKvQVa2Pa7E6wcCdvlUIMfdGLu0RV9E7uboLOQJk5KHQM+XkrcEN9cu54mn1/Y6X+zxPpedI3L4SCYpQAaec495xQQIePPdduzaJ7ff/7xccvHZMvCmHibX5XJJYpUESc/INOs68SwnJiboqtSpGe9TonxwvWKiXWacQj1xn0rictmjLVHudrhc9miLjotdmuJyYaLjUTTp+VI0LxTrNhoe032Xy07niz3aEu2OKGpUjRVSYAzMicekwgJRFd4zgnb89fcdct0dI6RNqzPkyQcHSLS+avP716xJfdm6Y3f+msi2nXvMco1qiWbOBAEEEECgQgLshAACCARNwPEB70+/bZMetwyTC85tLgP6dpXdew+IXu31PHO3c/uWMmfRGvOM3uQjqTJz7krp1aWtba7WBe1M4UAIIIAAAggggECYCtg74A0C6uYtO81R3lv1iVze93657LqhJo2eMMvk9+15qRzfrJF5Rm+rrgMlMzNLBvfvZbYxQQABBBBAAAEEELC/gOMD3isuaSWb1kwvlp5++DYzelUTE+Tlp++V9YsmyNr54+TtycPNI8rMRiYIIIBAkAQ4DAIIIIBAxQUcH/BapatZvarUqV3TanHKIYAAAggggAACCPhfoEI1EvBWiI2dEEAAAQQQQAABBMJFgIA3XEaKdiKAgHUBSiKAAAIIIFBAgIC3AAaLCCCAAAIIIIBAJAnQlzwBAt48B6YIIIAAAggggAACESpAwBuhA0u3ELAuQEkEEEAAAQQiW4CAN7LHl94hgAACCCCAgFUBykWsAAFvxA4tHUMAAQQQQAABBBBQAQJeVSAhYF2AkggggAACCCAQZgIEvGE2YDQXAQQQQAABewjQCgTCR4CAN3zGipYigAACCCCAAAIIVECAgLcCaOxiXYCSCCCAAALOEcjIyJDU1NSQpvT0dMnKynIOOj21JEDAa4mJQggggAACCFRKIOJ3joqKkk2bNsmCBQtCmr755htxuVwR700HfRMg4PXNi9IIIIAAAggggAACYSZAwGunAaMtCCCAAAIIIIAAAn4XIOD1OykVIoAAAghUVoD9EUAAAX8KEPD6U5O6EEAAAQQQQAABBGwnEMYBr+0saRACCCCAAAIIIICADQUIeG04KDQJAQQQ8EmAwggggAACZQoQ8JbJw0YEEEAAAQQQQACBcBEorZ0EvKXJkF8pAX3wtx1SpTrBzggggAACCCAQEQIEvBExjPbqRG5urnz77bfyxhtvhDR9/vnnom2xlw6tCb0ALUAAAQQQcJoAAa/TRjxI/T148KBs3bo1pGn//v1B6i2HQQABBBBAIAwFHNRkAl4HDTZdRQABBBBAAAEEnChAwBtBo56RkWE+wteP8UOZMjMzI0jV8V0BAAEEEEAAgbAXIOAN+yH8uwNbtmyRESNGhDRNmzZNXC7X341iCQEEEEAAgYgQoBPhLEDAa3H0kpJT5MChJIulKYYAAggggAACoRLQTzzT0tIkNTU15ClUBhy3sAABb2GPYmspqWkyeNg4ad1tkFzcY7D0GTRS9u0/VKwcGQioAAkBBBBAIPQC0dHRsn79elmwYEFI0+7du0OPQQuMAAGvYSh98uaCVfLz5u2yeu5Y+WTxRImOipJxU+eVvgNbEEAAAQQQQAABBGwlQMBbznAsW71BendrJ/Xq1JLq1RKlX+9OMn/Jh+bLYeXsymYEEEAAAQQQQAABGwgQ8JYzCFu275amjet7Sx3TqJ5ZPpycYuZJSUmycePGkKYvv/xSsrKyTHvCakJjEUAAAQQQQACBIAgQ8JaBrI/20nt4E+LjvKXi42LNckpKmplHR8fIoUOHQpoOHz4ser9SzZo1pV27diFNZ599tuTkihx//PEhbYc6nHzyyeJuirRo0SLkbalbN+8XpQsuuCDkbYmLyzuf1SjUSVwuSUioEnKTVq1auT+1EWnUqFHI29K8eXPTllNPPTXkbTn22GPNa+i8884LeVuqVatm3nNDfc7q8aOioiUmJsYnE33tawdiYmPk9NNPL5YqknfaaadplaI2Fdnfn/voe76+9zdo0MAvfatM2/R1rDDHHXdcyNtSo0YNSc3IlsNHMiudtE+kigsQ8JZh53K5JLFKgqRn/P1cWc9yYmKC2TMlM0panHNhSFPzs1rLX4czJCq+ZkjboQ5NT2guSak5UuPoxiFvy9ENjpWUjChp2PTkkLclvtrRciglR0467ZyQtyU9J860Rccr1OlAUqZkuRJCbnLiP86Wg0eypEqNeiFvS4NjThJ9X6nT8LiQt6V67UaSnCZy7EktQt6W3Jhqsu9Qesjboa+ZpLRc9xhF+9SW5h2ukLobvpb6qz+Wk2++wy/phH63SsZ5F0uVjl39Ul9l2tWo9w2S06qtHNX16pC35eju10hO6/bS8OrrQ96WxEu7iat1G4lr067SSfhXKQEC3nL4mjWpL1t3/P0ty20795g9alRLNPM6NeMlOInj4Mw5wDnAOcA5wDng1HPABB1MKixAwFsOXef2LWXOojWyZ99BST6SKjPnrpReXdq6P4l1lbMnmxFAAAEEAiJApQgggICPAgS85YD17XmpHN+skXToPURadR0omZlZMrh/r3L2YnOwBXJycmXX3v2SlXI8QfcAABAASURBVJ1t6dC+lrdUKYUQ8IMA56YfEKnCrwL67PnUtAzLdfpa3nLFFESgEgKRGvBWgqTwrlUTE+Tlp++V9YsmyNr54+TtycPNI8oKl2ItlAJrP/7a/DLS8Zr75MyO/yez3Vfky2pPWeVXrdsozdvfXCx57t0uq162IVBZgbLOzcrWzf4I+Cqgt/N1ueEBadfrHjnv8tvk0WemSWZW6RcVyiq//2BSsfdVfa/9ZOP3vjaL8ghUSICA1yJbzepVpU7tmhZLUyxYAnrVYejjL8td/XvK16telXEjB8uI56fL9j/3ltiE8srnSq75ouKS10dLwRQXG1NifWQi4C+B8s7Nih+HPRGomMATY2fKqSc2lc+XvSKLZzwl+lz6ZR98WmplZZXXpx7pjpNG/6vQe+uZp52o2SQEAi5AwBtwYg4QSIENX/4g+ui4Pj0ukZjoaLm0zbmiXzRc+/FXJR7WSvmE+FhTh9bjSS6Xq8T6yETAXwJWzk1/HYt6EChP4FDSEfnos++kX+/LpEpCnBzXtKFcdflFsmLtZyXuarV8k4Z1Cr2/at0lVkgmAn4WMAGvn+ukOgSCJrB73wHz5hkXF+s95gnNGsmuPQe86wUXrJTf7/7o7eGnpsiIMa/Je6s+sXxfcMHjsIyArwJWzk1f66Q8AhUV2PfXQbNrk4Z1zVwn+keY/tyzXxeLJavlx0yeLY+MflVmzFkuGiQXq4gMBAIkQMAbIFiqrbzAohXrZcobi0tMeuVBj3DYfRVCn5Wsy54UHx8nSfl/Cc+T55mXV75+3dpyy3VXmKsZus/9IyfJ6Jfe1EWSMwRC1svyzs2QNYwDO1LgcP57aMGLCfreuv/g4RI9yisf774o0bdnRznjtBOkdq3q5n395nuekowCz7kvsWIyEfCTAAGvnyCpxv8C+ga63321taSktzHoEWtUr2puadBlT0pPz5Dq+c9J9uR55uWVb3HqcTL0jn/Krdd3k+H33SQj7+8vby5YxVVeDyDzgAmUd24G7MBUjEAJAp5nzeuTiTyb9b21dq0antVC8/LKV6taRYbd08+8t953+7UyY/zD8vPm7fLjr1sL1cOKnQQiqy0EvJE1nhHVm+t7XSoP3NmnxNSp7Xmmr/XrHCVbtu82j4szGe6Jvok2qHeUe6n4f1/L1z06r56sMr6ZXPwo5CDgu4Cv56bvR2APBKwL1Dm6lins+WNLuvLHtl3SsF5tXSyWfC1fz/3erZWkui9Q6JyEQKAFCHgDLUz9ARVoedappv5ZC/Ouwr6/7gvzhIZ2F5xl8j/76kfpfetwExRrRnnl9WruF9/8LPqNeX2u7yuvL5JWZ/9DEuLjdHdSEQFW/SdQ3rnpvyNREwLlC+iTiS44r7n5Y0v6fvj71j/l3RXr5bJ2Lc3OetuYvrcuzX9qQ3nl1378tSxfs8Hct6uf0I2bOs88EUefAmEqZIJAgAUIeAMMTPWBFUisEi8vPnmPjJ4wyzyD955HX5RHhvQTzxctklNS5Ydftkha/lWE8srv2vOX3Hj3KPPMSX2urz5K5/H7+we2E9SOgFugvHPTXYT/CARVYNjdN8imn34374fdbnzIHeyeJ5d3ON+0Qf9Air63HjycbNZ1Ulb5jMxMeWT0NLmw+53S8oo7ZOkHn8iLT9wtGijrvhGQ6ILNBQh4bT5ANK98gUsuOlu+WTVNVrz1nHy1cqr0uaqjd6cOF54tm9ZMl1NOOMabV1Z5vbfsi+WvyLI3n5GP3nlJXn9pmDd49lbAAgIBEijr3AzQIakWgVIF9FFky2c9Kx/MeUE2LJkkTz44QGLzn0les0ZV895a8P22rPJ6G9rHiyeYurS+DxeMl9bnnlbqsdmAgL8FCHj9LUp9IRGIjo6Sxg3qeN+My2tEWeX19oVjGtWTWjWrlVeNb9spjYAFgbLOTQu7UwQBvwvUr3uU6F8dtVpxaeX1Wem6TZPLxbPNrXpSzj8CBLz+caQWBBBAAAEEELAoQDEEgi1AwBtscY6HAAIIIIAAAgggEFQBAt6gcnMw6wKURAABBBBAAAEE/CNAwOsfR2pBAAEEEEAgMALUigAClRYg4K00IRUggAACCCCAAAII2FmAgNfOo2O9bZREAAEEEEAAAQQQKEWAgLcUGLIRQAABBMJRgDYjgAACxQUIeIubkIMAAggggAACCCAQQQKODHgjaPzoCgIIIIAAAggggEA5AgS85QCxGQEEEIhgAbqGAAIIOEKAgNcRw0wnEUAAAQQQQAAB5wqUH/A614aeI4AAAggggAACCESAAAFvBAwiXUAAgeAIcBQEEEAAgfAUIOANz3Gj1QgggAACCCCAQKgEwu64BLxhN2Q0GAEEfBW48+GxMnfxWrNbSmq6LFi6Tn75fbtZ18mOXftk4vSF0rP/I9L/3tGaJSXlmQ0FJl9+94ssWrG+QA6LCCCAAAJ2FCDgteOo0CYEIkHARn345vvfZPfe/aZFhw4nyyOjX5X1n28y6zp58MlXZPH7H0uXjq2l1TmnaZaUlGc2FJi8s+wjeXDUKwVyWEQAAQQQsKMAAa8dR4U2IYBAwATq160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\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"white\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"#C8D4E3\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"white\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\"},\"bgcolor\":\"white\",\"radialaxis\":{\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"},\"yaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"},\"zaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"},\"bgcolor\":\"white\",\"caxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#EBF0F8\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#EBF0F8\",\"zerolinewidth\":2}}},\"title\":{\"text\":\"Simulation-Based Null Distribution\"},\"xaxis\":{\"title\":{\"text\":\"diff in props\"}},\"yaxis\":{\"title\":{\"text\":\"count\"}},\"shapes\":[{\"line\":{\"color\":\"#d62728\",\"dash\":\"solid\",\"width\":2},\"type\":\"line\",\"x0\":0.03399999999999992,\"x1\":0.03399999999999992,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"},{\"fillcolor\":\"#d62728\",\"line\":{\"width\":0},\"opacity\":0.3,\"type\":\"rect\",\"x0\":0.03399999999999992,\"x1\":0.07509999999999994,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"}]},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -327,7 +332,7 @@
     "obs = observe(spotify, formula=\"popular_or_not ~ track_genre\", success=\"popular\",\n",
     "              stat=\"diff in props\", order=(\"metal\", \"deep-house\"))\n",
     "null = (\n",
-    "    specify(spotify, formula=\"popular_or_not ~ track_genre\", success=\"popular\")\n",
+    "    spotify.specify(formula=\"popular_or_not ~ track_genre\", success=\"popular\")\n",
     "    .hypothesize(null=\"independence\")\n",
     "    .generate(reps=1000, type=\"permute\", seed=76)\n",
     "    .calculate(stat=\"diff in props\", order=(\"metal\", \"deep-house\"))\n",
@@ -337,7 +342,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "661aa2d7",
+   "id": "e5c42605",
    "metadata": {},
    "source": [
     "## Simulation vs. theory overlays"
@@ -346,7 +351,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "c018d339",
+   "id": "4a810cf7",
    "metadata": {},
    "outputs": [
     {
@@ -354,7 +359,7 @@
       "image/png": 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",
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Distribution\"},\"xaxis\":{\"title\":{\"text\":\"mean\"}},\"yaxis\":{\"title\":{\"text\":\"density\"}},\"bargap\":0.02,\"showlegend\":false},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -386,7 +391,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "be6a4a77",
+   "id": "b83c75b5",
    "metadata": {},
    "outputs": [
     {
@@ -394,7 +399,7 @@
       "image/png": 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Bootstrap Distribution\"},\"xaxis\":{\"title\":{\"text\":\"mean\"}},\"yaxis\":{\"title\":{\"text\":\"density\"}},\"bargap\":0.02,\"showlegend\":false},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -431,7 +436,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "162fb3c4",
+   "id": "962bd275",
    "metadata": {},
    "source": [
     "## Faceted regression-coefficient plots\n",
@@ -442,7 +447,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "7400ec80",
+   "id": "e552f09e",
    "metadata": {},
    "outputs": [
     {
@@ -450,7 +455,7 @@
       "image/png": 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TDtusXdrrAoBOjYB8BwcDX1a2hbEHDwDj9FLShVcHAqbTmgVU4zcCbPNe2dLShwN6QPe5gy7rAD14saEdzw8Csn7hVe5GlwE8Aou3TTMHAqjaHSEzGs8AdH5u7ZxS8wKnw2XdH9rvAddzez81TtMCClr+wyYHAwBYHKHig3MPAvkcDE\\u002fDcwsDiZxRU\\u002fzW9wDoroaYeE7fAG\\u002flwoe8Vo8ChSeYn6vPKwJ8TszbZvMDAupmlCw+1sMAevQHXjsO7wI0bi8QoTMLAEoZPCEvqtMBq9yiLpw6YQGC8Pt6iD7TAzttoueYcvcD4Ij6HPti+wFMcm69r28fAWuzOx26ms8DAOSRz8Z6nwKZQ9RUbVrjAmCRkrSvTwMCk16s6KBPCwLx9r8VWdr3ANrhzTPuRtcA+mGRuVmu7wPJDW204XITA4psiyHQIucCb+0qkHRPAwAy1IxBKdrvA51I6JE1kw8DGejnOq13HwF+fXR6hpq3A+Az2Ga5WwsAsKsQ6QXXHwHzZ5W5QjrPA+zBRwMyIwsBt8eWW6g+lwILamDwj57LAL2vI3PDVyMDkEdQ6VRSxwE\\u002fSjqWQvcXANWVJ20BbuMDeNhwW8wHAwMooqDnuk8TAGE931o+8pcDB0MLHGum3wHk10PJ6ibTAUhXyFmP9wsD7r+Fggdi0wAfJakXF0MfA0RBwMxSVw8Aryj0FdxPIwFIfTU6zHsrAeMosMmVYuMASW2S6adipwN7UoZJntL\\u002fAlBfJtnumwcA8BmJPUbrMwPjD1M1BS8bAvywNR+crvcC7krb6ZrG9wAc5qD7l7MbAoVWA5cdlwcC+hC541au4wK7XwMW9kq\\u002fAFMzZZIixysBIK8J1TKm1wP3378fMc7nApywKli7JwMDPg+kCH2m2wHjDXk6QcKfAy42di2Kbq8DIjxxaYWbGwF7I+OBscrzACH6aBr\\u002fducDGRJFjLjXGwF6XsnHabpXAG5L1XMr8ssBwJSE6C3e8wDSX3YwqmsbAQc4bUBs4vsBO0udWEwuxwC5uLMvd\\u002fMHAK+mO6dyIt8A6rf5KFh7DwBYPnmnWA8HAHipKAfIguMDtAXa\\u002fj8++wDOJiNJ2w8nASZkKe54KsMC6OhLzXcm\\u002fwJKFTWc0psLAN876HuYexsAiidiQ7y7CwIEeb0u8IsTAi\\u002fWqJ1xRtMBLy3WiBtvJwN4AQLlyGsPAwL1fkdVwvsAeYOJWHz+zwN+oparJJ7jAX9lzoW\\u002f7usBuM1sDR7C\\u002fwAKUwBFAw6nAWQtNNjg7uMDjPSoI3Qa4wHAjBuiC1bbAyNRhNnM0w8D2DfnA3EykwAbVjAaF3rvAOAXMrSlwucD9eDAm82nDwFMOiY9737jAOI32yHFZusD+zHxsUdS\\u002fwLx5AV94N8bAxdKcD+cOuMCUqas8PbvFwBMMDTYf5avAuQmb+OQZxMAA1A+FM8S8wEA8uLkAVMLAkuj0L1dNvcAsVf5W5H3JwIMDIK0Tr77Awo12llZ7wcDcTLKRHETGwBKNiRaNuLTAXPta7n62ysAXkX6MX2yWQLi5NWV4FL7Au6FhACCsssCeOyBw2ffBwCDek7FPJL3AgOL4OCX0tcAOBrOaHE\\u002fDwBv9OLUdg8bAYeb7mWn0w8ACF0FEVs7EwNodizbiwMfAz\\u002fOh3m2fwsCYypvU18a9wL4EU0xOe7PAnUoZBNkRu8Cu2h0NyGi4wDTnoZXQrcPAYOr6N8E4tMBp+K4mVPDFwPhYosLy+J3A3bkiME+qwMAwN9dgC0OqwDr74p8P08LAmj6UScMbwMB+AqYK5RfBwF6tv3V+BcrAJ15oi\\u002fnyxMD4uxZFZue4wGoqQBYjY8DA+a26no3VwsDvJDHGf4\\u002fGwJj5hg\\u002frMcnACNp354XsnsBu5DTmz8u8wCa3y6DEt8LABWkwBIb9w8BWhEl7mD6SwO0EnwkiybLAkJW\\u002ftSSBwcD6jlr8coy\\u002fwBaOU7ZyrLjAEZt5E5OEtsDrkPPAVl\\u002fAwCEu75ity8bAxPfBhcJGuMA\\u002f9wI2+BSwwFG5Cc1RUa3AtGjtH306wMBu+u+rSD3CwAKjE+J7arrAA8TY8hKkusCKlK1EC3S+wPDzMVRo973AVVzxgJJhxMAswgnd9Zy5wMqd29c+fsXALzgVlXiCo8C6KbcBPTLHwGxnrxia5cLAjK\\u002fBH8QtwcBF3mI9wxzKwE3JDSslAMDAmjkqAvJPxcAt\\u002fX1Jqd3LwIYNyI1DS73ATB3+l0TosMD0VZB8CS\\u002fHwKsp3Hm42K7Ax+80R5vMwsDakpxUln\\u002fCwE4lEJZ9O8LAqAUlECXGfsADAosZScfBwMNqGmDjdMzAygxRm0aCucCigCA0WsutwBTNyF4hvMrAsFTPBodrucBpb90Ow3G1wEIMwDoEoMTAeUPHF+PYz8CPO3Lie8e5wI8L8Np7E7zAbh5BjmROoMAnWDL7W1WzwP7ygY0dz8PAlKkFXlBIuMD2z2eICie\\u002fwCwsXjiU1r7ABoifjpbHtMBeYXky5Fe+wAqwbF9nQ7PAfmkSe0Gut8BKGmXAmACzwAn5vUKWAMjAC7B9T8OjlsBZMwmCapS0wMO0xfezw77ANDFF7mhGz8AHLwv8AzHBwG9V0wJsybjAOTPFA7Hux8AINe4wtDO4wOhJ7+jtwGnAUtOK7+pvsMCkWjaavw7EwKYR1RJDtcHAHMyV9ShewMAzQ6C4s1fFwMFVFQwav7bANMQxVJsIucB57Nv6cFvCwM61jhox0YPALba\\u002faGUxw8CxbwMsb5jDwKVqD0xyhsLA3EtG8XfntsBl464Ir5S4wBOC+QgzycHA6MGmoIF4isDCDMnOyQq3wHCzXnIv9sDADNB7rVuFusDae5CMikG\\u002fwENTW+guAsHA2p533m6VmMAoonUN8nbDwF\\u002fdungTLMTACY\\u002fgzL2R0cBldla3aEbBwFgaVk8gD37ASx\\u002fy\\u002feluxMAUoVC4mIaywCepU2+pEMXAh2NfJkamtsAFRBEb75q5wPBTn8Chy7bABZ2p3yrwssDDIPk4M8y4wIsw5Wr1BsbAHmJlLhfMmcA2FpSsNyi5wCPaRb0fY8LAfnLWvh7IpsBKFkzVRku8wLhsIvUJ28jAZcoXfbvswMBWKumlWfe\\u002fwDBvvFoOQMzA1BQftskSxcCK6nbMU0i1wAckb0fyYMHAwHR9H6jpwsDK4cVLiNnBwOxKtWpyWMHAzhX0GtUBy8BC5L7PfFDDwOD9A\\u002fA6drfA8GL+2w06ucDKQGC1ra2ywIDYZFk8WcHAgqmJTvnat8AYlq5MqOCswHZHQ+kdNLbAgcBxBUk0tcCMhTJJJuS+wFvNdi3Fcb\\u002fASIFBQB2KxMDPPKxWqMuywPpPAYtObr3AosmW5gJPqMDPTa9BaG69wJA0daF0MLnAPNZKoVUdtsCoJFBgzXazwByE\\u002f7PiKL7AjABsxOCrycBpwqoGX4+3wBytuPa9G7nAVwngzLNtu8AxAdy8AkPEwDHQ4+Q\\u002fesPAgWHFlcRdqcBZZpMoGYShwCLde+deD7PAbjIHC0Y+vcCqnqyYyTK8wK7r60xFDMDAS207pLpRwcDyh2QxlXW4wKJdJ6zagMHAxvfSbKHksMDXp0S7vGulwBWmEAjURcjApoUw12mHvsCVm0d969+zwFZbBCcOSq\\u002fAHwExm0NywsBRg6ZTAXyuwB4EedHDTcTANNx6xk6OpMAv0t38bWDIwL+GxK4Vo77As+Szeyp2vsDqFQaaUPXFwJ8sWbwB6arARrsdmgtrsMBylsx\\u002fJZjBwGCIQsJ09K7AuCLZwrMPxsBxkPkwZC26wK2S74TRsMbA1yZFeJGNvcAWWi3lqHi7wE+8SEW458nAbi\\u002fdSZoIw8A5AgRzx8S5wJLgdnC9VsPA\\u002fuZMuxA0ssDQUpY5O\\u002fGkwPCjO2L03L\\u002fAG9I\\u002f5kReqsCv9oyJnp\\u002fEwNdw4RspYMHAIPsiU680wsCtwtDBp9iiwKNEyXew+cjAvf1pInt+wcCE7xFQAcO5wMh7s3dUsbDA\\u002fEEPj4xvrsDaBpcEftC8wERvpnVCtcLAFH+Tu4HEuMBUwwIYgKGUwECBUIxXgsLAyFQkFqkQtMB3Xl0qek7AwB8WMWgpY8DAsfbBNSCspsCM1JTKrQTBwIzIGHvyIrjAVPFM5\\u002foimsA2zlAxlgK7wJINhyihbLnAMdfkWmD4tsDaxha8GDa9wO6gQRHDobDAN3JCf2cSqsBR\\u002fjPieh65wA5j5O+6eMLA8NedKe9Fq8AcHa8iywejwI4\\u002fqB6SirDAx8yIDA7twsAIXd+Q0cy\\u002fwFTGJDRkj7bAHBXH7iz4wMDetdiT5Ri0wDZKNak6mb\\u002fAp0f+SavTusCnJXgYbTuywG7WgwnSO7fAVIWBBGmRuMAwCKRtoFzDwAYomp\\u002fPG7bAgIcVGuCavcARbn3rInq6wJy11J8q6bTAYlb1ajCYtMAZMR7rkALLwJmtKDhUHrjABK\\u002fe4plKtsBmW2gm50+8wIB\\u002fz\\u002fnO+qrAAF6mCR3vxsAbwAMugEGzwKZZkw4XscLA6XjU+So5wsBi7v+tCMCcwFexwWn9P7\\u002fA1wLPEZB2wcB1YegbDxumwAw1MvQIZLrAXHfO5XuotcAoQvJxKsLAwOgdNP9YV7TADJYHiD9cu8BNbrCVxwvGwGm3nfGLVsbAQ3ChnTtyxMCY+Lx7BFKzwNCk6a\\u002f3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TDtusXdrrAoBOjYB8BwcDX1a2hbEHDwDj9FLShVcHAqbTmgVU4zcCbPNe2dLShwN6QPe5gy7rAD14saEdzw8Csn7hVe5GlwE8Aou3TTMHAqjaHSEzGs8AdH5u7ZxS8wKnw2XdH9rvAddzez81TtMCClr+wyYHAwBYHKHig3MPAvkcDE\\u002fDcwsDiZxRU\\u002fzW9wDoroaYeE7fAG\\u002flwoe8Vo8ChSeYn6vPKwJ8TszbZvMDAupmlCw+1sMAevQHXjsO7wI0bi8QoTMLAEoZPCEvqtMBq9yiLpw6YQGC8Pt6iD7TAzttoueYcvcD4Ij6HPti+wFMcm69r28fAWuzOx26ms8DAOSRz8Z6nwKZQ9RUbVrjAmCRkrSvTwMCk16s6KBPCwLx9r8VWdr3ANrhzTPuRtcA+mGRuVmu7wPJDW204XITA4psiyHQIucCb+0qkHRPAwAy1IxBKdrvA51I6JE1kw8DGejnOq13HwF+fXR6hpq3A+Az2Ga5WwsAsKsQ6QXXHwHzZ5W5QjrPA+zBRwMyIwsBt8eWW6g+lwILamDwj57LAL2vI3PDVyMDkEdQ6VRSxwE\\u002fSjqWQvcXANWVJ20BbuMDeNhwW8wHAwMooqDnuk8TAGE931o+8pcDB0MLHGum3wHk10PJ6ibTAUhXyFmP9wsD7r+Fggdi0wAfJakXF0MfA0RBwMxSVw8Aryj0FdxPIwFIfTU6zHsrAeMosMmVYuMASW2S6adipwN7UoZJntL\\u002fAlBfJtnumwcA8BmJPUbrMwPjD1M1BS8bAvywNR+crvcC7krb6ZrG9wAc5qD7l7MbAoVWA5cdlwcC+hC541au4wK7XwMW9kq\\u002fAFMzZZIixysBIK8J1TKm1wP3378fMc7nApywKli7JwMDPg+kCH2m2wHjDXk6QcKfAy42di2Kbq8DIjxxaYWbGwF7I+OBscrzACH6aBr\\u002fducDGRJFjLjXGwF6XsnHabpXAG5L1XMr8ssBwJSE6C3e8wDSX3YwqmsbAQc4bUBs4vsBO0udWEwuxwC5uLMvd\\u002fMHAK+mO6dyIt8A6rf5KFh7DwBYPnmnWA8HAHipKAfIguMDtAXa\\u002fj8++wDOJiNJ2w8nASZkKe54KsMC6OhLzXcm\\u002fwJKFTWc0psLAN876HuYexsAiidiQ7y7CwIEeb0u8IsTAi\\u002fWqJ1xRtMBLy3WiBtvJwN4AQLlyGsPAwL1fkdVwvsAeYOJWHz+zwN+oparJJ7jAX9lzoW\\u002f7usBuM1sDR7C\\u002fwAKUwBFAw6nAWQtNNjg7uMDjPSoI3Qa4wHAjBuiC1bbAyNRhNnM0w8D2DfnA3EykwAbVjAaF3rvAOAXMrSlwucD9eDAm82nDwFMOiY9737jAOI32yHFZusD+zHxsUdS\\u002fwLx5AV94N8bAxdKcD+cOuMCUqas8PbvFwBMMDTYf5avAuQmb+OQZxMAA1A+FM8S8wEA8uLkAVMLAkuj0L1dNvcAsVf5W5H3JwIMDIK0Tr77Awo12llZ7wcDcTLKRHETGwBKNiRaNuLTAXPta7n62ysAXkX6MX2yWQLi5NWV4FL7Au6FhACCsssCeOyBw2ffBwCDek7FPJL3AgOL4OCX0tcAOBrOaHE\\u002fDwBv9OLUdg8bAYeb7mWn0w8ACF0FEVs7EwNodizbiwMfAz\\u002fOh3m2fwsCYypvU18a9wL4EU0xOe7PAnUoZBNkRu8Cu2h0NyGi4wDTnoZXQrcPAYOr6N8E4tMBp+K4mVPDFwPhYosLy+J3A3bkiME+qwMAwN9dgC0OqwDr74p8P08LAmj6UScMbwMB+AqYK5RfBwF6tv3V+BcrAJ15oi\\u002fnyxMD4uxZFZue4wGoqQBYjY8DA+a26no3VwsDvJDHGf4\\u002fGwJj5hg\\u002frMcnACNp354XsnsBu5DTmz8u8wCa3y6DEt8LABWkwBIb9w8BWhEl7mD6SwO0EnwkiybLAkJW\\u002ftSSBwcD6jlr8coy\\u002fwBaOU7ZyrLjAEZt5E5OEtsDrkPPAVl\\u002fAwCEu75ity8bAxPfBhcJGuMA\\u002f9wI2+BSwwFG5Cc1RUa3AtGjtH306wMBu+u+rSD3CwAKjE+J7arrAA8TY8hKkusCKlK1EC3S+wPDzMVRo973AVVzxgJJhxMAswgnd9Zy5wMqd29c+fsXALzgVlXiCo8C6KbcBPTLHwGxnrxia5cLAjK\\u002fBH8QtwcBF3mI9wxzKwE3JDSslAMDAmjkqAvJPxcAt\\u002fX1Jqd3LwIYNyI1DS73ATB3+l0TosMD0VZB8CS\\u002fHwKsp3Hm42K7Ax+80R5vMwsDakpxUln\\u002fCwE4lEJZ9O8LAqAUlECXGfsADAosZScfBwMNqGmDjdMzAygxRm0aCucCigCA0WsutwBTNyF4hvMrAsFTPBodrucBpb90Ow3G1wEIMwDoEoMTAeUPHF+PYz8CPO3Lie8e5wI8L8Np7E7zAbh5BjmROoMAnWDL7W1WzwP7ygY0dz8PAlKkFXlBIuMD2z2eICie\\u002fwCwsXjiU1r7ABoifjpbHtMBeYXky5Fe+wAqwbF9nQ7PAfmkSe0Gut8BKGmXAmACzwAn5vUKWAMjAC7B9T8OjlsBZMwmCapS0wMO0xfezw77ANDFF7mhGz8AHLwv8AzHBwG9V0wJsybjAOTPFA7Hux8AINe4wtDO4wOhJ7+jtwGnAUtOK7+pvsMCkWjaavw7EwKYR1RJDtcHAHMyV9ShewMAzQ6C4s1fFwMFVFQwav7bANMQxVJsIucB57Nv6cFvCwM61jhox0YPALba\\u002faGUxw8CxbwMsb5jDwKVqD0xyhsLA3EtG8XfntsBl464Ir5S4wBOC+QgzycHA6MGmoIF4isDCDMnOyQq3wHCzXnIv9sDADNB7rVuFusDae5CMikG\\u002fwENTW+guAsHA2p533m6VmMAoonUN8nbDwF\\u002fdungTLMTACY\\u002fgzL2R0cBldla3aEbBwFgaVk8gD37ASx\\u002fy\\u002feluxMAUoVC4mIaywCepU2+pEMXAh2NfJkamtsAFRBEb75q5wPBTn8Chy7bABZ2p3yrwssDDIPk4M8y4wIsw5Wr1BsbAHmJlLhfMmcA2FpSsNyi5wCPaRb0fY8LAfnLWvh7IpsBKFkzVRku8wLhsIvUJ28jAZcoXfbvswMBWKumlWfe\\u002fwDBvvFoOQMzA1BQftskSxcCK6nbMU0i1wAckb0fyYMHAwHR9H6jpwsDK4cVLiNnBwOxKtWpyWMHAzhX0GtUBy8BC5L7PfFDDwOD9A\\u002fA6drfA8GL+2w06ucDKQGC1ra2ywIDYZFk8WcHAgqmJTvnat8AYlq5MqOCswHZHQ+kdNLbAgcBxBUk0tcCMhTJJJuS+wFvNdi3Fcb\\u002fASIFBQB2KxMDPPKxWqMuywPpPAYtObr3AosmW5gJPqMDPTa9BaG69wJA0daF0MLnAPNZKoVUdtsCoJFBgzXazwByE\\u002f7PiKL7AjABsxOCrycBpwqoGX4+3wBytuPa9G7nAVwngzLNtu8AxAdy8AkPEwDHQ4+Q\\u002fesPAgWHFlcRdqcBZZpMoGYShwCLde+deD7PAbjIHC0Y+vcCqnqyYyTK8wK7r60xFDMDAS207pLpRwcDyh2QxlXW4wKJdJ6zagMHAxvfSbKHksMDXp0S7vGulwBWmEAjURcjApoUw12mHvsCVm0d969+zwFZbBCcOSq\\u002fAHwExm0NywsBRg6ZTAXyuwB4EedHDTcTANNx6xk6OpMAv0t38bWDIwL+GxK4Vo77As+Szeyp2vsDqFQaaUPXFwJ8sWbwB6arARrsdmgtrsMBylsx\\u002fJZjBwGCIQsJ09K7AuCLZwrMPxsBxkPkwZC26wK2S74TRsMbA1yZFeJGNvcAWWi3lqHi7wE+8SEW458nAbi\\u002fdSZoIw8A5AgRzx8S5wJLgdnC9VsPA\\u002fuZMuxA0ssDQUpY5O\\u002fGkwPCjO2L03L\\u002fAG9I\\u002f5kReqsCv9oyJnp\\u002fEwNdw4RspYMHAIPsiU680wsCtwtDBp9iiwKNEyXew+cjAvf1pInt+wcCE7xFQAcO5wMh7s3dUsbDA\\u002fEEPj4xvrsDaBpcEftC8wERvpnVCtcLAFH+Tu4HEuMBUwwIYgKGUwECBUIxXgsLAyFQkFqkQtMB3Xl0qek7AwB8WMWgpY8DAsfbBNSCspsCM1JTKrQTBwIzIGHvyIrjAVPFM5\\u002foimsA2zlAxlgK7wJINhyihbLnAMdfkWmD4tsDaxha8GDa9wO6gQRHDobDAN3JCf2cSqsBR\\u002fjPieh65wA5j5O+6eMLA8NedKe9Fq8AcHa8iywejwI4\\u002fqB6SirDAx8yIDA7twsAIXd+Q0cy\\u002fwFTGJDRkj7bAHBXH7iz4wMDetdiT5Ri0wDZKNak6mb\\u002fAp0f+SavTusCnJXgYbTuywG7WgwnSO7fAVIWBBGmRuMAwCKRtoFzDwAYomp\\u002fPG7bAgIcVGuCavcARbn3rInq6wJy11J8q6bTAYlb1ajCYtMAZMR7rkALLwJmtKDhUHrjABK\\u002fe4plKtsBmW2gm50+8wIB\\u002fz\\u002fnO+qrAAF6mCR3vxsAbwAMugEGzwKZZkw4XscLA6XjU+So5wsBi7v+tCMCcwFexwWn9P7\\u002fA1wLPEZB2wcB1YegbDxumwAw1MvQIZLrAXHfO5XuotcAoQvJxKsLAwOgdNP9YV7TADJYHiD9cu8BNbrCVxwvGwGm3nfGLVsbAQ3ChnTtyxMCY+Lx7BFKzwNCk6a\\u002f3mHXAxIl0gNg8wsDUPDEUV5G6wLq5WOd7d8bAU8oC+wWRpsAemksLYZO\\u002fwIh2pgAIn7TAForbk3YCu8DIvP0fOx2kwMnR59VFp7\\u002fABJxqAgCUyMC\\u002fTw6Vzzy7wDTL9bHyfKDARjqvFLMSxcBOMn6m1xC7wGSyj8R0HbPAsNAOi2zln8Deeau3t6yxwEhCCsPsOsPA+VUCdFp6vsAarTq7p5G9wBQBqePiT6LAD4pV7lTfvMB+t7t8nNywwNi+zTyFMr\\u002fAkLbjOc1Sv8AuAdWXmgXGwAzpGtHDgL\\u002fA+Z17zc+ExMCALmrfTnS5wG2HqIizD67AThAU7HZnx8D++AtN\\u002fLmywOyNtIlIeMHAOk7FRX4wosCeyfK7KSfCwOAWeCKyFanArDomz1fQvMC+SA8WhjXJwP1bIJ49m8PAMENJYLhccUCN8i30KGXCwANpGEo34L7AQAC9p5m5T0BuKIaSM1e1wASHE\\u002flhU8XADQunaCF5vcCfGluWqi6ywH4wugZ4vcPA7UJ1RLMqs8CinVZFBy\\u002fEwOzHeOxZV8TAmZZ6OoiVwsBd6p75H5HHwEtCF7IiCMDA\\u002f+yN4c4RwMDc7oYPVBnDwNjpBXM6VsDAdH1J1C6hvcAQYp2\\u002fJNqtwCsnOqowq7PAecHDE1Bax8B4M8\\u002fe0+O3wP4cbsH6xbrAfku5s9FbtcCqaAPZcrWrwDKhVtGQ5KJALQKDv0gDwsC\\u002fkLDpyTWmwBT7HOusRMTAp7+\\u002fnds8sMAAf31QY9yjwMRPi40pQMXAYCpc43A7uMA07ZZGql3EwB0j8ZnZJLzAqVILzx+wq8AQ\\u002fx2GwQ7HwLmRiRg8R8PAwVHy+drJvsCqKgnj29m\\u002fwC2HjKdmuqjAyaKxKl8utMDnDaKs1SDCwEpRqiFEecfADEwzJCS4yMBWLtH6NO\\u002fDwH\\u002fYRkupocXAPY5d2S9\\u002fmcAD5KqYAxfLwNLIbH1K573ANtMb08zatcB5FYmyEGW+wKzts0woKsfAE+JSerWEw8DQnarjM9q2wBENzZtkr7rA71sMlb6Bt8CxDc\\u002flHdG4wLMs0Z4bFsDAs2QZSCbIvMA2w\\u002fQlrMjAwLJ\\u002fLk0VFL3ALi7kFXbNwcD87QRdj1nBwMTecaPjFq3AUnp5AyxntsCfXV+uJjK2wNlfF9W+Y8DAw\\u002f5FVuW3usD9yVvMmsrJwJbpAjvwSqDA\\u002fn0ehYHIwcB7zb0BCYrFwCpammEg+8DA8soaU+HCvMAolpLz2j24wPqoMDMjRcjA6SMemCrGxsD4Cq6PVcDGwJDbdo1zLcPAG11cB9PhwMCZ+0ovrp6mwN2v1It2z7nAEJjprVSBv8Bol410pwm1wILeAY71osrAHaLIewfqwcA\\u002f6D+2T224wMwIDFRzv7bA7KljRiDMu8CXhW9ecKXFwE7q4eLGlcHAAlyOtIaBtcCDm8JeDVSzwBHOZsUSib3A1eGzTZ5hwsD\\u002f0bks4ujDwLQhctCR+LnA1OveCut6vsDZfcCM+TzHwIQoT4sjZsXAFGVLCM9YgsBwxBetQd+3wDZIAWHJ\\u002fsTAG98Qk6YQw8CQ1TlTc2jBwAzfGxS\\u002f9b7A2NtWLvCMxMDGbNT1FfG9wPQ6ZNmhorTAlD2yGm4ykMC6UXslG0HFwDB6pLu9j5vAeBygfB58r8Do5cl5953BwJpPinAsqszAh8dcGMQ9q8CN6tN+Z3zAwPX8U5pOA77ATWk7aMyRusArT+\\u002fNYG20wCMc3j1ma7bA51veZwE8usDt636J7PLCwOMd\\u002fgBTzMHA3BMFKclgtsCacAQFUQazwDoAVReVH77AF5sYNa7Ww8Bzh3rhDc6gwKeKM6ymf8HAlhex6rAfusCppVDxe9DAwKeCxHv1rrXAyk8oBnKKrsBp1ikWAtq\\u002fwMkAMtbZisDAUtGC5kgFtMD2weEIg1a8wGG4mygCI8jAz\\u002f0gbdRGtsDEFAiUPsTAwKokwPMDDcPAZJRyzqwctsDRMRzl+BjCwO+7UhzTuqnAcMW6StXNxsBuRysHedmgwB+QGA3SMbfADzurCI+Tw8BmL2gQJZOxwDKSaADIesLA+hMQfbiCycCwzWLfe3OXwCP2i5gQ5r3AzCJW6bZnw8BX79d8a8CpwLnLje3MJLXA+aHZHvrSucC5ikNrdzC+wAir5rEsNbXA79NP\\u002fJKhrcBoZ9\\u002fLZjy9wNRWWniuAMjAQl8KmB3KtMA1fR8Sa5HDwPktt3ksgLDADG06xC\\u002fjxMBQYT2hIm7HwID0Vws6hcHAR6kNeEfuw8Ar1iagDSGowKoF70gF5rzAkVuryjQdz8DRvCBtF4G9wHeZ6EHGSb7Azo3oNhkUxcDEOlDGYIrBwLa9FdOyJsDAESNw4dz1tcAp91Wt18etwB7nMmQ3R6\\u002fAlnh5XIsQusDQyaRQfFDAwPsV0nusSI3AyMqnUWY3wMDVOYJWJxHGwP8QdUTqS7XAeJGR+ozFyMBwqWP2of7BwH\\u002fFCB1LPMnAcVYLEYEgtcACNlg0Tda6wMRt56a\\u002f+7DArIPSAqXqvcASiIfsOte9wIR0rpOcJr7AKyM1CD1MxsDvB52VKRrFwNWFqFpeRMjA58mMOLfluMAr3ah7tPSgwGLPNaUSFcPAYavy96Y+vMDw1rAC4S3CwPS1u\\u002fybKLnA3sS+rEATw8DRIF6RTCm6wGizD8cX46zAysj3ngs4v8Bzo1XxsKqhwMyglOaHNcDAUF+Q30CaxsDca3t9URSjwA4I45jt+cPABHgb8cqZvMDGfFaq\\u002ffSwwBrbvbNxv7rAS4b1aEeNwsBYTEEqiUK6wCIF0RtsxsPAxgqCEeHsvMDk3\\u002f9l3lS5wBL+v1r+WrnApBoWbrdjwcBS0SFFC1qlwFj35vfFYLbAR3mtKP72usD2SjZ5MmDAwKKhT\\u002fHn0bXArmPn3Wd0tsBwzJBiKZZYQDqrSQaW7rzAZGHzLUriu8D59kvGIk7CwL9YJrTO5L7AbQnSYjRAo8AMVqVIO5\\u002fCwOh08xoYNbLAfhJqGPC4vsD96hl0b87BwKJEB8QAvsDArp0Ic9mTvMCSX1jhPZnEwK0mNQ4JV73AFQm13\\u002fCzwcCgMMIqoKK4wLFCbW2cUKrAkR7+X8W0tsA8i26bxZq5wANwRyP2T6vAfdnj01oZscDW8A8FfQenwBBGCOJuJb\\u002fAOcD3bYk5wcD9mCNCHIXAwNbgxnB5DL\\u002fA2Gv6GZH3wcBgxZe9RpLJwCTir977OrDAW94kE+G9wcDSq2jtUoC1wE95V7Y1McPAKKiJYTh6v8AHRSs1kKi0wLz0nUr6GMbAMNh\\u002f3HBtvsDubaA2CLKVwActDe+2rLLAN35VfYQroEAgOB4YnBKSwKAXUZ\\u002fCK8HAnLnCrYo8w8CSbgcmVhO3wGqmh91lWbnA7m2YuxYFvsBUAc7nY6SowLQSN4O1ncLAuoEyVI3nw8BDj4VYZFDMwEDXhqOm4HhA\\u002fAgUYmQMwsDthPlzy1u3wPlsBWbxlMrAGQ6jAt0+wcD6tIqXX7W5wI0de1lu373A\\u002fHoxMTfAkcB4z9GRaQS5wKWoiFMRBrnAVUJ+Cz57tMBWCHNeuS3FwOh4ZapQVcfAIeNWRJ3YtcCsQZoo0TWWwG2GxwlhXsLAfl077M9mucD12Ep3maDBwM8kHFWiqJjAPZtrl0MhusCoATfZpBO8wMZttJ5jNbPAtBsIO8ZRsMDWDM+YHOLCwJXxvfXytMDA3BNCLBSukMDx133r63jDwPRb8Doc5rHAi6EuhvGBtcAs\\u002ffjL08K9wMe4hIpX4L3Am06oaeOJw8D5pUfi2+CwwK0oYB13cr7A1YWW6U9stsDIimD0B7zEwKmxBtYcx7vAFO95BNVHv8D8FukmCDKswNKRPqzSH7vAIWFj+VBiusDkXz36uO2kwNjIcNga67TAoujHwyPKssByk1VSepC3wO0PPfCt6LbAVK6dFLVlwcC0wlay2ja+wLkrkrsLicLA+G\\u002fwUZENuMDu5ADyx263wOES0ueUwMLAldMqUGRgwcBqSf\\u002fOS8fMwAovvDechMLAo6gXowBXucCkmyRtv8TCwNuGCo78p8DAy5rweiaMs8AO6OkaqfHAwNf+TEVSs7jARc8wEdtKvcDKHhc+FoC8wDd1wPD2rqfAFan4GaBQs8AP6lPiPR68wKzvs3f3cbTA5RWhY+rAt8CQnKFesyzIwE5EPPZ9D73AkXsByKzwpsA3311Pmm6+wNp9L3FbJK\\u002fAqAwIm4AersDYjy5OkJaqwMFLr0i2KL7Aano8WA9HwcCsmoh+J8fAwEQ3rQVIZcTAxM6WXc0MwMDWefMkmOe1wNOBauuBwq7AGm6sAES1o8BG4CvmiKSrwAX4x3VWZbjAb0EU4y5oxsBOr0j6UJvBwN+nJOGbjbTAGmpywYA1tMAYncgXJwvDwEKh+gPvYazA3wn6gmBQs8ALUYeOm9e3wB5KGSjpG7jAxPFRyaS2xsAkM2pYSa\\u002fBwIPOMMR8m7zAPYq4WkptxMAeoacJU1m8wDMOzMmV5rnA1xXrnvnFv8BbUSFYV2upwAqfR4\\u002f6BsDA9C6iDJyBtsAOVfrCMj21wJeJqTx\\u002ffMPATDdm2oG4rsA=\"},\"xaxis\":\"x3\",\"yaxis\":\"y3\",\"type\":\"histogram\"}],                        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domain\"},{\"line\":{\"color\":\"#1f77b4\",\"width\":2},\"type\":\"line\",\"x0\":101.50677381488379,\"x1\":101.50677381488379,\"xref\":\"x2\",\"y0\":0,\"y1\":1,\"yref\":\"y2 domain\"}]},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -603,7 +608,7 @@
     "\n",
     "houses = md.load_saratoga_houses()\n",
     "boot_fit = (\n",
-    "    specify(houses, formula=\"price ~ living_area + bedrooms\")\n",
+    "    houses.specify(formula=\"price ~ living_area + bedrooms\")\n",
     "    .generate(reps=1000, type=\"bootstrap\", seed=1)\n",
     "    .fit()\n",
     ")\n",
@@ -612,7 +617,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aa65c338",
+   "id": "635bff66",
    "metadata": {},
    "source": [
     "## Model plots"
@@ -621,7 +626,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "e80b821e",
+   "id": "507fa54d",
    "metadata": {},
    "outputs": [
     {
@@ -642,7 +647,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "4255f5a5",
+   "id": "8ea850d6",
    "metadata": {},
    "outputs": [
     {
@@ -662,7 +667,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "da5d979a",
+   "id": "7cef70cc",
    "metadata": {},
    "outputs": [
     {
@@ -682,7 +687,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5a2e2318",
+   "id": "0fa833e7",
    "metadata": {},
    "source": [
     "## Saving figures\n",
@@ -727,24 +732,24 @@
   "source_map": [
    11,
    17,
-   30,
-   44,
-   52,
-   56,
-   59,
-   65,
-   67,
-   71,
-   82,
-   86,
-   90,
-   92,
-   98,
-   108,
-   112,
-   119,
-   125,
-   130
+   35,
+   49,
+   57,
+   61,
+   64,
+   70,
+   72,
+   76,
+   87,
+   91,
+   95,
+   97,
+   103,
+   113,
+   117,
+   124,
+   130,
+   135
   ]
  },
  "nbformat": 4,
diff --git a/doc/_build/jupyter_execute/guides/regression.ipynb b/doc/_build/jupyter_execute/guides/regression.ipynb
index 4a16069..1cf4e0c 100644
--- a/doc/_build/jupyter_execute/guides/regression.ipynb
+++ b/doc/_build/jupyter_execute/guides/regression.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "dc133df4",
+   "id": "b7ed0f10",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "edb1298f",
+   "id": "42afbe20",
    "metadata": {},
    "source": [
     "# Regression\n",
@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "ac90e4f3",
+   "id": "027aca93",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "92055efc",
+   "id": "ea55826e",
    "metadata": {},
    "source": [
     "## Coefficient table (with confidence intervals)"
@@ -59,7 +59,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "b9670a62",
+   "id": "1ee20735",
    "metadata": {},
    "outputs": [
     {
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b59967c1",
+   "id": "6b7832df",
    "metadata": {},
    "source": [
     "Change the confidence level with `conf_level=` (e.g. `0.99`).\n",
@@ -109,7 +109,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "8ab3372a",
+   "id": "13a102b1",
    "metadata": {},
    "outputs": [
     {
@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f6ba991",
+   "id": "d536d1f6",
    "metadata": {},
    "source": [
     "## Model-fit summaries"
@@ -160,7 +160,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "b22b9ff0",
+   "id": "e1b99cdf",
    "metadata": {},
    "outputs": [
     {
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f99874da",
+   "id": "f73467f1",
    "metadata": {},
    "source": [
     "## Correlation"
@@ -207,7 +207,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "4cb6534a",
+   "id": "6de7c700",
    "metadata": {},
    "outputs": [
     {
@@ -245,7 +245,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f71362c8",
+   "id": "707696a7",
    "metadata": {},
    "source": [
     "## Visualizing models\n",
@@ -256,7 +256,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "77fc53b2",
+   "id": "380f5d74",
    "metadata": {},
    "outputs": [
     {
@@ -283,7 +283,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6b3d24d2",
+   "id": "78a24d2b",
    "metadata": {},
    "source": [
     "For the plotnine engine you can also drop the parallel-slopes lines onto your own\n",
@@ -299,7 +299,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "77834dfe",
+   "id": "ebe56ce9",
    "metadata": {},
    "outputs": [
     {
@@ -307,7 +307,7 @@
       "image/png": 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",
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a88LSalAG1ZBGF3YncCUGEdLPp2lQAKcS2Zlp8BAIO+dWpSNWUCmpkPozg+WQG4+n\\u002flAx6XAWC9\\u002fJrLZisBQRacfIIpwQGwauLZEtrNAeMUaI2h3hUDcMDzp4VC5wLC78aMpOXNAzXSZ8pwOk0CK0kzT6Le8QOSFUmrcs6nALP\\u002f51c4ns0CsNNHtsbqzQBAlOllrssBAcICtiUYDhkAX0t5iX0qhwEbuYbQnirPAlciSNVvgp8CoEmJ70zWSQNoREH7usLfA6MoucDpakUDsnTwHKkqUQJpmQ4f90aXAOHrdP9gWgUBiBGzb092gwCAUTGXc\\u002fmpAiBnTpNytlEAUNges+9uvQCSX+Q6GLHrAAItTT91Ku8AZfjhlY0GUwAD6z4C8ZJRAHiudadJMtMALEjn6H6yfwH+dH2tV0bDANAXRZ6WEnUCoAjuJ8zWUwG66ibjjQJnAX1Cn04gxtcAyRdLuL06mwALzWlY8DbDAvSmz8i5rtEAYmjmN5EamQDpaWDx7wsHAaG7ZAI5susBgvXH7UKl8QJB\\u002fsY5VtbPAwD2y2ypOcECo4o6+7i2KwEjuvu+SLYhA\\u002fC\\u002f63tZth0CwpeceXNKmQGDoQXsd3LbA5B7vK3HHtkBIAotss66EwNIw6uqmG61AVh5KqXBcr8AavbMJmKauwPRC33FEfrJA0iJ+2i5tpEAkYV++YWieQLQykDISgYVASmjzzQwYxUCUoSgd7niqQPlSIaOoUrVAZsYGHsnNmEDPj4eD8pO+wEa9JTIcrKdAUNNhZ1lQgkCc2RMLy26qQMuRQYESz7ZAKaXQMkdbwcA4E2\\u002fbzY+8QBtCms3nGKBAF3tvHc9Fg0DOP43J4uTBQCjWYIvyHZ\\u002fAOFDV7CiqrsDDIEDvcvqgQA8eU1u7S69ApSG66jZXsUAfk0hCE8KqQKARjSQqhKNAKvRBJZ0sh8BFpeioLgWkwJw8BhDItZbAgdZmuXozokBft6KNH8SNQH8VDgljuZBAZZnznffvlMAq+GA+3ySVQORH1jdP66dA9v7FPcWRksAhKFp153G+wI5NtgLewrlA0l\\u002fbVvyevcC+N6rRLUGSwIrdv\\u002fBmnbZArmccw+aSksDwueD0MMtvwPRGn35T2XHAECOhLHiEssBErlm5PXKaQFxwvdppZInAiKIA3FIpkcCO6MVC1GicQHsiIRPrDrzA9lacmmmMp8Dixtqjr+KnQMy1AF4+4JJAklLc67hPvEBYTamSEGC0QO7AQV8PEpbAbAuuVdn4uMDCjmGGHjekQLQxZJFApKBAqIG+EUhXnEBwUqzQR528QBCkWaafnsXAKK7XMGxyu0DYrqOIUUi2QMmTbv16KKXAVjEIoYnZrkB4\\u002fJJ1wsa8wJAWcH+QBqVA9QI85ljIskBJjX6VOYehQLQof1+41aVAXZZ3F9cstMBpJX6j9mmswMoanm8OkKTAWsEYFPHsmUCSnb+BNAaHwCKgkhj\\u002feaVAtItY2kSAfMAPslXp6Z2zQIaIdpWba7BA4fV8h2n+o8A+X8lZH2y1QKAPWgRUHFZAMOpQs6oRacCQxcZfHgl8QJIg6fRFkZ3AEgyVaOXNp8AI+iT2kNyzwBKRrVZHHKxARO5Q5YmhlMAmYHhi9n7DwHoOIFjurpJAGCwZenWZcsCmOJIUhxaywAWIrcto4sFAKFYP1blNgcCchcUOJX2fwNaXSqOnxpHAdtCC8f+QmMAovLZe5AizwGT8sh5PiqfAkPhSqaK0tcCfY61bxLa2wMx8nm4N4ppAcC7PaLu8fkABg5LcnVu5QBBKYWLed2lAHQUqP91sosAIHj8o76qwwC4iOP2cdbhAYOY9LHxje8AkCICsyU+GwEVSaYQJHKtAestORw76pkBNQYlOFLu6QMhOdqKUzJXAhkLP+k4SdMDNKbVWBMSiQDBeB9MaLrBA\\u002fIzETTgYlMCFj3yGSIaVwAoILed19bJA16Oqcv6TscC4hBOoLruFwDzm443DEbpAdq0e2t9MpMD5vH0\\u002fppmowCKUcB6ms41Am9lxekwepUDZrN6ObuygQHOmxIQsQq9AANS3aL3rlMBCZiXM\\u002ffCpQADi\\u002fJxtERHAHO433X5QmcDpdPoZfaSywA5iLlHkSKrACpgSxM\\u002fntsDWzM5QsUClwGkIaHOzn6tAygtbPw38kMCGNnr2aN23QNNRFBiXir1AKF4ACWWDrMA26uq+jvhzQPgW0\\u002fERFbhAB0TjhZ5PtUAbGIbRMN2wwES0+jW3TbXAbEpnBXFXuUAw8u25DPKeQFwnAendNZRAYSTLKKMmwkDWkI7DrWOiwAATq0AueprAwXWefCVCs0CQmALiVnqEwNofISTZmI\\u002fACvz4xdkMpUANOZq7VZTBwGYmWWdzH7nA7KqG6TQ+gsAAxRP\\u002figJcQCAveWlBNlFAkJ7eE2zjsEAebUa48tavwA67GZAw+qbACLZTTkaqm8DseOLKSFipwEATQJFNA0rA++o93WgNnkA8iqU+EuSjQBEB+OrJSr7ArIADtIR5n0C9x3aC\\u002fQ+iQDLqjK8ZR5dANpkXr9havUAkAA806IqaQBHui6CXaopAyCOL8q0erEBYVpq98MuFQFSNjUySsY1AtpXS2ktvokDaaDCYNUucwLrvuomJurRAvZsbt70oo0AsjwrantRyQFe6OQFFMqzA4DpeMyKyZUDwJchRlZ5zwJ3VyqOPqp5ApMZHxXNQtsBAu0aF3rWAQAk9\\u002fmMrgZNAgxFpFDhmrEBADKyJgkORQEk23FZoI7fAbCEP8VvtiMBtEkVsLQS+QLAYT9XJGYLAcIQGMHxNoMCSvldSTJevwG4SpIT7RqHAYbUdQQhZy8AjbTEZXN26QOColyMXqsnAuIMGkLfhfcC48cdYB1CtwENWMUG5MppAPl8r81YQlsBzjkBbpmqrQHUygPBFV6\\u002fAHGCPwZ5ksMBKlPtLOgOywBpBnR7purbAuEuMDbSrg8DzKUwf532iQA5iZZuguLnAAgZqUiLKmEDJH4VI\\u002fia6wDQ1W0DsfoLA+u7UGItUrUDwxlsdDGnCQP98+CimHrXAAJdVzC+kr0BcpC6MJWrAQEBDBrYYnaNAY\\u002f\\u002fXqpdSr0AYtsSd4u6zQPHcYam3sqDAvqNe7q5+scB87FbgSXOMQNuxc6Z8urdA1BsCNH0vvcA9oUW0scmbQJCoNJHbHGjAcCeyUvzTsECY2syanbCWwHvjIRdueZdAX1tU\\u002fbqDsEAI+PzymmKtQJF83t9T8ZjAsq64iRgKlcC7G0ZXlie8wLVFGcwXyahA5LZdQReimcDMZB9DCNqfQOB4M2CmgX1AgXzS8hJLuMCQddy5ZytxQMqzVCXxDrPAht1dAdeswsD0C0xgNs+hQAnWv\\u002fymgZRAAaA+NnI\\u002fs8D5ktNoBKO\\u002fwBYrtAJoUbhA4hUKRNY8xkAamlz8slKwQNbLc4VoC57A+i8facjiuMB0GMdr38G8wEpEd\\u002fvJk7JAVmtlm3cgnkDd1HecgwO2QKiSs5faYZDAu5zGRQ17tEDqIPNh4pSpQLWdDYpkIqjAcp09duoYo0AW5\\u002fnVwna8QGZ1e8yK86PATmrYJxKArsAuBLtj+7C4QPRJvtLXoabAMN7XkX\\u002f8fMCS32a18cfDQADWKkInsx1AvhTKnb8VtcDZGDtrhzOcQMIvlhFv9YNAJH8\\u002fCcS\\u002fckDCXOdJs9SUQEAvDTNYIqdA\\u002ffdRCqswkUAa1WvTxd+8wDg1783uzbPAwWVCxm+klMDK+cOE6weqwJglOv1bM1vAABNDUZ0AgUCpv7DOyE+cwFi9J+ede6fAWuOwjirPkkDRb6KKYpa5wEJ55udpcqNAg5AecB6Iw0Dm1dPIHXWzQB8TfL8b37nAqplpSwY3wUDjQfzjNvKtQGzaO+57sqJAbEjqmNWNtsCrBoCS50LCQLi0lnC4XrfA9aZmSoLnp0BozvYWjBG3QJ2Zd0lBUa1AVYXuJlo3oEBOEofrovSgwBsyOGTEfqjAAsZoOTeLucDGj+8jYsSTQPf+1DyClMHAuBediS+8r0A\\u002fVkuaGULCwGbAFr9rUJdATh6pV1dKnEDWN8n4Qx+sQPisl9eo9qXAPBNo20yUt0A1c27MjWKjwADj\\u002fc+OZoPACBSdfCOHrkBDH5Qu+pqwQOjg1sUXk6pAABVCY8ietkCpS846R32XwOYIez8scqdAjA\\u002fZqn31eUCARY2lyAg9wPR+3hLqTorAr9uZluBtpsDUhCHt8RKUwD9asLeQyrTAYAVfxoBAuUDWIGYpxf+zwE133MwPvqTA7KKNxSMcrsCU9LBArLR9wMlPPLIwu6pAflyVeiKlrsDIR0XZJOG1wJx9GWXAs6BAPufjWBBdmkAexGhluNCowC86QLf5LbtACh8jr+NbuMCbozJwQWejQJ7EDynhB7NA0i4Hmd0RkcB3Kx9PbOuuwOjKyJK3763A4FKD5ywDaUCkQgS4fqGIQOMQLoJqGp3A4CVZ0h4akkAiMKZn+QijQEBsyk1ObLpA8zbG9YUKo8AM6aI0OH2kwLA7njsZvbFAk9Vf1GpiocD4Xa21v8aFwLyJIxyxd6BA5Ea5W+oHkMDYvY7RrmyGQIZCHXbsmqTAYuSzeLE7msBwUVvnzUyBQEn+N3XHnLXAubbHhJIxkcDRdfUOT3qoQNi\\u002ft5diIppAJxEGOt\\u002fspUDwmZ5v0BhywAT3AqjUFbHAnFGFyXDsqkBton+5Q7mRwI7yWjpP4KFA6Jg+DW3It8C+SbGiKFyIQBjjrp18B6jAAujI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a88LSalAG1ZBGF3YncCUGEdLPp2lQAKcS2Zlp8BAIO+dWpSNWUCmpkPozg+WQG4+n\\u002flAx6XAWC9\\u002fJrLZisBQRacfIIpwQGwauLZEtrNAeMUaI2h3hUDcMDzp4VC5wLC78aMpOXNAzXSZ8pwOk0CK0kzT6Le8QOSFUmrcs6nALP\\u002f51c4ns0CsNNHtsbqzQBAlOllrssBAcICtiUYDhkAX0t5iX0qhwEbuYbQnirPAlciSNVvgp8CoEmJ70zWSQNoREH7usLfA6MoucDpakUDsnTwHKkqUQJpmQ4f90aXAOHrdP9gWgUBiBGzb092gwCAUTGXc\\u002fmpAiBnTpNytlEAUNges+9uvQCSX+Q6GLHrAAItTT91Ku8AZfjhlY0GUwAD6z4C8ZJRAHiudadJMtMALEjn6H6yfwH+dH2tV0bDANAXRZ6WEnUCoAjuJ8zWUwG66ibjjQJnAX1Cn04gxtcAyRdLuL06mwALzWlY8DbDAvSmz8i5rtEAYmjmN5EamQDpaWDx7wsHAaG7ZAI5susBgvXH7UKl8QJB\\u002fsY5VtbPAwD2y2ypOcECo4o6+7i2KwEjuvu+SLYhA\\u002fC\\u002f63tZth0CwpeceXNKmQGDoQXsd3LbA5B7vK3HHtkBIAotss66EwNIw6uqmG61AVh5KqXBcr8AavbMJmKauwPRC33FEfrJA0iJ+2i5tpEAkYV++YWieQLQykDISgYVASmjzzQwYxUCUoSgd7niqQPlSIaOoUrVAZsYGHsnNmEDPj4eD8pO+wEa9JTIcrKdAUNNhZ1lQgkCc2RMLy26qQMuRQYESz7ZAKaXQMkdbwcA4E2\\u002fbzY+8QBtCms3nGKBAF3tvHc9Fg0DOP43J4uTBQCjWYIvyHZ\\u002fAOFDV7CiqrsDDIEDvcvqgQA8eU1u7S69ApSG66jZXsUAfk0hCE8KqQKARjSQqhKNAKvRBJZ0sh8BFpeioLgWkwJw8BhDItZbAgdZmuXozokBft6KNH8SNQH8VDgljuZBAZZnznffvlMAq+GA+3ySVQORH1jdP66dA9v7FPcWRksAhKFp153G+wI5NtgLewrlA0l\\u002fbVvyevcC+N6rRLUGSwIrdv\\u002fBmnbZArmccw+aSksDwueD0MMtvwPRGn35T2XHAECOhLHiEssBErlm5PXKaQFxwvdppZInAiKIA3FIpkcCO6MVC1GicQHsiIRPrDrzA9lacmmmMp8Dixtqjr+KnQMy1AF4+4JJAklLc67hPvEBYTamSEGC0QO7AQV8PEpbAbAuuVdn4uMDCjmGGHjekQLQxZJFApKBAqIG+EUhXnEBwUqzQR528QBCkWaafnsXAKK7XMGxyu0DYrqOIUUi2QMmTbv16KKXAVjEIoYnZrkB4\\u002fJJ1wsa8wJAWcH+QBqVA9QI85ljIskBJjX6VOYehQLQof1+41aVAXZZ3F9cstMBpJX6j9mmswMoanm8OkKTAWsEYFPHsmUCSnb+BNAaHwCKgkhj\\u002feaVAtItY2kSAfMAPslXp6Z2zQIaIdpWba7BA4fV8h2n+o8A+X8lZH2y1QKAPWgRUHFZAMOpQs6oRacCQxcZfHgl8QJIg6fRFkZ3AEgyVaOXNp8AI+iT2kNyzwBKRrVZHHKxARO5Q5YmhlMAmYHhi9n7DwHoOIFjurpJAGCwZenWZcsCmOJIUhxaywAWIrcto4sFAKFYP1blNgcCchcUOJX2fwNaXSqOnxpHAdtCC8f+QmMAovLZe5AizwGT8sh5PiqfAkPhSqaK0tcCfY61bxLa2wMx8nm4N4ppAcC7PaLu8fkABg5LcnVu5QBBKYWLed2lAHQUqP91sosAIHj8o76qwwC4iOP2cdbhAYOY9LHxje8AkCICsyU+GwEVSaYQJHKtAestORw76pkBNQYlOFLu6QMhOdqKUzJXAhkLP+k4SdMDNKbVWBMSiQDBeB9MaLrBA\\u002fIzETTgYlMCFj3yGSIaVwAoILed19bJA16Oqcv6TscC4hBOoLruFwDzm443DEbpAdq0e2t9MpMD5vH0\\u002fppmowCKUcB6ms41Am9lxekwepUDZrN6ObuygQHOmxIQsQq9AANS3aL3rlMBCZiXM\\u002ffCpQADi\\u002fJxtERHAHO433X5QmcDpdPoZfaSywA5iLlHkSKrACpgSxM\\u002fntsDWzM5QsUClwGkIaHOzn6tAygtbPw38kMCGNnr2aN23QNNRFBiXir1AKF4ACWWDrMA26uq+jvhzQPgW0\\u002fERFbhAB0TjhZ5PtUAbGIbRMN2wwES0+jW3TbXAbEpnBXFXuUAw8u25DPKeQFwnAendNZRAYSTLKKMmwkDWkI7DrWOiwAATq0AueprAwXWefCVCs0CQmALiVnqEwNofISTZmI\\u002fACvz4xdkMpUANOZq7VZTBwGYmWWdzH7nA7KqG6TQ+gsAAxRP\\u002figJcQCAveWlBNlFAkJ7eE2zjsEAebUa48tavwA67GZAw+qbACLZTTkaqm8DseOLKSFipwEATQJFNA0rA++o93WgNnkA8iqU+EuSjQBEB+OrJSr7ArIADtIR5n0C9x3aC\\u002fQ+iQDLqjK8ZR5dANpkXr9havUAkAA806IqaQBHui6CXaopAyCOL8q0erEBYVpq98MuFQFSNjUySsY1AtpXS2ktvokDaaDCYNUucwLrvuomJurRAvZsbt70oo0AsjwrantRyQFe6OQFFMqzA4DpeMyKyZUDwJchRlZ5zwJ3VyqOPqp5ApMZHxXNQtsBAu0aF3rWAQAk9\\u002fmMrgZNAgxFpFDhmrEBADKyJgkORQEk23FZoI7fAbCEP8VvtiMBtEkVsLQS+QLAYT9XJGYLAcIQGMHxNoMCSvldSTJevwG4SpIT7RqHAYbUdQQhZy8AjbTEZXN26QOColyMXqsnAuIMGkLfhfcC48cdYB1CtwENWMUG5MppAPl8r81YQlsBzjkBbpmqrQHUygPBFV6\\u002fAHGCPwZ5ksMBKlPtLOgOywBpBnR7purbAuEuMDbSrg8DzKUwf532iQA5iZZuguLnAAgZqUiLKmEDJH4VI\\u002fia6wDQ1W0DsfoLA+u7UGItUrUDwxlsdDGnCQP98+CimHrXAAJdVzC+kr0BcpC6MJWrAQEBDBrYYnaNAY\\u002f\\u002fXqpdSr0AYtsSd4u6zQPHcYam3sqDAvqNe7q5+scB87FbgSXOMQNuxc6Z8urdA1BsCNH0vvcA9oUW0scmbQJCoNJHbHGjAcCeyUvzTsECY2syanbCWwHvjIRdueZdAX1tU\\u002fbqDsEAI+PzymmKtQJF83t9T8ZjAsq64iRgKlcC7G0ZXlie8wLVFGcwXyahA5LZdQReimcDMZB9DCNqfQOB4M2CmgX1AgXzS8hJLuMCQddy5ZytxQMqzVCXxDrPAht1dAdeswsD0C0xgNs+hQAnWv\\u002fymgZRAAaA+NnI\\u002fs8D5ktNoBKO\\u002fwBYrtAJoUbhA4hUKRNY8xkAamlz8slKwQNbLc4VoC57A+i8facjiuMB0GMdr38G8wEpEd\\u002fvJk7JAVmtlm3cgnkDd1HecgwO2QKiSs5faYZDAu5zGRQ17tEDqIPNh4pSpQLWdDYpkIqjAcp09duoYo0AW5\\u002fnVwna8QGZ1e8yK86PATmrYJxKArsAuBLtj+7C4QPRJvtLXoabAMN7XkX\\u002f8fMCS32a18cfDQADWKkInsx1AvhTKnb8VtcDZGDtrhzOcQMIvlhFv9YNAJH8\\u002fCcS\\u002fckDCXOdJs9SUQEAvDTNYIqdA\\u002ffdRCqswkUAa1WvTxd+8wDg1783uzbPAwWVCxm+klMDK+cOE6weqwJglOv1bM1vAABNDUZ0AgUCpv7DOyE+cwFi9J+ede6fAWuOwjirPkkDRb6KKYpa5wEJ55udpcqNAg5AecB6Iw0Dm1dPIHXWzQB8TfL8b37nAqplpSwY3wUDjQfzjNvKtQGzaO+57sqJAbEjqmNWNtsCrBoCS50LCQLi0lnC4XrfA9aZmSoLnp0BozvYWjBG3QJ2Zd0lBUa1AVYXuJlo3oEBOEofrovSgwBsyOGTEfqjAAsZoOTeLucDGj+8jYsSTQPf+1DyClMHAuBediS+8r0A\\u002fVkuaGULCwGbAFr9rUJdATh6pV1dKnEDWN8n4Qx+sQPisl9eo9qXAPBNo20yUt0A1c27MjWKjwADj\\u002fc+OZoPACBSdfCOHrkBDH5Qu+pqwQOjg1sUXk6pAABVCY8ietkCpS846R32XwOYIez8scqdAjA\\u002fZqn31eUCARY2lyAg9wPR+3hLqTorAr9uZluBtpsDUhCHt8RKUwD9asLeQyrTAYAVfxoBAuUDWIGYpxf+zwE133MwPvqTA7KKNxSMcrsCU9LBArLR9wMlPPLIwu6pAflyVeiKlrsDIR0XZJOG1wJx9GWXAs6BAPufjWBBdmkAexGhluNCowC86QLf5LbtACh8jr+NbuMCbozJwQWejQJ7EDynhB7NA0i4Hmd0RkcB3Kx9PbOuuwOjKyJK3763A4FKD5ywDaUCkQgS4fqGIQOMQLoJqGp3A4CVZ0h4akkAiMKZn+QijQEBsyk1ObLpA8zbG9YUKo8AM6aI0OH2kwLA7njsZvbFAk9Vf1GpiocD4Xa21v8aFwLyJIxyxd6BA5Ea5W+oHkMDYvY7RrmyGQIZCHXbsmqTAYuSzeLE7msBwUVvnzUyBQEn+N3XHnLXAubbHhJIxkcDRdfUOT3qoQNi\\u002ft5diIppAJxEGOt\\u002fspUDwmZ5v0BhywAT3AqjUFbHAnFGFyXDsqkBton+5Q7mRwI7yWjpP4KFA6Jg+DW3It8C+SbGiKFyIQBjjrp18B6jAAujIQ0oQs0BgfH1O5KZXwDt2hxK\\u002fu6JAhokjKsKntcC8w+e8mgqtwCDbMEGZ54JArTNVac+QqMBAs6xioBOKwDenaH8HyKlANsvQi6zDnUASPusPrdumQDRBMhy2759AvnNBwrljocDuf\\u002f7c6IOLwH\\u002fq7QDTiYtAoGa1\\u002fXiLjUBgumNzZp+PQDUHvs2QW8FATJcAHGAZwkACV+WZ1cuzQAIzy1+mUqLAE\\u002fUl+bJWrsAApxKkaYtuwNsNnrQpHrdAolmhIojLr8DA0zvCZ6NRwIj+zt5bf4XAacnQmDcJrUA0a\\u002fRFTQp6wLWZJs3FYLrAij5mfQlzpEA8C8ksvbmPwBjY4Xfv\\u002fHzAzvG2oM3xoMAEPCwLYOWwQByLPlN9nrZAk7ROGbDKu0A0zhp5KWyxQP8BnvOQn7LAJhDSXc68psA=\"},\"xaxis\":\"x3\",\"yaxis\":\"y3\",\"type\":\"histogram\"}],                        {\"annotations\":[{\"font\":{\"size\":16},\"showarrow\":false,\"text\":\"intercept\",\"x\":0.14444444444444446,\"xanchor\":\"center\",\"xref\":\"paper\",\"y\":1.0,\"yanchor\":\"bottom\",\"yref\":\"paper\"},{\"font\":{\"size\":16},\"showarrow\":false,\"text\":\"living_area\",\"x\":0.5,\"xanchor\":\"center\",\"xref\":\"paper\",\"y\":1.0,\"yanchor\":\"bottom\",\"yref\":\"paper\"},{\"font\":{\"size\":16},\"showarrow\":false,\"text\":\"bedrooms\",\"x\":0.8555555555555556,\"xanchor\":\"center\",\"xref\":\"paper\",\"y\":1.0,\"yanchor\":\"bottom\",\"yref\":\"paper\"}],\"bargap\":0.02,\"showlegend\":false,\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"white\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"white\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"#C8D4E3\",\"linecolor\":\"#C8D4E3\",\"minorgridcolor\":\"#C8D4E3\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"#C8D4E3\",\"linecolor\":\"#C8D4E3\",\"minorgridcolor\":\"#C8D4E3\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scattermap\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermap\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777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Null Distribution\"},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,0.2888888888888889]},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0]},\"xaxis2\":{\"anchor\":\"y2\",\"domain\":[0.35555555555555557,0.6444444444444445]},\"yaxis2\":{\"anchor\":\"x2\",\"domain\":[0.0,1.0]},\"xaxis3\":{\"anchor\":\"y3\",\"domain\":[0.7111111111111111,1.0]},\"yaxis3\":{\"anchor\":\"x3\",\"domain\":[0.0,1.0]},\"shapes\":[{\"line\":{\"color\":\"#d62728\",\"dash\":\"solid\",\"width\":2},\"type\":\"line\",\"x0\":-7483.095465602403,\"x1\":-7483.095465602403,\"xref\":\"x3\",\"y0\":0,\"y1\":1,\"yref\":\"y3 domain\"},{\"line\":{\"color\":\"#d62728\",\"dash\":\"dash\",\"width\":2},\"type\":\"line\",\"x0\":7483.095465602403,\"x1\":7483.095465602403,\"xref\":\"x3\",\"y0\":0,\"y1\":1,\"yref\":\"y3 domain\"},{\"fillcolor\":\"#d62728\",\"line\":{\"width\":0},\"opacity\":0.3,\"type\":\"rect\",\"x0\":-15369.090579565614,\"x1\":-7483.095465602403,\"xref\":\"x3\",\"y0\":0,\"y1\":1,\"yref\":\"y3 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       ],
       "text/plain": [
        "Figure({\n",
@@ -491,16 +491,16 @@
     "from moderndive import shade_confidence_interval, shade_p_value\n",
     "\n",
     "f = \"price ~ living_area + bedrooms\"\n",
-    "obs_fit = specify(houses, formula=f).fit()\n",
+    "obs_fit = houses.specify(formula=f).fit()\n",
     "\n",
     "# Bootstrap distribution of each coefficient → per-term CIs\n",
-    "boot_fit = specify(houses, formula=f).generate(reps=1000, type=\"bootstrap\", seed=1).fit()\n",
+    "boot_fit = houses.specify(formula=f).generate(reps=1000, type=\"bootstrap\", seed=1).fit()\n",
     "boot_fit.get_confidence_interval(level=0.95)          # one row per term\n",
     "visualize_fit(boot_fit) + shade_confidence_interval(boot_fit.get_confidence_interval())\n",
     "\n",
     "# Null distribution → per-term p-values, each facet shaded at its own estimate\n",
     "null_fit = (\n",
-    "    specify(houses, formula=f).hypothesize(null=\"independence\")\n",
+    "    houses.specify(formula=f).hypothesize(null=\"independence\")\n",
     "    .generate(reps=1000, type=\"permute\", seed=1).fit()\n",
     ")\n",
     "null_fit.get_p_value(obs_stat=obs_fit, direction=\"two-sided\")\n",
@@ -509,7 +509,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "492b5252",
+   "id": "deaa4248",
    "metadata": {},
    "source": [
     "Per-facet shading works in both engines: `shade_p_value` / `shade_confidence_interval`\n",
diff --git a/doc/_build/jupyter_execute/guides/sampling.ipynb b/doc/_build/jupyter_execute/guides/sampling.ipynb
index cb1b7af..24fc7ad 100644
--- a/doc/_build/jupyter_execute/guides/sampling.ipynb
+++ b/doc/_build/jupyter_execute/guides/sampling.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "9171bec6",
+   "id": "0db12f0d",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b88a9e8",
+   "id": "75743494",
    "metadata": {},
    "source": [
     "# Sampling\n",
@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "5ec986a1",
+   "id": "778be711",
    "metadata": {},
    "outputs": [
     {
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9bcee499",
+   "id": "823a2021",
    "metadata": {},
    "source": [
     "## One virtual sample"
@@ -89,7 +89,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "af464f6b",
+   "id": "8e879ec1",
    "metadata": {},
    "outputs": [
     {
@@ -130,7 +130,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "892a49af",
+   "id": "c9bca22a",
    "metadata": {},
    "source": [
     "## Many samples → a sampling distribution\n",
@@ -141,7 +141,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "0caf812e",
+   "id": "7b87f1de",
    "metadata": {},
    "outputs": [
     {
@@ -189,7 +189,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "501cc21f",
+   "id": "370e9f26",
    "metadata": {},
    "source": [
     "That `prop_red` column is a **sampling distribution**. Visualize its spread the\n",
@@ -205,7 +205,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "eb5bde46",
+   "id": "4e7c65c0",
    "metadata": {},
    "outputs": [
     {
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a3bbfd8f",
+   "id": "245dcc87",
    "metadata": {},
    "source": [
     "## Tactile samples\n",
@@ -263,7 +263,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "ca9878c7",
+   "id": "daa58281",
    "metadata": {},
    "outputs": [
     {
@@ -310,7 +310,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4b465f7f",
+   "id": "9932aac9",
    "metadata": {},
    "source": [
     "```{seealso}\n",
diff --git a/doc/_build/jupyter_execute/guides/theory-based.ipynb b/doc/_build/jupyter_execute/guides/theory-based.ipynb
index 7bc53ea..59b7e33 100644
--- a/doc/_build/jupyter_execute/guides/theory-based.ipynb
+++ b/doc/_build/jupyter_execute/guides/theory-based.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "0f0eb6cc",
+   "id": "4f556925",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7498133c",
+   "id": "e5cadb0e",
    "metadata": {},
    "source": [
     "# Theory-based inference\n",
@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "09b25eea",
+   "id": "6dc70ab2",
    "metadata": {},
    "outputs": [
     {
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "acdd22fe",
+   "id": "38373ddd",
    "metadata": {},
    "source": [
     "`t_stat` and `chisq_stat` return just the test statistic if that's all you need.\n",
@@ -103,14 +103,14 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "640c9c2a",
+   "id": "cb7f728a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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       "text/plain": [
-       "<plotnine.ggplot.ggplot object at 0x13b462060>"
+       "<plotnine.ggplot.ggplot object at 0x139d1a060>"
       ]
      },
      "execution_count": 3,
@@ -137,7 +137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0739ef7e",
+   "id": "10106896",
    "metadata": {},
    "source": [
     "Supported distributions: `\"t\"`, `\"z\"`, `\"F\"` (pass `df=(df1, df2)`), and\n",
@@ -152,7 +152,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "9dd4a203",
+   "id": "52d705d4",
    "metadata": {},
    "outputs": [
     {
@@ -160,7 +160,7 @@
       "image/png": 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",
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{\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"white\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"white\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"#C8D4E3\",\"linecolor\":\"#C8D4E3\",\"minorgridcolor\":\"#C8D4E3\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"#C8D4E3\",\"linecolor\":\"#C8D4E3\",\"minorgridcolor\":\"#C8D4E3\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scattermap\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermap\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"white\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"#C8D4E3\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"white\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\"},\"bgcolor\":\"white\",\"radialaxis\":{\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"},\"yaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"},\"zaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"},\"bgcolor\":\"white\",\"caxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#EBF0F8\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#EBF0F8\",\"zerolinewidth\":2}}},\"title\":{\"text\":\"Theoretical Distribution\"},\"xaxis\":{\"title\":{\"text\":\"mean\"}},\"yaxis\":{\"title\":{\"text\":\"density\"}},\"bargap\":0.02,\"showlegend\":false},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -189,7 +189,7 @@
     "from moderndive import specify, visualize\n",
     "\n",
     "boot = (\n",
-    "    specify(age, response=\"age\")\n",
+    "    age.specify(response=\"age\")\n",
     "    .generate(reps=1000, type=\"bootstrap\", seed=1)\n",
     "    .calculate(stat=\"mean\")\n",
     ")\n",
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5086879e",
+   "id": "cfce35e8",
    "metadata": {},
    "source": [
     "## Population standard deviation\n",
@@ -211,7 +211,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "426c4e7f",
+   "id": "aa38fce9",
    "metadata": {},
    "outputs": [
     {
diff --git a/doc/_build/jupyter_execute/index.ipynb b/doc/_build/jupyter_execute/index.ipynb
index 14b798c..ee2a11e 100644
--- a/doc/_build/jupyter_execute/index.ipynb
+++ b/doc/_build/jupyter_execute/index.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "d3eb497d",
+   "id": "f2ced15f",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a93c1238",
+   "id": "d344f744",
    "metadata": {},
    "source": [
     "# moderndive (Python)\n",
@@ -67,7 +67,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "5012fbc3",
+   "id": "4f43cb39",
    "metadata": {},
    "outputs": [
     {
@@ -98,7 +98,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "c297fe46",
+   "id": "7c52a3b0",
    "metadata": {},
    "outputs": [
     {
@@ -132,7 +132,7 @@
    "source": [
     "# Null distribution under \"genre doesn't matter\" (permutation)\n",
     "null = (\n",
-    "    specify(spotify, formula=\"popular_or_not ~ track_genre\", success=\"popular\")\n",
+    "    spotify.specify(formula=\"popular_or_not ~ track_genre\", success=\"popular\")\n",
     "    .hypothesize(null=\"independence\")\n",
     "    .generate(reps=1000, type=\"permute\", seed=76)\n",
     "    .calculate(stat=\"diff in props\", order=(\"metal\", \"deep-house\"))\n",
@@ -143,7 +143,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "29571551",
+   "id": "51622caa",
    "metadata": {},
    "outputs": [
     {
@@ -151,7 +151,7 @@
       "image/png": 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Null Distribution\"},\"xaxis\":{\"title\":{\"text\":\"diff in props\"}},\"yaxis\":{\"title\":{\"text\":\"count\"}},\"shapes\":[{\"line\":{\"color\":\"#d62728\",\"dash\":\"solid\",\"width\":2},\"type\":\"line\",\"x0\":0.03399999999999992,\"x1\":0.03399999999999992,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"},{\"fillcolor\":\"#d62728\",\"line\":{\"width\":0},\"opacity\":0.3,\"type\":\"rect\",\"x0\":0.03399999999999992,\"x1\":0.07509999999999994,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"}]},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -199,9 +199,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8f740d70",
+   "id": "61bfc1e3",
    "metadata": {},
    "source": [
+    "```{note}\n",
+    "Plots throughout this documentation are rendered as **static images**. When you\n",
+    "run the code yourself, the default `engine=\"plotly\"` produces **interactive**\n",
+    "figures (hover, zoom, pan); `engine=\"plotnine\"` gives static grammar-of-graphics\n",
+    "plots.\n",
+    "```\n",
+    "\n",
     "## Where to next\n",
     "\n",
     "- New here? Start with {doc}`getting-started`.\n",
diff --git a/doc/coming-from-r.md b/doc/coming-from-r.md
index 6f3b032..94ad9aa 100644
--- a/doc/coming-from-r.md
+++ b/doc/coming-from-r.md
@@ -18,7 +18,7 @@ pennies %>%
 ```python
 # Python — verbs are methods on the returned objects
 (
-    specify(md.load_pennies(), response="year")
+    md.load_pennies().specify(response="year")
     .hypothesize(null="point", mu=1995)
     .generate(reps=1000, type="bootstrap", seed=1)
     .calculate(stat="mean")
diff --git a/doc/getting-started.md b/doc/getting-started.md
index e8c9670..09b43da 100644
--- a/doc/getting-started.md
+++ b/doc/getting-started.md
@@ -72,7 +72,7 @@ obs = observe(
 
 # 2. A null distribution: specify → hypothesize → generate → calculate
 null = (
-    specify(yawn, formula="yawn ~ group", success="yes")
+    yawn.specify(formula="yawn ~ group", success="yes")
     .hypothesize(null="independence")
     .generate(reps=1000, type="permute", seed=42)
     .calculate(stat="diff in props", order=("seed", "control"))
@@ -90,6 +90,11 @@ Each verb has a focused guide: {doc}`guides/sampling`,
 Plots default to **plotly** (interactive). Pass `engine="plotnine"` for
 grammar-of-graphics output. The composition syntax is identical:
 
+```{note}
+The plots shown in this documentation are **static images**. Running the code
+yourself yields **interactive** plotly figures by default.
+```
+
 ```{code-cell} python
 from moderndive import visualize, shade_p_value
 
diff --git a/doc/guides/confidence-intervals.md b/doc/guides/confidence-intervals.md
index b507a51..f6b279d 100644
--- a/doc/guides/confidence-intervals.md
+++ b/doc/guides/confidence-intervals.md
@@ -30,7 +30,7 @@ from moderndive import specify, get_confidence_interval, visualize, shade_confid
 almonds = md.load_almonds_sample_100()
 
 boot = (
-    specify(almonds, response="weight")
+    almonds.specify(response="weight")
     .generate(reps=1000, type="bootstrap", seed=1)
     .calculate(stat="mean")
 )
@@ -44,7 +44,7 @@ get_confidence_interval(boot, level=0.95, type="percentile")
 # shape: (1, 2) → lower_ci ≈ 3.61, upper_ci ≈ 3.75
 
 # Standard-error method (needs the point estimate)
-point = float(specify(almonds, response="weight").calculate(stat="mean"))
+point = float(almonds.specify(response="weight").calculate(stat="mean"))
 get_confidence_interval(boot, level=0.95, type="se", point_estimate=point)
 
 # Bias-corrected
@@ -80,7 +80,7 @@ import polars as pl
 mythbusters = md.load_mythbusters_yawn()
 
 boot_prop = (
-    specify(mythbusters, response="yawn", success="yes")
+    mythbusters.specify(response="yawn", success="yes")
     .generate(reps=1000, type="bootstrap", seed=1)
     .calculate(stat="prop")
 )
@@ -93,7 +93,7 @@ Add an explanatory variable and an `order=` to compare two groups:
 
 ```{code-cell} python
 boot_diff = (
-    specify(mythbusters, formula="yawn ~ group", success="yes")
+    mythbusters.specify(formula="yawn ~ group", success="yes")
     .generate(reps=1000, type="bootstrap", seed=1)
     .calculate(stat="diff in props", order=("seed", "control"))
 )
diff --git a/doc/guides/hypothesis-testing.md b/doc/guides/hypothesis-testing.md
index a09c22f..e4c3302 100644
--- a/doc/guides/hypothesis-testing.md
+++ b/doc/guides/hypothesis-testing.md
@@ -41,7 +41,7 @@ obs = observe(
 
 # Null: genre is independent of popularity → permute the labels
 null = (
-    specify(spotify, formula="popular_or_not ~ track_genre", success="popular")
+    spotify.specify(formula="popular_or_not ~ track_genre", success="popular")
     .hypothesize(null="independence")
     .generate(reps=1000, type="permute", seed=76)
     .calculate(stat="diff in props", order=("metal", "deep-house"))
@@ -69,7 +69,7 @@ age = md.load_age_at_marriage()
 obs_t = observe(age, response="age", stat="t", null="point", mu=23)
 
 null_t = (
-    specify(age, response="age")
+    age.specify(response="age")
     .hypothesize(null="point", mu=23)
     .generate(reps=1000, type="bootstrap", seed=1)
     .calculate(stat="t")
@@ -85,7 +85,7 @@ import polars as pl
 coins = pl.DataFrame({"flip": ["heads"] * 30 + ["tails"] * 70})
 
 null_p = (
-    specify(coins, response="flip", success="heads")
+    coins.specify(response="flip", success="heads")
     .hypothesize(null="point", p=0.5)
     .generate(reps=1000, type="draw", seed=1)   # "simulate" is an alias
     .calculate(stat="prop")
diff --git a/doc/guides/plotting.md b/doc/guides/plotting.md
index 2492c09..08a1c1c 100644
--- a/doc/guides/plotting.md
+++ b/doc/guides/plotting.md
@@ -24,8 +24,13 @@ Every plotting function in `moderndive` takes an `engine=` argument:
 - `engine="plotnine"` — grammar-of-graphics figures (`plotnine.ggplot`).
 
 The composition syntax is identical across engines, so you can switch a whole
-analysis by changing one argument. (In this documentation, plotly figures are
-shown as static images; in a notebook they're fully interactive.)
+analysis by changing one argument.
+
+```{note}
+The figures on this page are **static images** so they render in the docs. In a
+notebook or script, the default `engine="plotly"` produces **interactive**
+figures (hover, zoom, pan).
+```
 
 ```{code-cell} python
 import moderndive as md
@@ -35,7 +40,7 @@ from moderndive import (
 )
 
 boot = (
-    specify(md.load_almonds_sample_100(), response="weight")
+    md.load_almonds_sample_100().specify(response="weight")
     .generate(reps=1000, type="bootstrap", seed=1)
     .calculate(stat="mean")
 )
@@ -73,7 +78,7 @@ spotify = md.load_spotify_metal_deephouse()
 obs = observe(spotify, formula="popular_or_not ~ track_genre", success="popular",
               stat="diff in props", order=("metal", "deep-house"))
 null = (
-    specify(spotify, formula="popular_or_not ~ track_genre", success="popular")
+    spotify.specify(formula="popular_or_not ~ track_genre", success="popular")
     .hypothesize(null="independence")
     .generate(reps=1000, type="permute", seed=76)
     .calculate(stat="diff in props", order=("metal", "deep-house"))
@@ -100,7 +105,7 @@ from moderndive.infer.viz import visualize_fit
 
 houses = md.load_saratoga_houses()
 boot_fit = (
-    specify(houses, formula="price ~ living_area + bedrooms")
+    houses.specify(formula="price ~ living_area + bedrooms")
     .generate(reps=1000, type="bootstrap", seed=1)
     .fit()
 )
diff --git a/doc/guides/regression.md b/doc/guides/regression.md
index 3971971..2d40648 100644
--- a/doc/guides/regression.md
+++ b/doc/guides/regression.md
@@ -98,16 +98,16 @@ from moderndive.infer.viz import visualize_fit
 from moderndive import shade_confidence_interval, shade_p_value
 
 f = "price ~ living_area + bedrooms"
-obs_fit = specify(houses, formula=f).fit()
+obs_fit = houses.specify(formula=f).fit()
 
 # Bootstrap distribution of each coefficient → per-term CIs
-boot_fit = specify(houses, formula=f).generate(reps=1000, type="bootstrap", seed=1).fit()
+boot_fit = houses.specify(formula=f).generate(reps=1000, type="bootstrap", seed=1).fit()
 boot_fit.get_confidence_interval(level=0.95)          # one row per term
 visualize_fit(boot_fit) + shade_confidence_interval(boot_fit.get_confidence_interval())
 
 # Null distribution → per-term p-values, each facet shaded at its own estimate
 null_fit = (
-    specify(houses, formula=f).hypothesize(null="independence")
+    houses.specify(formula=f).hypothesize(null="independence")
     .generate(reps=1000, type="permute", seed=1).fit()
 )
 null_fit.get_p_value(obs_stat=obs_fit, direction="two-sided")
diff --git a/doc/guides/theory-based.md b/doc/guides/theory-based.md
index 51f6247..1318c71 100644
--- a/doc/guides/theory-based.md
+++ b/doc/guides/theory-based.md
@@ -77,7 +77,7 @@ curve, or both:
 from moderndive import specify, visualize
 
 boot = (
-    specify(age, response="age")
+    age.specify(response="age")
     .generate(reps=1000, type="bootstrap", seed=1)
     .calculate(stat="mean")
 )
diff --git a/doc/index.md b/doc/index.md
index dda2806..fdde1ba 100644
--- a/doc/index.md
+++ b/doc/index.md
@@ -74,7 +74,7 @@ obs
 ```{code-cell} python
 # Null distribution under "genre doesn't matter" (permutation)
 null = (
-    specify(spotify, formula="popular_or_not ~ track_genre", success="popular")
+    spotify.specify(formula="popular_or_not ~ track_genre", success="popular")
     .hypothesize(null="independence")
     .generate(reps=1000, type="permute", seed=76)
     .calculate(stat="diff in props", order=("metal", "deep-house"))
@@ -87,6 +87,13 @@ get_p_value(null, obs_stat=obs, direction="right")
 visualize(null) + shade_p_value(obs_stat=obs, direction="right")
 ```
 
+```{note}
+Plots throughout this documentation are rendered as **static images**. When you
+run the code yourself, the default `engine="plotly"` produces **interactive**
+figures (hover, zoom, pan); `engine="plotnine"` gives static grammar-of-graphics
+plots.
+```
+
 ## Where to next
 
 - New here? Start with {doc}`getting-started`.

From a795c6b9ecc0a8d07e16809dc18da5e107f1c517 Mon Sep 17 00:00:00 2001
From: Chester Ismay <chester.ismay@gmail.com>
Date: Fri, 19 Jun 2026 08:42:55 -0700
Subject: [PATCH 8/9] docs: rebuild HTML after merging main (#10)

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017CTL1QSTg1DmDUpqYuPEog
---
 doc/_build/html/.doctrees/api.doctree         | Bin 3766775 -> 3766775 bytes
 doc/_build/html/.doctrees/environment.pickle  | Bin 3509556 -> 3509556 bytes
 .../html/.doctrees/getting-started.doctree    | Bin 30895 -> 30895 bytes
 .../guides/confidence-intervals.doctree       | Bin 63504 -> 63504 bytes
 .../guides/hypothesis-testing.doctree         | Bin 70796 -> 70796 bytes
 .../html/.doctrees/guides/plotting.doctree    | Bin 356967 -> 356967 bytes
 .../html/.doctrees/guides/regression.doctree  | Bin 140608 -> 140608 bytes
 .../.doctrees/guides/theory-based.doctree     | Bin 53329 -> 53329 bytes
 doc/_build/html/.doctrees/index.doctree       | Bin 68593 -> 68593 bytes
 doc/_build/jupyter_execute/datasets.ipynb     |   8 +--
 .../jupyter_execute/getting-started.ipynb     |  26 ++++-----
 .../guides/confidence-intervals.ipynb         |  30 +++++-----
 .../guides/hypothesis-testing.ipynb           |  26 ++++-----
 .../jupyter_execute/guides/plotting.ipynb     |  54 +++++++++---------
 .../jupyter_execute/guides/regression.ipynb   |  34 +++++------
 .../jupyter_execute/guides/sampling.ipynb     |  24 ++++----
 .../jupyter_execute/guides/theory-based.ipynb |  22 +++----
 doc/_build/jupyter_execute/index.ipynb        |  14 ++---
 18 files changed, 119 insertions(+), 119 deletions(-)

diff --git a/doc/_build/html/.doctrees/api.doctree b/doc/_build/html/.doctrees/api.doctree
index d02ee130f370020e8b923aaea1947425085e58fa..d698c72554d1d5b74db1317e451d7763f0589e48 100644
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diff --git a/doc/_build/html/.doctrees/getting-started.doctree b/doc/_build/html/.doctrees/getting-started.doctree
index 225d158c0555125a2986d6ab39dd31f75e4f924b..ccf9438bacf06ef24ab142a1490d43cbd624f9c9 100644
GIT binary patch
delta 28
gcmZ4gk#YS;#trfQEN12@rjwKXjlqn~v;0Lh0i>A<2LJ#7

delta 28
gcmZ4gk#YS;#trfQEXGMGhLe;1jlqn~v;0Lh0jMVmPXGV_

diff --git a/doc/_build/html/.doctrees/guides/confidence-intervals.doctree b/doc/_build/html/.doctrees/guides/confidence-intervals.doctree
index 2010178c24c8e6c800c25ea7f3e16f80bacf2101..769452c2a68890ef3dd5fca3092ea51d206cbccf 100644
GIT binary patch
delta 280
zcmbR6fqB9Q<_*)$RLsoGER0PojC4)XjLmgTER$1oEt66WbyLmF%*{<LEX_=kEhq0b
rJFQ8KLM2U2h3d%*HAE)gYt|sj=*|2Vi@k_aNn1w*dTy>hJ5>t+x`s`s

delta 280
zcmbR6fqB9Q<_*)$R7@-^(o79Zl5~?z4HI=u64MNHEiBDUb<@lZ4UCK}Q_RfLOegO)
rJFQ8KLM2U2h3d%*HAE)gYt|sj=*|2Vi@k_aNn1w*dTy>hJ5>t+qqI*I

diff --git a/doc/_build/html/.doctrees/guides/hypothesis-testing.doctree b/doc/_build/html/.doctrees/guides/hypothesis-testing.doctree
index 679dc3e41fbb84150d3315dd0206dd2f919e6a04..0faac88db91f58e28b07b203d30248d7d7f47995 100644
GIT binary patch
delta 280
zcmeBK$<niuWy1+|mDI#UlSDHU3ta<qvou{3W8);<q%=bdT?-Q<lVs!6v{Yk*w8?MO
rPiqpRP)So$p?b2TjL78qRT@MYz1cz2IDjaXv~|Qozs+Y(9`FJH(%@3E

delta 280
zcmeBK$<niuWy1+|6+<%vOLI#LOI_nsLvvk|#56-)%Oq1{UE^f)6iXAsBvVU^<jHT;
rPiqpRP)So$p?b2TjL78qRT@MYz1cz2IDjaXv~|Qozs+Y(9`FJH=lD&u

diff --git a/doc/_build/html/.doctrees/guides/plotting.doctree b/doc/_build/html/.doctrees/guides/plotting.doctree
index 13c7512381fde3713d38d4b20cbdde9c9f3150f7..dcf49996218c240c9b1ddb104fa89fa25157a6ac 100644
GIT binary patch
delta 1624
zcmc(fJ!@1!6o#2u0&0lZB!xkv?sjruKF)kx5H$%bhJasf8t;5{7XE{j30Me$+)mq|
zh$~y%Kaf;=Es{T=LLhByt-O2l1Ds~rYMSSqdFSPQ=i+?%;(U2@r20Wy6Oc<lQ5Zmn
z8cJamNR3ILR?)laBa&v20kZ1%KHcd5e<Q2D59yuymL|A0ez*IY%v;9et?^-F7kWyh
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zk{yyYG&>oKZw7`NV_49jEw(W!3I*0_1}R3xt=Gu}SA9Y<$s^mGtmlzkP(~G5d6~!0
zQp54{dd<??OM~P+I^R~sM4kXkj8Mu{w53s2A;Tnty>cUq?A*v9>!}+XbG?4@^x%DS
zqh6iRlba8WMCPQQzTOvroGWma7r+cl$_E)zb-71><ZwUt>1Gc1ZAjPZ-R<7$+xo+^
z-VVu`nVE*K1I9{La2G&mrJ?;V9aw=3tTstV6Wh*&>d7H#`Z-`1gshcaE!f{)_%e41
O*FSfOc=T%F;r)Lt*ULBn

delta 1643
zcmc(fze^lJ9L3pPgd}1}qeUbbbK8Y4%<mr{5)%-Q15MJV$;{47mm>dxZ6HP9!0lqG
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M*pH=mQ!f|q0ANweBme*a

diff --git a/doc/_build/html/.doctrees/guides/regression.doctree b/doc/_build/html/.doctrees/guides/regression.doctree
index 47c0a5a59ef501999bc335d54a89d376c33b0013..5e28d0e4c9e6d48455388bda4ff1982f15d31389 100644
GIT binary patch
delta 277
zcmX?bisQg3jtz2KRFadE6O)V#lXX+mj8k+?43ms?lT6Z#b<I)|Q<KaL(h^gRQ>Oc;
qGM*+%<z&OHj%vghsiZTRu~K65hOO1>i87d$PEc64{jLa8vH$?-k6CyC

delta 277
zcmX?bisQg3jtz2KRE!hN%u-D)%ymr?O$>ES3@y`i6I0U?buAN3%#96FQ_PYr4X693
qGM*+%<z&OHj%vghsiZTRu~K65hOO1>i87d$PEc64{jLa8vH$=wb5S?|

diff --git a/doc/_build/html/.doctrees/guides/theory-based.doctree b/doc/_build/html/.doctrees/guides/theory-based.doctree
index ee20c0e1baf2ce8f3839a3d3824e3dbcfd736345..cafca0b28d02a3f174f546e6f36531298f0dc733 100644
GIT binary patch
delta 309
zcmcb(fcfGA<_(9Hn9WjCCm&NX0nwZPDs_mcSR|TTrWhNh=q9C@ChD44Bw6YvCRwEE
z8W^Tp7#pM*B_~=MOkQbzT9X)sN}8Gq)sr19L?-9eXs8jTLuayLmBeOoOKqn?<CL2Y
Ko7e7LA_M@Ix?C6l

delta 309
zcmcb(fcfGA<_(9Hm@QKbCm&NX0nwZPDs_mcSf-|0n3$Ou>87R_Tj-jYr6lQE7$%zN
z8l@$rS{hpzTN+uUPF`t#T9X)sN}8Gq)sr19L?-9eXs8jTLuayLmBeOoOKqn?<CL2Y
Ko7e7LA_M@8ELjKu

diff --git a/doc/_build/html/.doctrees/index.doctree b/doc/_build/html/.doctrees/index.doctree
index 34f4f1df14ef0de27c4f06f6b6da302ae647664e..13a6ef02e13efe10f900ec15309bc6dae81c1aad 100644
GIT binary patch
delta 272
zcmex3o#o?nmJPeTR7?{SlarEDEp;tZ&CGR8%uEw?EzM00bq&qUEK<zOQVkN#EGOUh
mI!&CybKZ`s#F(fwS@DA8W?vu40OHK0nFEgdZQlRJNFD%GPEi2>

delta 272
zcmex3o#o?nmJPeTR18wnER#)Al68#@O)YdyEG$fPEs~N9bS+X$jgyQ`O;e35(k9>c
mI!&CybKZ`s#F(fwS@DA8W?vu40OHK0nFEgdZQlRJNFD$Om{7$4

diff --git a/doc/_build/jupyter_execute/datasets.ipynb b/doc/_build/jupyter_execute/datasets.ipynb
index c5c5915..1bc9291 100644
--- a/doc/_build/jupyter_execute/datasets.ipynb
+++ b/doc/_build/jupyter_execute/datasets.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "6bc9e1d1",
+   "id": "2483e0f4",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3c2f7e86",
+   "id": "3a64ee65",
    "metadata": {},
    "source": [
     "# Datasets\n",
@@ -31,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "4c03ef56",
+   "id": "97a618b4",
    "metadata": {},
    "outputs": [
     {
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "65590d67",
+   "id": "90c0500c",
    "metadata": {},
    "source": [
     "## By topic\n",
diff --git a/doc/_build/jupyter_execute/getting-started.ipynb b/doc/_build/jupyter_execute/getting-started.ipynb
index 49f810d..e313c09 100644
--- a/doc/_build/jupyter_execute/getting-started.ipynb
+++ b/doc/_build/jupyter_execute/getting-started.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "2329b818",
+   "id": "7080fcba",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e21e788c",
+   "id": "667a7ef0",
    "metadata": {},
    "source": [
     "# Getting started\n",
@@ -44,7 +44,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "f0b1d8b2",
+   "id": "1c787f28",
    "metadata": {},
    "outputs": [
     {
@@ -88,7 +88,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "39f01525",
+   "id": "245b91c1",
    "metadata": {},
    "source": [
     "List everything that's available with `md.available_datasets()` (58 datasets), and\n",
@@ -103,7 +103,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "260e7425",
+   "id": "308f7abb",
    "metadata": {},
    "outputs": [
     {
@@ -142,7 +142,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b253c5d",
+   "id": "255d131b",
    "metadata": {},
    "source": [
     "## The inference pipeline\n",
@@ -154,7 +154,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "f80d95c5",
+   "id": "585e7a0f",
    "metadata": {},
    "outputs": [
     {
@@ -208,7 +208,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3a03948",
+   "id": "87bfdc80",
    "metadata": {},
    "source": [
     "Each verb has a focused guide: {doc}`guides/sampling`,\n",
@@ -228,14 +228,14 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "7f4a4ab2",
+   "id": "17b8bfb3",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/png": 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",
       "text/plain": [
-       "<plotnine.ggplot.ggplot object at 0x133bd1f30>"
+       "<plotnine.ggplot.ggplot object at 0x1367d5f30>"
       ]
      },
      "execution_count": 5,
@@ -260,7 +260,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca3b71d2",
+   "id": "19626132",
    "metadata": {},
    "source": [
     "See {doc}`guides/plotting` for shading, confidence-interval overlays, theoretical\n",
@@ -272,7 +272,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "681e85ae",
+   "id": "d743a4ac",
    "metadata": {},
    "outputs": [
     {
@@ -316,7 +316,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5de5f402",
+   "id": "14e8ef2d",
    "metadata": {},
    "source": [
     "Full details in {doc}`guides/regression`."
diff --git a/doc/_build/jupyter_execute/guides/confidence-intervals.ipynb b/doc/_build/jupyter_execute/guides/confidence-intervals.ipynb
index 41c8f31..a02b16a 100644
--- a/doc/_build/jupyter_execute/guides/confidence-intervals.ipynb
+++ b/doc/_build/jupyter_execute/guides/confidence-intervals.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "90c4c72c",
+   "id": "c3d90e82",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d2d21e97",
+   "id": "1beb7647",
    "metadata": {},
    "source": [
     "# Bootstrapping & confidence intervals\n",
@@ -33,7 +33,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "565a66b9",
+   "id": "6a561e3c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e8ab101",
+   "id": "357c5d64",
    "metadata": {},
    "source": [
     "## Confidence intervals — three methods"
@@ -60,7 +60,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "826b185b",
+   "id": "00400d65",
    "metadata": {},
    "outputs": [
     {
@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0a0ad7c0",
+   "id": "c0e71848",
    "metadata": {},
    "source": [
     "Distribution objects also expose the getter as a method:"
@@ -115,7 +115,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "f9ef37bb",
+   "id": "38eefebc",
    "metadata": {},
    "outputs": [
     {
@@ -152,7 +152,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad157a9b",
+   "id": "6be13897",
    "metadata": {},
    "source": [
     "## Visualize the interval"
@@ -161,7 +161,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "eb989df6",
+   "id": "0859a66c",
    "metadata": {},
    "outputs": [
     {
@@ -169,7 +169,7 @@
       "image/png": 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Bootstrap Distribution\"},\"xaxis\":{\"title\":{\"text\":\"mean\"}},\"yaxis\":{\"title\":{\"text\":\"count\"}},\"shapes\":[{\"fillcolor\":\"#1f77b4\",\"line\":{\"width\":0},\"opacity\":0.3,\"type\":\"rect\",\"x0\":3.606975,\"x1\":3.7499999999999996,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"},{\"line\":{\"color\":\"#1f77b4\",\"width\":2},\"type\":\"line\",\"x0\":3.606975,\"x1\":3.606975,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"},{\"line\":{\"color\":\"#1f77b4\",\"width\":2},\"type\":\"line\",\"x0\":3.7499999999999996,\"x1\":3.7499999999999996,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"}]},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -233,7 +233,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe464be1",
+   "id": "f351be58",
    "metadata": {},
    "source": [
     "## A confidence interval for a proportion"
@@ -242,7 +242,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "f9cba51d",
+   "id": "eeaa78b2",
    "metadata": {},
    "outputs": [
     {
@@ -288,7 +288,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "43ec658b",
+   "id": "e3882b53",
    "metadata": {},
    "source": [
     "## A confidence interval for a difference\n",
@@ -299,7 +299,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "81751a46",
+   "id": "22e351f0",
    "metadata": {},
    "outputs": [
     {
@@ -341,7 +341,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a259911f",
+   "id": "dca4f734",
    "metadata": {},
    "source": [
     "```{seealso}\n",
diff --git a/doc/_build/jupyter_execute/guides/hypothesis-testing.ipynb b/doc/_build/jupyter_execute/guides/hypothesis-testing.ipynb
index 681b84f..f519380 100644
--- a/doc/_build/jupyter_execute/guides/hypothesis-testing.ipynb
+++ b/doc/_build/jupyter_execute/guides/hypothesis-testing.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "46c26eba",
+   "id": "f2978ad8",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "071d4511",
+   "id": "55eef650",
    "metadata": {},
    "source": [
     "# Hypothesis testing\n",
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "45ea42ae",
+   "id": "c9fcbd66",
    "metadata": {},
    "outputs": [
     {
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a0ef462e",
+   "id": "ee08c36d",
    "metadata": {},
    "source": [
     "## Shade the p-value"
@@ -102,7 +102,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "5b11db5a",
+   "id": "398b462a",
    "metadata": {},
    "outputs": [
     {
@@ -110,7 +110,7 @@
       "image/png": 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Null Distribution\"},\"xaxis\":{\"title\":{\"text\":\"diff in props\"}},\"yaxis\":{\"title\":{\"text\":\"count\"}},\"shapes\":[{\"line\":{\"color\":\"#d62728\",\"dash\":\"solid\",\"width\":2},\"type\":\"line\",\"x0\":0.03399999999999992,\"x1\":0.03399999999999992,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"},{\"fillcolor\":\"#d62728\",\"line\":{\"width\":0},\"opacity\":0.3,\"type\":\"rect\",\"x0\":0.03399999999999992,\"x1\":0.07509999999999994,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"}]},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -157,7 +157,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "05406ed1",
+   "id": "e65196aa",
    "metadata": {},
    "source": [
     "`direction` is one of `\"right\"`/`\"greater\"`, `\"left\"`/`\"less\"`, or `\"two-sided\"`.\n",
@@ -171,7 +171,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "cfba73b4",
+   "id": "dfddbac4",
    "metadata": {},
    "outputs": [
     {
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d1e0d753",
+   "id": "dd2f3a43",
    "metadata": {},
    "source": [
     "For a one-proportion test you can also *simulate* draws directly:"
@@ -227,7 +227,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "81e1bc3e",
+   "id": "cae07fdd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -245,7 +245,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "527c35eb",
+   "id": "f74c981d",
    "metadata": {},
    "source": [
     "## Available statistics\n",
@@ -266,7 +266,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "7e204d86",
+   "id": "6b65653f",
    "metadata": {},
    "outputs": [
     {
@@ -315,7 +315,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d3c3c6ce",
+   "id": "169a633f",
    "metadata": {},
    "source": [
     "The function receives `(response, explanatory)` as numpy arrays (`explanatory` is\n",
diff --git a/doc/_build/jupyter_execute/guides/plotting.ipynb b/doc/_build/jupyter_execute/guides/plotting.ipynb
index b9e83dc..c06be0e 100644
--- a/doc/_build/jupyter_execute/guides/plotting.ipynb
+++ b/doc/_build/jupyter_execute/guides/plotting.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "29fc1175",
+   "id": "95b03bcd",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ca7d6fa",
+   "id": "61ec962e",
    "metadata": {},
    "source": [
     "# Plotting: plotly & plotnine\n",
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "32be16b0",
+   "id": "b6b94db6",
    "metadata": {},
    "outputs": [
     {
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47815da8",
+   "id": "aa55073c",
    "metadata": {},
    "source": [
     "## visualize() returns an InferPlot\n",
@@ -104,7 +104,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "713fb87c",
+   "id": "67337b81",
    "metadata": {},
    "outputs": [
     {
@@ -112,7 +112,7 @@
       "image/png": 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       ],
       "text/plain": [
        "Figure({\n",
@@ -168,14 +168,14 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "39b992b7",
+   "id": "6c219933",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "<plotnine.ggplot.ggplot object at 0x138733950>"
+       "<plotnine.ggplot.ggplot object at 0x13852f950>"
       ]
      },
      "execution_count": 4,
@@ -195,7 +195,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "65534b9c",
+   "id": "86a5f853",
    "metadata": {},
    "source": [
     "### Keyword form\n",
@@ -206,7 +206,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "2162766f",
+   "id": "fdd13db2",
    "metadata": {},
    "outputs": [
     {
@@ -214,7 +214,7 @@
       "image/png": 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       ],
       "text/plain": [
        "Figure({\n",
@@ -269,7 +269,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c51e26b",
+   "id": "8d197fdf",
    "metadata": {},
    "source": [
     "## Shading p-values"
@@ -278,7 +278,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "82033a17",
+   "id": "60a56f84",
    "metadata": {},
    "outputs": [
     {
@@ -286,7 +286,7 @@
       "image/png": 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9XNbOHyddO7Yu6fCW8tIy8v7oRmxMTLHysflfHCvvj1RERxXv5zOP3iFvvfyfQuma7u2LHaNoxknHNZEBffOu2K3J/2Kd1X5rMLnS/bHxVZdfbKpdvmaDjBjzmlx23b9E7zfVzP0Hk3QmI4beUmLq7r6iqwUOJSXrTI5rmhdMmZVKTqKiXO6rvFebWqbY4iqvaUqJk1827zD5555xspmXNLHiXdJ+/s5zuVymSper+HloNvhp4pfXSnReW/3UJKpBAAEEELCBQGB/+lSgg1klPExfq4mNjfEGjT/9tk2zApoqesVXb1/Qhs18aZh5hNqp7qtv+nSJmPzAVLcVTNml9LdgmSYN6ppVfdC/WSgw+XNP3hfbGtQ7ukCutUUNXFv843gpmDwf7ZZXQ4z7FxIto1/O07kv/W7UoI48+eAA2bDkZdErx/pLjQa5i1au16rklBPyrmR3cf+S0LtbOymazjvzFFPO09ai3/43Gysx6XDhWeaje73yvHlryX9woqLnRyWaAzr7sQAAEABJREFUVWhX/ZLguKlzTZ7e1mIWSpmU5+3Zzcq56Cnr6/zP3XnnqedKr2d/XxyttC9QrxVPe5kjgEAIBTg0ApUQsF3A2+HqIeZLavrt7KL9Wpv/WK3TTzmu6Ca/rjesf7T5gtvuvQe89epH91Zupfgz/76/qPwrWlqB3l/7+7a8j991XZMG8Dq38tSEk9wfsWvZ2YtWi95Hq8ua9CPt5Ws+M7cX1D26pmYFJelTJGYtXGWO1Tx/LKz2e92n34jurzu7XC5zJVzvN9b1X/KfNqFPt9D1STPe0VmhpLcZ6BeoNFMDdp0vKvJUhsp+2cjlcon+wROtW311XjBV5vwoWE9Flzdv/VPuefRFc5tM354dpaxbOqx4+3IuVrTNni9sFgzOrTr60j67vVYq6sV+CCCAAAL+FbBdwKtX+vRLat1ufNA8x1OfJavPX+1ywwPmubrNmtQXKx+7V4bJ8wxgfbSSPi5L78Ht0HuI6NMcyqtX/xqWltHne+q++qeQu7rbro8/0/yCSR+jpV/M02eRakDw/KTZBTd7l/UK8e39upsvrt085Gl5Z/lHoo980r92poX0D2+4XC5d9HvS8dB7dTVpO/X5wRf1uMtY6G0a+iVDPajVfmvbO1031Dx9Qx8f9vY7H8jjL8zQKsyX8nSh/3VdzFMt1E8dZy9aI2/Mf1/0vNB9N+Z/wVC/XKT3GOuV2KGPvyxT3lhs/gKZfqFO66lMuvj8FnJGgS/9FayrMudHwXqsLr80bb4xuu+xCeZ5w/p4sE+//EE6tT1PHrirb5nVWPHWCqyei1rWSnpy3OvmPmh9vOB9+fdp/+OkZnJp/j3cWocvjlbbF8rXivaJhAACCCBgTwHbBbxPPXyr6LNZNRDUoE6fJatfINJ1vZo1Y/zDUjUxoZBmVIH7AqOi8rrkcrkKlXG58tYLljUF8vPNcv6k39WXyUUtTzdBnf5BhDmL17iDsUtNnhbJq0mX8lLBOh90ByB6BVqfjar76iOuLji3uXnUkZZ2uf7e+yF32c7tW8r8JR/KSHfQp0FvwTIu199lB918ldx5S0/vc4P1of5JyanmkV2d8/9KmO5bWv91mybPdl22mvRpDJq0nRq461U6vT95lHusPHVY7bc+LUODVH36xgNPTjaB3K+/7zB/ya31uaeZ6vSLhPOmjpRunS4wjycb8fx0GTX+dXPlXwPsFqfmXeHXR8xNff5+c4V76Qefij6iTJ8yMeTW3qYel+tvP5NR0iS/jMtVuKzL5ZK785/Lq7u5V3VmUmXOD5cr7zguV97cVFjKJCoqr8x7qz4R/cVg3affSmpauuh4Txp9nzw3fKDE5D+FwVOFp1rPOFvx1n1LOxc99bhceW3RskVTwfPf5cort//AYfOHQh4Z/ap55rK+pic/8y/x9Enr8MWxtPblH85db97rXuu1+lpxufLa6hKX7lYsefpebAMZCISFAI1EAIGCAn//lCiYG8LlKy+7SCaN/pd8vepV8wxZfT6tPtP22w/+K8Pu6Sd6BcfTvCoJcbJpzXQZ+/hdniwTlGretVd28Obpgj7CS/P1CRC67kn6fFDN1/tIPXkacOkP5/fffl7mTX1cvlj+ijsgu0FeeXaoOV71aommaEnHr1enlsx6+T/m2b/a9vXvThD9cpg+H1aPU7NG3tMStIITjm1snkP78eKJ5tmx6999SbNFAz8tqwG+yXBPNLAZdFMP+WrlVHl3+pOm/k/c++kfZXBv9v7XQF33Ldp/bZfmD7/vJm/Z8hY+WzrJ9Ff386R1C1+U1/PvT9Y2eerQ+q30W9urz8xdv2iC97mnunx9r06eqsxc6xs97HZzHujzkvUJEToO6ljwyqve76vP8dXt+oxiNbn1+m6m3fqED1NZGZNru7c3ZdW8aDG9qujpt46VZ3tlzg89jtZZcGw99RadP/efgaZtWl6Tjofe8zzmsUHuXwrPKBbs6v76S5GWVRddt+qt/Rvz2J1S9Fws7XzSuks6//WXDT2+tnP13LHmSSt6zuhrWh9npvt5klVHLV9a+4r2V8vqeWnlteJp64nHNdbdvKmz+xdI7UOXjq28eSwggAACCIS3gO0CXg+n/tCqX/co8+UhnRe8MuQpE8i5y+USvcdQv3Tm63Ndta1664V+hFswwC2tvTXcAbQ+R9Zzr2Jp5TRfy+gPf61fn2SheRVN/t7Pl37XrF5V9AkCjRvUEc+TJkpqj54HxzSqZ/5aXmnj4DnucU0bSrBMXK6Knx8l9TPQeVa9fTkXy2uz/tLS/JRjze0ppZV1uXxz9KV9dn6tlOZBPgIIIIBAYARsG/AGprvUigACCCCAQDEBMhBAIMIFCHgjfIDpHgIIIIAAAggg4HQBAl6rZwDlEEAAAQQQQAABBMJSgIA3LIeNRiOAAAKhE+DICCCAQLgJEPCG24jRXgQQQAABBBBAAAGfBAIU8PrUBgojgAACCCCAAAIIIBAwAQLegNFSMQIIICAiICCAAAIIhFyAgDfkQ0ADEEAAAQQQQACByBcIZQ8JeEOpz7ERQAABBBBAAAEEAi5AwBtwYg6AAALWBSiJAAIIIICA/wUIeP1vSo0IIIAAAggggEDlBNjbrwIEvH7lpDIEEEAAAQQQQAABuwkQ8NptRGgPAtYFKIkAAggggAACFgQIeC0gUQQBBBBAAAEE7CxA2xAoW4CAt2wftiKAAAIIIIAAAgiEuQABb5gPIM23LkBJBBBAAAEEEHCmAAGvM8edXiOAAAIIOFeAniPgOAECXscNOR1GAAEEEEAAAQScJUDA66zxtt5bSiKAAAIIIIAAAhEiQMAbIQNJNxBAAAEEAiNArQggEP4CBLzhP4b0AAEEEEAAAQQQQKAMAQLeMnCsb6IkAggggAACCCCAgF0FCHjtOjK0CwEEEAhHAdqMAAII2FCAgNeGg0KTEEAAAQQQQAABBPwnEIqA13+tpyYEEEAAAQQQQAABBMoRIOAtB4jNCCCAQOAEqBkBBBBAIBgCBLzBUOYYCCCAAAIIIIAAAqULBHgLAW+AgakeAQQQQAABBBBAILQCBLyh9efoCCBgXYCSCCCAAAIIVEiAgLdCbOyEAAIIIIAAAgiESoDj+ipAwOurGOURQAABBBBAAAEEwkqAgDeshovGImBdgJIIIIAAAgggkCdAwJvnwBQBBBBAAAEEIlOAXiEgBLycBAgggAACCCCAAAIRLUDAG9HDS+csC1AQAQQQQAABBCJWgIA3YoeWjiGAAAIIIOC7AHsgEIkCBLyROKr0CQEEEEAAAQQQQMArQMDrpWDBugAlEUAAAQQQQACB8BEg4A2fsaKlCCCAAAJ2E6A9CCAQFgIEvGExTDQSAQQQQAABBBBAoKICBLwVlbO+HyURQAABBBBAAAEEQihAwBtCfA6NAAIIOEuA3iKAAAKhESDgDY07R0UAAQQQQAABBBAIkoDtAt4g9ZvDIIAAAggggAACCDhEgIDXIQNNNxFAIOwEaDACCCCAgJ8ECHj9BEk1CCCAAAIIIIAAAoEQqHydBLyVN6QGBBBAAAEEEEAAARsLEPDaeHBoGgIIWBegJAIIIIAAAqUJEPCWJkM+AggggAACfhLI2rtXfjjlVPnp/PMlefVqEgY+nwM+nIoULUGAgLcEFLIQQAABBBBAAAEEIkeAgDdyxpKeIGBdgJIIIIAAAgg4SICA10GDTVcRQAABBBBAoLAAa84QIOB1xjjTSwQQQAABBBBAwLECBLyOHXo6bl2AkggggAACCCAQzgIEvOE8erQdAQQQQACBYApwLATCVICAN0wHjmYjgAACCCCAAAIIWBMg4LXmRCnrApREAAEEEEAAAQRsJUDAa6vhoDEIIIAAApEjQE8QQMAuAgS8dhkJ2oEAAggggAACCCAQEAEC3oCwWq+UkggggAACCCCAAAKBFSDgDawvtSOAAAIIWBOgFAIIIBAwAQLegNFSMQIIIIAAAggggIAdBMIr4K2kWE5OrmRn55RZy18HDoumooWSklPkwKGkotmsI4AAAggggAACCNhcwDEBb25urowYM10ef+G1YkOigfCUNxZLm6sGS9ued8vlfe/3lklJTZPBw8ZJ626D5OIeg6XPoJGyb/8h73YWEEAAgVAIcEwEEEAAAesCjgh4l6/ZYALZuYvXlijzwitzZMac5XLHjT1k3cIXZdGMUd5yby5YJT9v3i6r546VTxZPlOioKBk3dZ53OwsIIBA5Atu2bZM1a9aENG3atEnS09MjB5WeIIAAAoEVsFS7IwLeNq3OlDlTRki3ThcUQ9n710GZ9tYSue/2a+X6XpdK7VrVpUHd2t5yy1ZvkN7d2km9OrWkerVE6de7k8xf8qHoFWNvIRYQQCAiBHbu3BnSYHeNO9j+7rvvIsKSTiCAAAJ2EnBEwJtYJd4EsVUTqxSz/+b7zSbvux9/l36DR8mAoc/Kuys+Mnk62bJ9tzRtXF8XTTqmUT0zP5ycYuZMEEAgDARoIgIIIICAowUcEfCWNcK79u43m+scXVNu+eflcu4ZJ8tDo6bIe6s+MVdx9R7ehPg4U0Yn8XGxOpOUlDQzP3QkU0gYcA6E/zmQlpFtXtN2mGRm5/C+EmHvrYdTMvNOrVyRI2lZJAx8PgfyTqDKT51ag+MDXh34E5o1koE39pBLLj7HzLtfdqGsXPu5uFwuSaySIOkZ+W9U7sKe5cTEBPeaSGJ8NAkDzoEIOAdiY+zzdhit7z0RYMr7498/H6rERZufGeISiY+NImHg8zmQdwIxraiAfd7hK9qDSu7XpGFd+W3LTsnM+vvqTpZ7OTMry9TcrEl92bpjt1nWybade3QmNaolmrn+kCRFCQaRZODMvkRHucQu/6LcbeE1FRVx7yuS/y8mOkpIGPh6Dgj/KiXgiIBXn72bmZkl2dnZYoJZ97I+ikzlzm5xkrmKO3nGu+7tOfL197/J0g8+lYtattDN0rl9S5mzaI3s2XdQko+kysy5K6VXl7bictnnh6NpKBMEEEAAAQQQ8K8AtUWMgCMC3nnvrZWzOg0QfSzZwmX/M8sLl60zg6hXasePHCyvzVkuZ3TsL30HjZS+PTvKtVe2N9v79rxUjm/WSDr0HiKtug4UDZwH9+9ltjFBAAEEEEAAAQQQsL+AIwLea6/sIJvWTC+U9CqtZ3guOK+5fLx4gqx46zn5bOkkGXZPP/fHTdFmc9XEBHn56Xtl/aIJsnb+OHl78nDziDKzkQkCIhgggAACCCCAgM0FHBHwWhmDmOhoadygjrm9oaTyNatXlTq1a5a0iTwEEEAAAQQQEAgQsK8AAa99x4aWIYAAAggggAACCPhBgIDXD4hUYV2AkgggUL5ATk6OHDhwQHbt2hXSdOjQofIbSwkEEEAgDAQIeMNgkGgiAgg4T2Dt2rUyadKkkKaNGzc6Dz54PeZICCAQRAEC3iBicygEEEAAAQQQQACB4AsQ8Abf3PoRKYkAAggggAACCCBQaQEC3tLwtrcAABAASURBVEoTUgECCCCAQKAFqB8BBBCojAABb2X02BcBBBBAAAEEEEDA9gIRFPDa3poGIoAAAmEnkJ6eLqtXr5aFCxeGNO3Zsyfs7GgwAgjYR4CA1z5jQUsQcKRAamqqpKWlhTxlZWVFjr+fe/LTTz/JV199FdKk54mfu0V1CCDgIAECXgcNNl1FwI4CR44ckdmzZ4c0LVmyRFwulx15aBMCCCCAQCUEPLsS8HokmCOAQEgE9Mrq5s2bJZRp27ZtIek7B0UAAQQQCI4AAW9wnDkKAgjYVoCGIYAAAghEugABb6SPMP1DAAEEEEAAAQSsCERwGQLeCB5cuoYAAggggAACCCAgQsDLWYAAAr4IUBYBBBBAAIGwEyDgDbsho8EIIIAAAgggEHoBWhBOAgS84TRatBUBBBBAAAEEEEDAZwECXp/J2AEB6wKURAABBBBAAIHQCxDwhn4MaAECCCCAAAKRLkD/EAipAAFvSPk5OAIIIIAAAggggECgBQh4Ay1M/dYFKIkAAggggAACCARAgIA3AKhUiQACCCCAQGUE2BcBBPwrQMDrX09qQwABBBBAAAEEELCZAAGvzQbEenMoiQACCCCAAAIIIGBFgIDXihJlEEAAAQTsK0DLEEAAgXIEHBXw5uTkSnZ2TjkkJW9OSk6RA4eSSt5ILgIIIIAAAggggIBtBZwS8Epubq6MGDNdHn/htVIHY8eufdLyijtkzOTZ3jIpqWkyeNg4ad1tkFzcY7D0GTRS9u0/5N3OAgIIIIAAAggggIC9BRwR8C5fs0Ha9rxb5i5eW+po6BXcgQ+MEQ1wCxZ6c8Eq+Xnzdlk9d6x8sniiREdFybip8woWYRkBBBAIIwGaigACCDhPwBEBb5tWZ8qcKSOkW6cLShzhrOxs+ffIl+WcFidL5/bnFyqzbPUG6d2tndSrU0uqV0uUfr07yfwlH5orxoUKsoIAAggggAACCCBgS4ESA15btrQSjUqsEi8N6taWqolVSqzlmQlvSUZGlgy754Zi27ds3y1NG9f35h/TqJ5ZPpycYuZMEEAAAQQQQAABBOwt4IiAt6whmLVwlaz9+Ct5YcRdEhsbU6io3vertzgkxMd58+PjYs1ySkqamWdm5QgJA86Bip0DWRX8Eql58fl5kptbYoWOz9T3Qbsg6PkSzq81j6P2g5QjGPhm4Dl/mFdMwPEB7/S3l0mzJvVl8sx35ZkJs2TTT7/L+s83yZQ3FovL5ZLEKgmSnpHp1fUsJyYmmLwjaVlCwoBzoGLnQFpGttgnzrRRS+zTFPM+Z4eJ/kKSnpETtu+3KelZeYzusdV+kHLcP1tJvpwHeSdQKKfhfWzHB7z9r7tCzjvzFKlVs5pJ0dFRold0a1RLNCOrwfDWHbvNsk627dyjM/Fsr1UtTkgYcA5U7ByoViVWXOYVFfqJy2WXlojYqCnuttjDRU2qVokJ2/fbmlXjxPxzc2o/SDGCgW8G5vxhUmEBRwS82e6PTTMzsyQ7O1uysrJFl/WZvKr2zx6XyG03dPemU09sJue0OEk0X7d3bt9S5ixaI3v2HZTkI6kyc+5K6dWlrW1+CGgbSQjYRYB2IIAAAgggYEcBRwS8895bK2d1GmAeS7Zw2f/M8sJl6yyNR9+el8rxzRpJh95DpFXXgSZYHty/l6V9KYQAAggggAACjhSg0zYTcETAe+2VHWTTmumFkl6lLWksxjw2SO67/VrvpqqJCfLy0/fK+kUTZO38cfL25OHmEWXeAiwggAACCCCAAAII2FrAEQGvP0agZvWqUqd2TX9URR0I5AkwRQABBBBAAIGgCBDwBoWZgyCAAAIIIIBAaQLkIxBoAQLeQAtTPwIIIIAAAggggEBIBQh4Q8rPwa0LUBIBBBBAAAEEEKiYAAFvxdzYCwEEEEAAgdAIcFQEEPBZgIDXZzJ2QAABBBBAAAEEEAgnAQLecBot622lJAIIIIAAAggggEC+AAFvPgQzBBBAAIFIFKBPCCCAgAgBL2cBAggggAACCCCAQEQLEPCKSESPMJ1DAAEEEEAAAQQcLkDA6/ATgO4jgAACBQRYRAABBCJSgIA3IoeVTiGAAAIIIIAAAgh4BHwPeD17MkcAAQQQQAABBBBAIAwECHjDYJBoIgII2FOAViGAAAIIhIcAAW94jBOtRAABBBBAAAEE7Cpg+3YR8Np+iGggAggggAACCCCAQGUECHgro8e+CCBgXYCSCCCAAAIIhEiAgDdE8BwWAQQQQAABBJwpQK+DL0DAG3xzjogAAggggAACCCAQRAEC3iBicygErAtQEgEEEEAAAQT8JUDA6y9J6kEAAQQQQAAB/wtQIwJ+ECDg9QMiVSCAAAIIIIAAAgjYV4CA175jQ8usC1ASAQQQQAABBBAoVYCAt1QaNiCAAAIIIBBuArQXAQRKEiDgLUmFPAQQQAABBBBAAIGIESDgjZihtN4RSiKAAAIIIIAAAk4SIOB10mjTVwQQQACBggIsI4CAQwQcFfDm5ORKdnZOiUN7KOmI7Nl3sMRtmpmUnCIHDiXpIgkBBBBAAAEEEEAgjAQcE/Dm5ubKiDHT5fEXXis0PPv2H5IuNzwgF3a/Uzr0HiJX3vSwLFqx3lsmJTVNBg8bJ627DZKLewyWPoNGiu7jLcACAggggAACCCCAgK0FHBHwLl+zQdr2vFvmLl5bbDD0qu9Vl18sq+aMkU8WT5TLO5zvDopnSGpahin75oJV8vPm7bJ67lizPToqSsZNnWe2MUEAAQScJEBfEUAAgXAVcETA26bVmTJnygjp1umCYuNUr04tue2G7tKgbm2pXi1Rrux8kaS4r+r+8Msfpuyy1Rukd7d2ouV0e7/enWT+kg9FrxibAkwQQAABBBBAAAEEbC3g54DXnn1NrBJvAtqqiVXKbeBnX/1oyhx7TEMz37J9tzRtXN8s6+SYRvV0JoeTU8ycCQIIIIAAAggggIC9BRwR8Fodgl9+3y6jxr8hA2/sIbVrVTdXcfVqb0J8nLeK+LhYs5ySkmbmh45kCgmD8DgHMtznqr1Scmqm5JpXUugnfv/UphJdsouJdsEuLrlulCNpWbY7hw8dsfaaOpySd5ucnvDaD1KWYOCbgb4eSRUXIODNt9uxa5/cfv/zcsnFZ8vAm3qYXJfLJYlVEiQ9I9Os68SznJiYoKtSJS6KhEGYnAMx7nbaK8XHRotL7PKPlthlJEpqh/vtWOJj9P02xnbncZW48tuUEBsj5p/7NIt394MUZcYTB+sO5vwJ04kdmk3A6x6FX3/fIdfdMULatDpDnnxwgERH/83SrEl92bpjt7tU3v9tO/eYhRrVEs08zv0DmxQtGISDQZR7nOyVYt0/+MUm/zSgsklTbNUMl8tlm/bEuM+XuNgo253HVtvkgdR+kKIEA98MhH+VEvg7sqtUNfbeWZ+9m5mZJdnZ2ZKVlS26rE9n0Fb/9Ns26XHLMLng3OYyoG9X2b33gOjVXs8zdzu3bylzFq0xz+hNPpIqM+eulF5d2orL5dLdSQgg4BcBKkEAAQQQQCBwAo4IeOe9t1bO6jRA9LFkC5f9zywvXLbOqG7estPM31v1iVze93657LqhJo2eMMvk9+15qRzfrJF5Rm+rrgNNsDy4fy+zjQkCCCCAAAIIIOBXASoLiIAjAt5rr+wgm9ZML5T0Kq2KXnFJq0L5nnJPP3ybbpaqiQny8tP3yvpFE2Tt/HHy9uTh5hFlZiMTBBBAAAEEEEAAAdsLOCLg9cco1KxeVerUrumPqqgDgcoKsD8CCCCAAAII+CBAwOsDFkURQAABBBBAwE4CtAUBawIEvNacKIUAAggggAACCCAQpgIEvGE6cDTbugAlEUAAAQQQQMDZAgS8zh5/eo8AAggg4BwBeoqAYwUIeB079HQcAQQQQAABBBBwhgABrzPG2XovKYkAAggggAACCESYAAFvhA0o3UEAAQQQ8I8AtSCAQOQIEPBGzljSEwQQQAABBBBAAIESBAh4S0CxnkVJBBBAAAEEEEAAAbsLEPDafYRoHwIIIBAOArQRAQQQsLEAAa+NB4emIYAAAggggAACCFReIJgBb+VbSw0IIIAAAggggAACCPgoQMDrIxjFEUAAgcoLUAMCCCCAQDAFCHiDqc2xEEAAAQQQQAABBP4WCNISAW+QoDkMAggggAACCCCAQGgECHhD485REUDAugAlEUAAAQQQqJQAAW+l+NgZAQQQQAABBBAIlgDHqagAAW9F5dgPAQQQQAABBBBAICwECHjDYphoJALWBSiJAAIIIIAAAoUFCHgLe7CGAAIIIIAAApEhQC8Q8AoQ8HopWEAAAQQQQAABBBCIRAEC3kgcVfpkXYCSCCCAAAIIIBDxAgS8ET/EdBABBBCIHIEVK1ZIqFN6enrkgBboCYsIRLIAAW8kjy59QwABBCJMYP369RLqlJ2dHWGqdAeByBcg4I38MfZjD6kKAQQQQAABBBAIPwEC3vAbM1qMAAIIIBBqAY6PAAJhJeCogDcnJ1eys3NKHCDdtmvvfskq5aOqpOQUOXAoqcR9yUQAAQQQQAABBBCwr4BjAt7c3FwZMWa6PP7Ca8VGY+3HX0urrgOl4zX3yZkd/09mL1rjLZOSmiaDh42T1t0GycU9BkufQSNl3/5D3u1lLLAJAQQQQAABBBBAwAYCjgh4l6/ZIG173i1zF68tRp6aliFDH39Z7urfU75e9aqMGzlYRjw/Xbb/udeUfXPBKvl583ZZPXesfLJ4okRHRcm4qfPMNiYIIIAAAlYEKIMAAgiEVsARAW+bVmfKnCkjpFunC4ppb/jyB9GruH16XCIx0dFyaZtzpVmT+rL2469M2WWrN0jvbu2kXp1aUr1aovTr3UnmL/lQ9IqxKcAEAQQQQAABBBBAwNYCtgl4A6mUWCVeGtStLVUTqxQ7zO59B0yAGxcX6912QrNGsmvPAbO+Zftuadq4vlnWyTGN6ulMDienmHm23hdMEhxyMajg68C8kGwwybVBG+zYhFxgShyWirzneSrS74yQcgUD3ww85w/zigk4IuAti+Zw0hFJrJJQqEh8fJzol9T0Kq5e/U1wr3sKxOcHxikpaSbr8JFMIWHAOVCxcyAlLUtsE0/ZJ7Jzf4Jk3l5sMrHHCNloeMz4JKf4ds4nucubAXVzpqRnCwkDX88Bc/4wqbCA4wPeGtWrmlsaCgqmp2eY2xdcLpcJhtMzMr2bPcuJiXlB8lHV44SEAedAxc6B6omx4vK+ukK74HLZpSUiNmqKuy32cLGXiUjNar6d87Xc5UX/uTmrVYkREga+ngN6+jgv+a/HUf6rKjxrql/nKNHbFjIzs7wd0C+pNah3lFnX+3m37thtlnWybecenUmNaolmzgQBBBBAAAEEEEDA3gKOCHizs3NEA9rs7GzJyso2y3rvkA5Ny7NO1ZnMWrhKstzb31/3hXlCQ7sLzjL5ndu3lDmL1siefQcl+UiqzJy7Unp1aWuOf+Q1AAAQAElEQVSbqx6mkUwQcLAAXUcAAQQQQKA8AUcEvPPeWytndRpgHku2cNn/zPLCZeuMjX6h7cUn75HRE2aZZ/De8+iL8siQftKkYV2zvW/PS+X4Zo2kQ+8h5lm9GjgP7t/LbGOCAAIIIIAAAgjYRIBmlCHgiID32is7yKY10wslvUrrcbnkorPlm1XTZMVbz8lXK6dKn6s6ejZJ1cQEefnpe2X9ogmydv44eXvycPOIMm8BFhBAAAEEEEAAAQRsLeCIgNfKCERHR0njBnUkNjamxOI1q1eVOrVrlriNTATCRoCGIoAAAggg4EABAl4HDjpdRgABBBBAwOkC9N9ZAiEJeDd8+aP89Nu2YtJ7/zooi1asN18eK7aRDAQQQAABBBBAAAEEKiAQkoB35tzl8sFHG4s1V78Q9uCoV2TbjrxHfxUrQAYCQRXgYAgggAACCCAQCQIhCXhLgzt4ONls0vtpzQITBBBAAAEEEAi9AC1AIMwFghrw6tXbOx54Xj7Z+IMsXPo/0WVP6n/vaLnmtsfkHyc1k6aN64c5K81HAAEEEEAAAQQQsItAUAPe2JgYiYuLlYT4WNGruLrsSfoEhMeG3iwTn7rXLja0wzcBSiOAAAIIIIAAArYUCGrAO/L+/jJ+5N1y3+3XyqNDbjTLuq7pmUfvkGu6tecZt7Y8TWgUAggggIB1AUoigIDdBIIa8Ho63/OKNnLBec0lJydXjqSkFUuecswRQAABBBBAAAEEEKisQEgC3j37DsrIF2ZIu153y/ld7iiWDiUdqWy/bL8/DUQAAQQQQAABBBAIjkBIAt6pby6Wt975QPr0vFRGPXSr6O0MBVNiQnxwes9REEAAAQRCLcDxEUAAgYALhCTgXfrBp3J7v+4y6KYe0qPzRdK1Y+tCKTY2JuAd5wAIIIAAAggggAACzhAIScDb/JRjJSk5xbowJRFAAAEEEEAAAQQQqKBASALefr07y8JlH8m+/Ycq2Gx2QwABBJwpQK8RQAABBHwXCEnAu2DpOklJTZN2ve6R5u1vLpb40prvA8keCCCAAAIIIICAgwR86mpIAt4rOrSSfw+8rtSUEB/nUycojAACCCCAAAIIIIBAaQIhCXg7tjlHbv7n5aWm+LjY0tpLPgIIIGBdgJIIIIAAAgi4BaLcKej/c3NzpawU9AZxQAQQQAABBBBAIIIFnN61kAS89/znRTm9wy2lJu7hdfppSf8RQAABBBBAAAH/CYQk4L2mW3sZft9NxVLtWtWlTasWUoV7eP03wtSEgGUBCiKAAAIIIBCZAiEJeNu0OkOuvbJDsXTf7dfKF9/8EpnS9AoBBBBAAAEEwkOAVkacQEgC3tIUzz3jZPO4sl//2FFaEfIRQAABBBBAAAEEEPBJwDYBb05Orvxvw3em8dWqJpo5EwRsLEDTEEAAAQQQQCBMBEIS8D76zDRpc9XgQqnFJbfIk+NmSuf2LaVp43phwkczEUAAAQQQcLoA/UfA/gIhCXhbn3uaXNfjkkJJ/xDFgmlPyJjH7rS/Gi1EAAEEEEAAAQQQCBuBkAS8XTu2ljtv6Vko6R+iOPn4JmEDR0N9E6A0AggggAACCCAQKoGQBLza2fSMTFmwdJ08/dKbMvy5/8qUNxbLtp17dFPI0p979oveS1xSA5KSU+TAoaSSNpGHAAIIIICAVQHKIYBACARCEvDu239IrrzpYXlk9Ksyc+4KWbLqUxk7Za5c3vd+Wb7ms6AzzJizXDr3+bdcf+dIadfrbhkzeba3DSmpaTJ42Dhp3W2QXNxjsPQZNFK0/d4CLCCAAAIIIIAAAgjYWiAkAe+4qfNk/8EkeW3cQ7JxxRT5bOkkWTTjKWl/4Vly32MTJDUtI2hom376Q0ZPmCUj7/8/+WDOC/La+Ifl1VlL5OvvfzNteHPBKvl583ZZPXesfLJ4okRHRYm232wM1IR6EUAAAQQQQAABBPwmEJKA96PPvpU+V10i5515isTHxZrOHN+0oQzu38ss/7YleM/hTU5JNcdsUO8oM9d26F9827p9t1lftnqD9O7WTurVqSXVqyVKv96dZP6SDyU3N9dsZ4IAAgggEDgBakYAAQT8IRCSgLdm9apyOCmlWPtLyitWyM8ZZzc/Uc5pcbJcPWC4ub1i0ox3pU7tmtLOfbVZD7XFHfg2bVxfF006plE9Mz+cXLz9ZgMTBBBAAAEEEEAAAVsJhCTg1T8tPGfxGtGkAeWhpCPywUdfyvOT3pbatarLKSc09QGpckXj3FeY27Y+QxrWqy2LV34sL06bL21bnymJVeLNVVy9hzchPs57EM8V6ZSUNJOXlJolJAw4Byp2DqSkZ5vXkR0mdvrUxk6fH9nFxVYm7hM2Oc23cz7Z/bPCvZv5n5qRLSQMfD0HzMnDpMICIQl4B950lVzU8nR57Lnp0uWGB+TC7neaL4bt3L1Pxo28W2JjoivcIV93XPfpN6JfmHt9wiPy9uTh8sqzQ0Xv2523eK24XC534Jsg+kQJT72e5cTEBJMVF+0SEgacAxU7B2KizMuISREBV5F1y6sRXNBOJtqWuCiXT+/9se6fFZ7hiXHvS3IJBr4ZeM4f5hUTiKrYbpXbq0pCnAksX39pmAy/7ybRPzoxbuRgWT7rWTmnxUmVq9zHvb/45mc5oVkjqVEt0ex5kTsQP//sU0XzNaNZk/qydUfe/by67nl0mqd8fFy0kDDgHKjYORAXGy12+edyaRhjl9bYpx0uFy4ljUZcBd77PfXExkS5L+yQcPDtHBD++SRQtHBIAt4fftliHv119FE15dorO4j+0YlL25wrr89baZ7NW7SRgVw/+fhj5LctO0Wv9OpHd1t37JE167+S004+1hy2c/uWMmfRGtmz76AkH0mVmXNXSq8ubc3VX1OACQIIIIAAAggggICtBUIS8GpgqwFm4wZ1CuFUq5pons2bGsTHkl3e4Xy5q39PeWLsTDm/y0C5echTcuv13eT6qzuJ/uvb81I53n0FuEPvIdKq60DJzMzyPk1Ct5MQQCBcBWg3AggggIBTBEIS8H77w2bpeukFEh1d+PCd2p5r3AveQmAyAjiJinLJwBt7mNsp3vnvE/L+22NkyK293R835X3UWjUxQV5++l5Zv2iCrJ0/ztznq48oC2CTqBoBBBBAAAEEEAiegAOOVDjiDFKHqyTEy45d+4odTW8n0MzY2BidBT01cl9x1gC4pAPro9T0cWUlbSMPAQQQQAABBBBAwL4CIQl4zznjZJn97mrz18yys3OMjt4jO+WNReapCMc0rGvymCCAgG0EaAgCCCCAAAJhKxCSgPeOfldK/bpHSd9BI6V1t0HSs/8jovfIrvv0W/nPvTdKqK7whu0o0nAEEEAAAQQQCJIAhwlHgZAEvDVrVJXFM56SoXf8U/SPUDSod7TceE1nmT35Mel+2YXh6EibEUAAAQQQQAABBGwqEJKAVy0SqyTILdddIWMeG2S+FPbAnX2k+Sl5jwLT7SQEwlmAtiOAAAIIIICAfQRCFvDah4CWIIAAAggggECABKgWAVsIEPDaYhhoBALBFUhPT5dff/015CkzMzO4HedoCCCAAAKOFCDgdeSw26zTNCfoAlFRUfL666+HPGk7gt55DogAAggg4DgBAl7HDTkdRgABBBCwqwDtQgCBwAgQ8AbGlVoRQAABBBBAAAEEbCJAwGuTgbDeDEoigAACCCCAAAII+CJAwOuLFmURQAABBOwjQEsQQAABiwIEvBahKIYAAggggAACCCAQngKRHvCG56jQagQQQAABBBBAAAG/CRDw+o2SihBAAAE7C9A2BBBAwLkCBLzOHXt6jgACCCCAAAIIOEKgUMDriB7TSQQQQAABBBBAAAFHCRDwOmq46SwCCFgUoBgCCCCAQAQJEPBG0GDSFQQQQAABBBBAwL8CkVEbAW9kjCO9QAABBBBAAAEEEChFgIC3FBiyEUDAugAlEUAAAQQQsLMAAa+dR4e2IYAAAggggEA4CdBWmwoQ8Np0YGgWAggggAACCCCAgH8ECHj940gtCFgXoCQCCCCAAAIIBFWAgDeo3BwMAQQQQAABBDwCzBEIlgABb7CkOQ4CCCCAAAIIIIBASAQIeEPCzkGtC1ASAQQQQAABBBConAABbxG/vw4cFk1FsiUpOUUOHEoqms06AggggAACwRHgKAggUGEBAl43XU5Orkx5Y7G0uWqwtO15t1ze9353bt7/lNQ0GTxsnLTuNkgu7jFY+gwaKfv2H8rbyBQBBBBAwJEC6enpkpWV5VPyQGVnZ4u/UmZmpqda5gggUIYAAa8b54VX5siMOcvljht7yLqFL8qiGaPcuXn/31ywSn7evF1Wzx0rnyyeKNFRUTJu6ry8jfab0iIEEEAAgQALuFwuycjIkIkTJ1pO06dPN63SQHnJkiXij7RixQqJjo429TJBAIGyBRwf8O7966BMe2uJ3Hf7tXJ9r0uldq3q0qBuba/astUbpHe3dlKvTi2pXi1R+vXuJPOXfCi5ubneMiwggAACCNhNILDt0Z8B+/fvF6vp4MGDpkG57k8Uk5KSxB8pOTnZ1MkEAQTKF3B8wPvN95uN0nc//i79Bo+SAUOflXdXfGTydLJl+25p2ri+Lpp0TKN6Zn44OcXMNe4lifsXAFI4nQfm5GWCAAIRIRBO7z20teI/KyPiZA1hJxwd8Kr7rr37dSZ1jq4pt/zzcjn3jJPloVFT5L1Vn7iDuFzRe3gT4uNMGZ3Ex8XqTFJS0sx8f1K6kDAIp3PgQHK6ObfNCWyDiV0+K9ErdjbgME3QoMAs2GBiFxc7meiw2MklOTVTSJFvoOcdqeICjg94le6EZo1k4I095JKLzzHz7pddKCvXfi4ul0sSqyRIesbfXwrwLCcmJuiucnSNeBIGYXUO1K4eb85tcwLbYOKyQRu0CS6XXVoi7vGRYP8r9Xgulz1cbNIMr5PLZR+X6omxQop8A+/Jx0KFBBwf8DZpWFd+27JTMrOyvYBZ7uXMrCyz3qxJfdm6Y7dZ1sm2nXt0JjWqJZo5EwQQQAABBBBAAAF7C1gPeO3djwq37uwWJ5mruJNnvCvZ2Tny9fe/ydIPPpWLWrYwdXZu31LmLFoje/YdlOQjqTJz7krp1aWt+wqMPX67N41kggACCCCAAAIIIFCqgOMDXr1SO37kYHltznI5o2N/6TtopPTt2VGuvbK9Qevb81I5vlkj6dB7iLTqOlAyM7NkcP9eZhsTBBBwpgC9RgABBBAILwHHB7w6XBec11w+XjxBVrz1nHy2dJIMu6efxOQ/27BqYoK8/PS9sn7RBFk7f5y8PXm4eUSZ7kdCAAEEEEAAAQQcLBA2XSfgzR8qDXAbN6hjbm/Izyo0q1m9qtSpXbNQHisIIIAAAggggAAC9hcg4LX/GNFCBMJbgNYjgAACCCAQYgEC3hAPAIdHAAEEEEAAAWcI0MvQCRDwhs6eIyOAAAIIIIAAAggEQYCANwjI15xR5gAAEABJREFUHAIB6wKURAABBBBAAAF/CxDw+luU+hBAAAEEEECg8gLUgIAfBQh4/YhJVQgggAACCCCAAAL2EyDgtd+Y0CLrApREAAEEEEAAAQTKFSDgLZeIAggggAACCNhdgPYhgEBZAgS8ZemwDQEEEEAAAQQQQCDsBQh4w34IrXeAkggggAACCCCAgBMFCHidOOr0GQEEEHC2AL1HAAGHCRDwOmzA6S4CCCCAAAIIIOA0AQLe0kacfAT8LJCeni5bt26V33//PaQpOTnZzz2jOgQQQAABBOwtQMBr7/GhdREkEBUVJXPnzpXXXnstpEkD7whipStBEOAQCCCAQLgLEPCG+wjSfgQQQAABBBBAAIEyBfwU8JZ5DDYigAACCCCAAAIIIBAyAQLekNFzYAQQiEgBOoUAAgggYDsBAl7bDQkNQgABBBBAAAEEwl/ATj0g4LXTaNAWBBBAAAEEEEAAAb8LEPD6nZQKEUDAugAlEUAAAQQQCLwAAW/gjTkCAggggAACCCBQtgBbAypAwBtQXipHAAEEEEAAAQQQCLUAAW+oR4DjI2BdgJIIIIAAAgggUAEBAt4KoLELAggggAACCIRSgGMj4JsAAa9vXpRGAAEEEEAAAQQQCDMBAl6LA5aUnCIHDiVZLE0xOwjQBgQQQAABBBBAQAUIeFUhP+3YtU9aXnGHjJk8Oz9HJCU1TQYPGyetuw2Si3sMlj6DRsq+/Ye821lAAAEEEEDA5gI0DwHHCxDw5p8CegV34ANjTICbn2Vmby5YJT9v3i6r546VTxZPlOioKBk3dZ7ZxgQBBBBAAAEEEEDA/gIEvO4xysrOln+PfFnOaXGydG5/vjvn7//LVm+Q3t3aSb06taR6tUTp17uTzF/yoeTm5v5dKBKW6AMCCCCAAAIIIBChAgS87oF9ZsJbkpGRJcPuucG9Vvj/lu27pWnj+t7MYxrVM8uHk1PMnAkCCCCAQGQJ0BsEEIg8AccHvLMWrpK1H38lL4y4S2JjYwqNsF7F1Xt4E+LjvPnxcbFmOSUlzcyPpGUJCQMr54CeT+akCfFE28EHFMUHQV2K55JjFxe7faZmJ5e0jGwhRb4B70aVE3B8wDv97WXSrEl9mTzzXXlmwizZ9NPvsv7zTTLljcXicrkksUqCpGdkepXzlkUSExNMXpS7DMklGJRvIPxDAIEKC7gqvGdk76guvP+6HPEzSPhXKQHHB7z9r7tCzjvzFKlVs5pJ0dFRold0a1RLNLAaDG/dsdss62Tbzj06E8/2KvHRQsLAyjngcumPJnP6hHTicrncv8yFtAm2PLjL5bJlu0LdKJerHJdQNzBEx3e57OMSFxslpMg3CNGpHjGHdXzA+88el8htN3T3plNPbCbntDhJNF9HuXP7ljJn0RrZs++gJB9JlZlzV0qvLm3dAYNLN5MQQAABBBBAAAEEbC4QjIDX5gRlN69vz0vl+GaNpEPvIdKq60DJzMySwf17lb0TWxFAAAEEEEAAAQRsI0DAW2Qoxjw2SO67/VpvbtXEBHn56Xtl/aIJsnb+OHl78nDziDJvARYQQAABywIURAABBBAIhQABr0X1mtWrSp3aNS2WphgCCCCAAAIIIIBAqQJB3kDAG2RwDocAAggggAACCCAQXAEC3uB6czQEELAuQEkEEEAAAQT8IkDA6xdGKkEAAQQQQAABBAIlQL2VFSDgrawg+yOAAAIIIIAAAgjYWoCA19bDQ+MQsC5ASQQQQAABBBAoWYCAt2QXciNEIC0tTXbv3i1bt24Nadq/f3+EiNINBBBAwPYCNBCBYgIEvMVIyIg0gVWrVsm0adNCmjTgjjRX+oMAAggggEC4CBDwhstI0U7/ClAbAggggAACCDhGgIDXMUNNRxFAAAEEECguQA4CThAg4HXCKNNHBBBAAAEEEEDAwQIEvA4efOtdpyQCCCCAAAIIIBC+AgS84Tt2tBwBBBBAINgCHA8BBMJSgIA3LIeNRiOAAAIIIIAAAghYFSDgtSplvRwlEUAAAQQQQAABBGwkQMBro8GgKQgggEBkCdAbBBBAwB4CBLz2GAdagQACCCCAAAIIIBAggZAHvAHqF9UigAACCCCAAAIIIGAECHgNAxMEEEAg5AI0AAEEEEAgQAIEvAGCpVoEEEAAAQSCIZCamipHjhwJaUpJSZH09PRgdJdjOELA/50k4PW/KTUigAACCCAQNIF9+/bJO++8E9K0fv16cblcQeszB0LAVwECXl/FKI8AArYQoBEIIIAAAghYFSDgtSpFOQQQQAABBBBAwH4CtMiCAAGvBSSKIIAAAggggAACCISvAAFv+I4dLUfAugAlEUAAAQQQcLAAAa+DB5+uI4AAAggg4DQB+utMAQJeZ447vUYAAQQQQAABBBwjQMCbP9SHko7Inn0H89eKz5KSU+TAoaTiG8iJQAG6hAACCCCAAAKRJOD4gHff/kPS5YYH5MLud0qH3kPkypselkUr1nvHOCU1TQYPGyetuw2Si3sMlj6DRoru4y3AAgIIIIAAApEqQL8QiBABxwe8OTm5ctXlF8uqOWPkk8UT5fIO58vjL8yQ1LQMM8RvLlglP2/eLqvnjjXbo6OiZNzUeWYbEwQQQAABBBBAAAH7Czg+4K1Xp5bcdkN3aVC3tlSvlihXdr5I9KruD7/8YUZv2eoN0rtbO9Fyur1f704yf8mHkpuba7YzEQgQQAABBBBAAAFbCzg+4C06Op999aPJOvaYhma+Zftuadq4vlnWyTGN6ulMDienmDkTBBBAAAEE8gSYIoCAXQUIeAuMzC+/b5dR49+QgTf2kNq1qpuruHq1NyE+zlsqPi7WLKekpJn5vkPpQrKvQVa2Pa7E6wcCdvlUIMfdGLu0RV9E7uboLOQJk5KHQM+XkrcEN9cu54mn1/Y6X+zxPpedI3L4SCYpQAaec495xQQIePPdduzaJ7ff/7xccvHZMvCmHibX5XJJYpUESc/INOs68SwnJiboqtSpGe9TonxwvWKiXWacQj1xn0rictmjLVHudrhc9miLjotdmuJyYaLjUTTp+VI0LxTrNhoe032Xy07niz3aEu2OKGpUjRVSYAzMicekwgJRFd4zgnb89fcdct0dI6RNqzPkyQcHSLS+avP716xJfdm6Y3f+msi2nXvMco1qiWbOBAEEEECgQgLshAACCARNwPEB70+/bZMetwyTC85tLgP6dpXdew+IXu31PHO3c/uWMmfRGvOM3uQjqTJz7krp1aWtba7WBe1M4UAIIIAAAggggECYCtg74A0C6uYtO81R3lv1iVze93657LqhJo2eMMvk9+15qRzfrJF5Rm+rrgMlMzNLBvfvZbYxQQABBBBAAAEEELC/gOMD3isuaSWb1kwvlp5++DYzelUTE+Tlp++V9YsmyNr54+TtycPNI8rMRiYIIIBAkAQ4DAIIIIBAxQUcH/BapatZvarUqV3TanHKIYAAAggggAACCPhfoEI1EvBWiI2dEEAAAQQQQAABBMJFgIA3XEaKdiKAgHUBSiKAAAIIIFBAgIC3AAaLCCCAAAIIIIBAJAnQlzwBAt48B6YIIIAAAggggAACESpAwBuhA0u3ELAuQEkEEEAAAQQiW4CAN7LHl94hgAACCCCAgFUBykWsAAFvxA4tHUMAAQQQQAABBBBQAQJeVSAhYF2AkggggAACCCAQZgIEvGE2YDQXAQQQQAABewjQCgTCR4CAN3zGipYigAACCCCAAAIIVECAgLcCaOxiXYCSCCCAAALOEcjIyJDU1NSQpvT0dMnKynIOOj21JEDAa4mJQggggAACCFRKIOJ3joqKkk2bNsmCBQtCmr755htxuVwR700HfRMg4PXNi9IIIIAAAggggAACYSZAwGunAaMtCCCAAAIIIIAAAn4XIOD1OykVIoAAAghUVoD9EUAAAX8KEPD6U5O6EEAAAQQQQAABBGwnEMYBr+0saRACCCCAAAIIIICADQUIeG04KDQJAQQQ8EmAwggggAACZQoQ8JbJw0YEEEAAAQQQQACBcBEorZ0EvKXJkF8pAX3wtx1SpTrBzggggAACCCAQEQIEvBExjPbqRG5urnz77bfyxhtvhDR9/vnnom2xlw6tCb0ALUAAAQQQcJoAAa/TRjxI/T148KBs3bo1pGn//v1B6i2HQQABBBBAIAwFHNRkAl4HDTZdRQABBBBAAAEEnChAwBtBo56RkWE+wteP8UOZMjMzI0jV8V0BAAEEEEAAgbAXIOAN+yH8uwNbtmyRESNGhDRNmzZNXC7X341iCQEEEEAAgYgQoBPhLEDAa3H0kpJT5MChJIulKYYAAggggAACoRLQTzzT0tIkNTU15ClUBhy3sAABb2GPYmspqWkyeNg4ad1tkFzcY7D0GTRS9u0/VKwcGQioAAkBBBBAIPQC0dHRsn79elmwYEFI0+7du0OPQQuMAAGvYSh98uaCVfLz5u2yeu5Y+WTxRImOipJxU+eVvgNbEEAAAQQQQAABBGwlQMBbznAsW71BendrJ/Xq1JLq1RKlX+9OMn/Jh+bLYeXsymYEEEAAAQQQQAABGwgQ8JYzCFu275amjet7Sx3TqJ5ZPpycYuZJSUmycePGkKYvv/xSsrKyTHvCakJjEUAAAQQQQACBIAgQ8JaBrI/20nt4E+LjvKXi42LNckpKmplHR8fIoUOHQpoOHz4ser9SzZo1pV27diFNZ599tuTkihx//PEhbYc6nHzyyeJuirRo0SLkbalbN+8XpQsuuCDkbYmLyzuf1SjUSVwuSUioEnKTVq1auT+1EWnUqFHI29K8eXPTllNPPTXkbTn22GPNa+i8884LeVuqVatm3nNDfc7q8aOioiUmJsYnE33tawdiYmPk9NNPL5YqknfaaadplaI2Fdnfn/voe76+9zdo0MAvfatM2/R1rDDHHXdcyNtSo0YNSc3IlsNHMiudtE+kigsQ8JZh53K5JLFKgqRn/P1cWc9yYmKC2TMlM0panHNhSFPzs1rLX4czJCq+ZkjboQ5NT2guSak5UuPoxiFvy9ENjpWUjChp2PTkkLclvtrRciglR0467ZyQtyU9J860Rccr1OlAUqZkuRJCbnLiP86Wg0eypEqNeiFvS4NjThJ9X6nT8LiQt6V67UaSnCZy7EktQt6W3Jhqsu9Qesjboa+ZpLRc9xhF+9SW5h2ukLobvpb6qz+Wk2++wy/phH63SsZ5F0uVjl39Ul9l2tWo9w2S06qtHNX16pC35eju10hO6/bS8OrrQ96WxEu7iat1G4lr067SSfhXKQEC3nL4mjWpL1t3/P0ty20795g9alRLNPM6NeMlOInj4Mw5wDnAOcA5wDng1HPABB1MKixAwFsOXef2LWXOojWyZ99BST6SKjPnrpReXdq6P4l1lbMnmxFAAAEEAiJApQgggICPAgS85YD17XmpHN+skXToPURadR0omZlZMrh/r3L2YnOwBXJycmXX3v2SlXI8QfcAABAASURBVJ1t6dC+lrdUKYUQ8IMA56YfEKnCrwL67PnUtAzLdfpa3nLFFESgEgKRGvBWgqTwrlUTE+Tlp++V9YsmyNr54+TtycPNI8oKl2ItlAJrP/7a/DLS8Zr75MyO/yez3Vfky2pPWeVXrdsozdvfXCx57t0uq162IVBZgbLOzcrWzf4I+Cqgt/N1ueEBadfrHjnv8tvk0WemSWZW6RcVyiq//2BSsfdVfa/9ZOP3vjaL8ghUSICA1yJbzepVpU7tmhZLUyxYAnrVYejjL8td/XvK16telXEjB8uI56fL9j/3ltiE8srnSq75ouKS10dLwRQXG1NifWQi4C+B8s7Nih+HPRGomMATY2fKqSc2lc+XvSKLZzwl+lz6ZR98WmplZZXXpx7pjpNG/6vQe+uZp52o2SQEAi5AwBtwYg4QSIENX/4g+ui4Pj0ukZjoaLm0zbmiXzRc+/FXJR7WSvmE+FhTh9bjSS6Xq8T6yETAXwJWzk1/HYt6EChP4FDSEfnos++kX+/LpEpCnBzXtKFcdflFsmLtZyXuarV8k4Z1Cr2/at0lVkgmAn4WMAGvn+ukOgSCJrB73wHz5hkXF+s95gnNGsmuPQe86wUXrJTf7/7o7eGnpsiIMa/Je6s+sXxfcMHjsIyArwJWzk1f66Q8AhUV2PfXQbNrk4Z1zVwn+keY/tyzXxeLJavlx0yeLY+MflVmzFkuGiQXq4gMBAIkQMAbIFiqrbzAohXrZcobi0tMeuVBj3DYfRVCn5Wsy54UHx8nSfl/Cc+T55mXV75+3dpyy3VXmKsZus/9IyfJ6Jfe1EWSMwRC1svyzs2QNYwDO1LgcP57aMGLCfreuv/g4RI9yisf774o0bdnRznjtBOkdq3q5n395nuekowCz7kvsWIyEfCTAAGvnyCpxv8C+ga63321taSktzHoEWtUr2puadBlT0pPz5Dq+c9J9uR55uWVb3HqcTL0jn/Krdd3k+H33SQj7+8vby5YxVVeDyDzgAmUd24G7MBUjEAJAp5nzeuTiTyb9b21dq0antVC8/LKV6taRYbd08+8t953+7UyY/zD8vPm7fLjr1sL1cOKnQQiqy0EvJE1nhHVm+t7XSoP3NmnxNSp7Xmmr/XrHCVbtu82j4szGe6Jvok2qHeUe6n4f1/L1z06r56sMr6ZXPwo5CDgu4Cv56bvR2APBKwL1Dm6lins+WNLuvLHtl3SsF5tXSyWfC1fz/3erZWkui9Q6JyEQKAFCHgDLUz9ARVoedappv5ZC/Ouwr6/7gvzhIZ2F5xl8j/76kfpfetwExRrRnnl9WruF9/8LPqNeX2u7yuvL5JWZ/9DEuLjdHdSEQFW/SdQ3rnpvyNREwLlC+iTiS44r7n5Y0v6fvj71j/l3RXr5bJ2Lc3OetuYvrcuzX9qQ3nl1378tSxfs8Hct6uf0I2bOs88EUefAmEqZIJAgAUIeAMMTPWBFUisEi8vPnmPjJ4wyzyD955HX5RHhvQTzxctklNS5Ydftkha/lWE8srv2vOX3Hj3KPPMSX2urz5K5/H7+we2E9SOgFugvHPTXYT/CARVYNjdN8imn34374fdbnzIHeyeJ5d3ON+0Qf9Air63HjycbNZ1Ulb5jMxMeWT0NLmw+53S8oo7ZOkHn8iLT9wtGijrvhGQ6ILNBQh4bT5ANK98gUsuOlu+WTVNVrz1nHy1cqr0uaqjd6cOF54tm9ZMl1NOOMabV1Z5vbfsi+WvyLI3n5GP3nlJXn9pmDd49lbAAgIBEijr3AzQIakWgVIF9FFky2c9Kx/MeUE2LJkkTz44QGLzn0les0ZV895a8P22rPJ6G9rHiyeYurS+DxeMl9bnnlbqsdmAgL8FCHj9LUp9IRGIjo6Sxg3qeN+My2tEWeX19oVjGtWTWjWrlVeNb9spjYAFgbLOTQu7UwQBvwvUr3uU6F8dtVpxaeX1Wem6TZPLxbPNrXpSzj8CBLz+caQWBBBAAAEEELAoQDEEgi1AwBtscY6HAAIIIIAAAgggEFQBAt6gcnMw6wKURAABBBBAAAEE/CNAwOsfR2pBAAEEEEAgMALUigAClRYg4K00IRUggAACCCCAAAII2FmAgNfOo2O9bZREAAEEEEAAAQQQKEWAgLcUGLIRQAABBMJRgDYjgAACxQUIeIubkIMAAggggAACCCAQQQKODHgjaPzoCgIIIIAAAggggEA5AgS85QCxGQEEEIhgAbqGAAIIOEKAgNcRw0wnEUAAAQQQQAAB5wqUH/A614aeI4AAAggggAACCESAAAFvBAwiXUAAgeAIcBQEEEAAgfAUIOANz3Gj1QgggAACCCCAQKgEwu64BLxhN2Q0GAEEfBW48+GxMnfxWrNbSmq6LFi6Tn75fbtZ18mOXftk4vSF0rP/I9L/3tGaJSXlmQ0FJl9+94ssWrG+QA6LCCCAAAJ2FCDgteOo0CYEIkHARn345vvfZPfe/aZFhw4nyyOjX5X1n28y6zp58MlXZPH7H0uXjq2l1TmnaZaUlGc2FJi8s+wjeXDUKwVyWEQAAQQQsKMAAa8dR4U2IYBAwATq160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Null Distribution\"},\"xaxis\":{\"title\":{\"text\":\"diff in props\"}},\"yaxis\":{\"title\":{\"text\":\"count\"}},\"shapes\":[{\"line\":{\"color\":\"#d62728\",\"dash\":\"solid\",\"width\":2},\"type\":\"line\",\"x0\":0.03399999999999992,\"x1\":0.03399999999999992,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"},{\"fillcolor\":\"#d62728\",\"line\":{\"width\":0},\"opacity\":0.3,\"type\":\"rect\",\"x0\":0.03399999999999992,\"x1\":0.07509999999999994,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"}]},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -342,7 +342,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5c42605",
+   "id": "aae5023a",
    "metadata": {},
    "source": [
     "## Simulation vs. theory overlays"
@@ -351,7 +351,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "4a810cf7",
+   "id": "10e162e2",
    "metadata": {},
    "outputs": [
     {
@@ -359,7 +359,7 @@
       "image/png": 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",
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Distribution\"},\"xaxis\":{\"title\":{\"text\":\"mean\"}},\"yaxis\":{\"title\":{\"text\":\"density\"}},\"bargap\":0.02,\"showlegend\":false},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -391,7 +391,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "b83c75b5",
+   "id": "05e562ab",
    "metadata": {},
    "outputs": [
     {
@@ -399,7 +399,7 @@
       "image/png": 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Bootstrap Distribution\"},\"xaxis\":{\"title\":{\"text\":\"mean\"}},\"yaxis\":{\"title\":{\"text\":\"density\"}},\"bargap\":0.02,\"showlegend\":false},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -436,7 +436,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "962bd275",
+   "id": "cf3b93a4",
    "metadata": {},
    "source": [
     "## Faceted regression-coefficient plots\n",
@@ -447,7 +447,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "e552f09e",
+   "id": "5d545990",
    "metadata": {},
    "outputs": [
     {
@@ -455,7 +455,7 @@
       "image/png": 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TDtusXdrrAoBOjYB8BwcDX1a2hbEHDwDj9FLShVcHAqbTmgVU4zcCbPNe2dLShwN6QPe5gy7rAD14saEdzw8Csn7hVe5GlwE8Aou3TTMHAqjaHSEzGs8AdH5u7ZxS8wKnw2XdH9rvAddzez81TtMCClr+wyYHAwBYHKHig3MPAvkcDE\\u002fDcwsDiZxRU\\u002fzW9wDoroaYeE7fAG\\u002flwoe8Vo8ChSeYn6vPKwJ8TszbZvMDAupmlCw+1sMAevQHXjsO7wI0bi8QoTMLAEoZPCEvqtMBq9yiLpw6YQGC8Pt6iD7TAzttoueYcvcD4Ij6HPti+wFMcm69r28fAWuzOx26ms8DAOSRz8Z6nwKZQ9RUbVrjAmCRkrSvTwMCk16s6KBPCwLx9r8VWdr3ANrhzTPuRtcA+mGRuVmu7wPJDW204XITA4psiyHQIucCb+0qkHRPAwAy1IxBKdrvA51I6JE1kw8DGejnOq13HwF+fXR6hpq3A+Az2Ga5WwsAsKsQ6QXXHwHzZ5W5QjrPA+zBRwMyIwsBt8eWW6g+lwILamDwj57LAL2vI3PDVyMDkEdQ6VRSxwE\\u002fSjqWQvcXANWVJ20BbuMDeNhwW8wHAwMooqDnuk8TAGE931o+8pcDB0MLHGum3wHk10PJ6ibTAUhXyFmP9wsD7r+Fggdi0wAfJakXF0MfA0RBwMxSVw8Aryj0FdxPIwFIfTU6zHsrAeMosMmVYuMASW2S6adipwN7UoZJntL\\u002fAlBfJtnumwcA8BmJPUbrMwPjD1M1BS8bAvywNR+crvcC7krb6ZrG9wAc5qD7l7MbAoVWA5cdlwcC+hC541au4wK7XwMW9kq\\u002fAFMzZZIixysBIK8J1TKm1wP3378fMc7nApywKli7JwMDPg+kCH2m2wHjDXk6QcKfAy42di2Kbq8DIjxxaYWbGwF7I+OBscrzACH6aBr\\u002fducDGRJFjLjXGwF6XsnHabpXAG5L1XMr8ssBwJSE6C3e8wDSX3YwqmsbAQc4bUBs4vsBO0udWEwuxwC5uLMvd\\u002fMHAK+mO6dyIt8A6rf5KFh7DwBYPnmnWA8HAHipKAfIguMDtAXa\\u002fj8++wDOJiNJ2w8nASZkKe54KsMC6OhLzXcm\\u002fwJKFTWc0psLAN876HuYexsAiidiQ7y7CwIEeb0u8IsTAi\\u002fWqJ1xRtMBLy3WiBtvJwN4AQLlyGsPAwL1fkdVwvsAeYOJWHz+zwN+oparJJ7jAX9lzoW\\u002f7usBuM1sDR7C\\u002fwAKUwBFAw6nAWQtNNjg7uMDjPSoI3Qa4wHAjBuiC1bbAyNRhNnM0w8D2DfnA3EykwAbVjAaF3rvAOAXMrSlwucD9eDAm82nDwFMOiY9737jAOI32yHFZusD+zHxsUdS\\u002fwLx5AV94N8bAxdKcD+cOuMCUqas8PbvFwBMMDTYf5avAuQmb+OQZxMAA1A+FM8S8wEA8uLkAVMLAkuj0L1dNvcAsVf5W5H3JwIMDIK0Tr77Awo12llZ7wcDcTLKRHETGwBKNiRaNuLTAXPta7n62ysAXkX6MX2yWQLi5NWV4FL7Au6FhACCsssCeOyBw2ffBwCDek7FPJL3AgOL4OCX0tcAOBrOaHE\\u002fDwBv9OLUdg8bAYeb7mWn0w8ACF0FEVs7EwNodizbiwMfAz\\u002fOh3m2fwsCYypvU18a9wL4EU0xOe7PAnUoZBNkRu8Cu2h0NyGi4wDTnoZXQrcPAYOr6N8E4tMBp+K4mVPDFwPhYosLy+J3A3bkiME+qwMAwN9dgC0OqwDr74p8P08LAmj6UScMbwMB+AqYK5RfBwF6tv3V+BcrAJ15oi\\u002fnyxMD4uxZFZue4wGoqQBYjY8DA+a26no3VwsDvJDHGf4\\u002fGwJj5hg\\u002frMcnACNp354XsnsBu5DTmz8u8wCa3y6DEt8LABWkwBIb9w8BWhEl7mD6SwO0EnwkiybLAkJW\\u002ftSSBwcD6jlr8coy\\u002fwBaOU7ZyrLjAEZt5E5OEtsDrkPPAVl\\u002fAwCEu75ity8bAxPfBhcJGuMA\\u002f9wI2+BSwwFG5Cc1RUa3AtGjtH306wMBu+u+rSD3CwAKjE+J7arrAA8TY8hKkusCKlK1EC3S+wPDzMVRo973AVVzxgJJhxMAswgnd9Zy5wMqd29c+fsXALzgVlXiCo8C6KbcBPTLHwGxnrxia5cLAjK\\u002fBH8QtwcBF3mI9wxzKwE3JDSslAMDAmjkqAvJPxcAt\\u002fX1Jqd3LwIYNyI1DS73ATB3+l0TosMD0VZB8CS\\u002fHwKsp3Hm42K7Ax+80R5vMwsDakpxUln\\u002fCwE4lEJZ9O8LAqAUlECXGfsADAosZScfBwMNqGmDjdMzAygxRm0aCucCigCA0WsutwBTNyF4hvMrAsFTPBodrucBpb90Ow3G1wEIMwDoEoMTAeUPHF+PYz8CPO3Lie8e5wI8L8Np7E7zAbh5BjmROoMAnWDL7W1WzwP7ygY0dz8PAlKkFXlBIuMD2z2eICie\\u002fwCwsXjiU1r7ABoifjpbHtMBeYXky5Fe+wAqwbF9nQ7PAfmkSe0Gut8BKGmXAmACzwAn5vUKWAMjAC7B9T8OjlsBZMwmCapS0wMO0xfezw77ANDFF7mhGz8AHLwv8AzHBwG9V0wJsybjAOTPFA7Hux8AINe4wtDO4wOhJ7+jtwGnAUtOK7+pvsMCkWjaavw7EwKYR1RJDtcHAHMyV9ShewMAzQ6C4s1fFwMFVFQwav7bANMQxVJsIucB57Nv6cFvCwM61jhox0YPALba\\u002faGUxw8CxbwMsb5jDwKVqD0xyhsLA3EtG8XfntsBl464Ir5S4wBOC+QgzycHA6MGmoIF4isDCDMnOyQq3wHCzXnIv9sDADNB7rVuFusDae5CMikG\\u002fwENTW+guAsHA2p533m6VmMAoonUN8nbDwF\\u002fdungTLMTACY\\u002fgzL2R0cBldla3aEbBwFgaVk8gD37ASx\\u002fy\\u002feluxMAUoVC4mIaywCepU2+pEMXAh2NfJkamtsAFRBEb75q5wPBTn8Chy7bABZ2p3yrwssDDIPk4M8y4wIsw5Wr1BsbAHmJlLhfMmcA2FpSsNyi5wCPaRb0fY8LAfnLWvh7IpsBKFkzVRku8wLhsIvUJ28jAZcoXfbvswMBWKumlWfe\\u002fwDBvvFoOQMzA1BQftskSxcCK6nbMU0i1wAckb0fyYMHAwHR9H6jpwsDK4cVLiNnBwOxKtWpyWMHAzhX0GtUBy8BC5L7PfFDDwOD9A\\u002fA6drfA8GL+2w06ucDKQGC1ra2ywIDYZFk8WcHAgqmJTvnat8AYlq5MqOCswHZHQ+kdNLbAgcBxBUk0tcCMhTJJJuS+wFvNdi3Fcb\\u002fASIFBQB2KxMDPPKxWqMuywPpPAYtObr3AosmW5gJPqMDPTa9BaG69wJA0daF0MLnAPNZKoVUdtsCoJFBgzXazwByE\\u002f7PiKL7AjABsxOCrycBpwqoGX4+3wBytuPa9G7nAVwngzLNtu8AxAdy8AkPEwDHQ4+Q\\u002fesPAgWHFlcRdqcBZZpMoGYShwCLde+deD7PAbjIHC0Y+vcCqnqyYyTK8wK7r60xFDMDAS207pLpRwcDyh2QxlXW4wKJdJ6zagMHAxvfSbKHksMDXp0S7vGulwBWmEAjURcjApoUw12mHvsCVm0d969+zwFZbBCcOSq\\u002fAHwExm0NywsBRg6ZTAXyuwB4EedHDTcTANNx6xk6OpMAv0t38bWDIwL+GxK4Vo77As+Szeyp2vsDqFQaaUPXFwJ8sWbwB6arARrsdmgtrsMBylsx\\u002fJZjBwGCIQsJ09K7AuCLZwrMPxsBxkPkwZC26wK2S74TRsMbA1yZFeJGNvcAWWi3lqHi7wE+8SEW458nAbi\\u002fdSZoIw8A5AgRzx8S5wJLgdnC9VsPA\\u002fuZMuxA0ssDQUpY5O\\u002fGkwPCjO2L03L\\u002fAG9I\\u002f5kReqsCv9oyJnp\\u002fEwNdw4RspYMHAIPsiU680wsCtwtDBp9iiwKNEyXew+cjAvf1pInt+wcCE7xFQAcO5wMh7s3dUsbDA\\u002fEEPj4xvrsDaBpcEftC8wERvpnVCtcLAFH+Tu4HEuMBUwwIYgKGUwECBUIxXgsLAyFQkFqkQtMB3Xl0qek7AwB8WMWgpY8DAsfbBNSCspsCM1JTKrQTBwIzIGHvyIrjAVPFM5\\u002foimsA2zlAxlgK7wJINhyihbLnAMdfkWmD4tsDaxha8GDa9wO6gQRHDobDAN3JCf2cSqsBR\\u002fjPieh65wA5j5O+6eMLA8NedKe9Fq8AcHa8iywejwI4\\u002fqB6SirDAx8yIDA7twsAIXd+Q0cy\\u002fwFTGJDRkj7bAHBXH7iz4wMDetdiT5Ri0wDZKNak6mb\\u002fAp0f+SavTusCnJXgYbTuywG7WgwnSO7fAVIWBBGmRuMAwCKRtoFzDwAYomp\\u002fPG7bAgIcVGuCavcARbn3rInq6wJy11J8q6bTAYlb1ajCYtMAZMR7rkALLwJmtKDhUHrjABK\\u002fe4plKtsBmW2gm50+8wIB\\u002fz\\u002fnO+qrAAF6mCR3vxsAbwAMugEGzwKZZkw4XscLA6XjU+So5wsBi7v+tCMCcwFexwWn9P7\\u002fA1wLPEZB2wcB1YegbDxumwAw1MvQIZLrAXHfO5XuotcAoQvJxKsLAwOgdNP9YV7TADJYHiD9cu8BNbrCVxwvGwGm3nfGLVsbAQ3ChnTtyxMCY+Lx7BFKzwNCk6a\\u002f3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TDtusXdrrAoBOjYB8BwcDX1a2hbEHDwDj9FLShVcHAqbTmgVU4zcCbPNe2dLShwN6QPe5gy7rAD14saEdzw8Csn7hVe5GlwE8Aou3TTMHAqjaHSEzGs8AdH5u7ZxS8wKnw2XdH9rvAddzez81TtMCClr+wyYHAwBYHKHig3MPAvkcDE\\u002fDcwsDiZxRU\\u002fzW9wDoroaYeE7fAG\\u002flwoe8Vo8ChSeYn6vPKwJ8TszbZvMDAupmlCw+1sMAevQHXjsO7wI0bi8QoTMLAEoZPCEvqtMBq9yiLpw6YQGC8Pt6iD7TAzttoueYcvcD4Ij6HPti+wFMcm69r28fAWuzOx26ms8DAOSRz8Z6nwKZQ9RUbVrjAmCRkrSvTwMCk16s6KBPCwLx9r8VWdr3ANrhzTPuRtcA+mGRuVmu7wPJDW204XITA4psiyHQIucCb+0qkHRPAwAy1IxBKdrvA51I6JE1kw8DGejnOq13HwF+fXR6hpq3A+Az2Ga5WwsAsKsQ6QXXHwHzZ5W5QjrPA+zBRwMyIwsBt8eWW6g+lwILamDwj57LAL2vI3PDVyMDkEdQ6VRSxwE\\u002fSjqWQvcXANWVJ20BbuMDeNhwW8wHAwMooqDnuk8TAGE931o+8pcDB0MLHGum3wHk10PJ6ibTAUhXyFmP9wsD7r+Fggdi0wAfJakXF0MfA0RBwMxSVw8Aryj0FdxPIwFIfTU6zHsrAeMosMmVYuMASW2S6adipwN7UoZJntL\\u002fAlBfJtnumwcA8BmJPUbrMwPjD1M1BS8bAvywNR+crvcC7krb6ZrG9wAc5qD7l7MbAoVWA5cdlwcC+hC541au4wK7XwMW9kq\\u002fAFMzZZIixysBIK8J1TKm1wP3378fMc7nApywKli7JwMDPg+kCH2m2wHjDXk6QcKfAy42di2Kbq8DIjxxaYWbGwF7I+OBscrzACH6aBr\\u002fducDGRJFjLjXGwF6XsnHabpXAG5L1XMr8ssBwJSE6C3e8wDSX3YwqmsbAQc4bUBs4vsBO0udWEwuxwC5uLMvd\\u002fMHAK+mO6dyIt8A6rf5KFh7DwBYPnmnWA8HAHipKAfIguMDtAXa\\u002fj8++wDOJiNJ2w8nASZkKe54KsMC6OhLzXcm\\u002fwJKFTWc0psLAN876HuYexsAiidiQ7y7CwIEeb0u8IsTAi\\u002fWqJ1xRtMBLy3WiBtvJwN4AQLlyGsPAwL1fkdVwvsAeYOJWHz+zwN+oparJJ7jAX9lzoW\\u002f7usBuM1sDR7C\\u002fwAKUwBFAw6nAWQtNNjg7uMDjPSoI3Qa4wHAjBuiC1bbAyNRhNnM0w8D2DfnA3EykwAbVjAaF3rvAOAXMrSlwucD9eDAm82nDwFMOiY9737jAOI32yHFZusD+zHxsUdS\\u002fwLx5AV94N8bAxdKcD+cOuMCUqas8PbvFwBMMDTYf5avAuQmb+OQZxMAA1A+FM8S8wEA8uLkAVMLAkuj0L1dNvcAsVf5W5H3JwIMDIK0Tr77Awo12llZ7wcDcTLKRHETGwBKNiRaNuLTAXPta7n62ysAXkX6MX2yWQLi5NWV4FL7Au6FhACCsssCeOyBw2ffBwCDek7FPJL3AgOL4OCX0tcAOBrOaHE\\u002fDwBv9OLUdg8bAYeb7mWn0w8ACF0FEVs7EwNodizbiwMfAz\\u002fOh3m2fwsCYypvU18a9wL4EU0xOe7PAnUoZBNkRu8Cu2h0NyGi4wDTnoZXQrcPAYOr6N8E4tMBp+K4mVPDFwPhYosLy+J3A3bkiME+qwMAwN9dgC0OqwDr74p8P08LAmj6UScMbwMB+AqYK5RfBwF6tv3V+BcrAJ15oi\\u002fnyxMD4uxZFZue4wGoqQBYjY8DA+a26no3VwsDvJDHGf4\\u002fGwJj5hg\\u002frMcnACNp354XsnsBu5DTmz8u8wCa3y6DEt8LABWkwBIb9w8BWhEl7mD6SwO0EnwkiybLAkJW\\u002ftSSBwcD6jlr8coy\\u002fwBaOU7ZyrLjAEZt5E5OEtsDrkPPAVl\\u002fAwCEu75ity8bAxPfBhcJGuMA\\u002f9wI2+BSwwFG5Cc1RUa3AtGjtH306wMBu+u+rSD3CwAKjE+J7arrAA8TY8hKkusCKlK1EC3S+wPDzMVRo973AVVzxgJJhxMAswgnd9Zy5wMqd29c+fsXALzgVlXiCo8C6KbcBPTLHwGxnrxia5cLAjK\\u002fBH8QtwcBF3mI9wxzKwE3JDSslAMDAmjkqAvJPxcAt\\u002fX1Jqd3LwIYNyI1DS73ATB3+l0TosMD0VZB8CS\\u002fHwKsp3Hm42K7Ax+80R5vMwsDakpxUln\\u002fCwE4lEJZ9O8LAqAUlECXGfsADAosZScfBwMNqGmDjdMzAygxRm0aCucCigCA0WsutwBTNyF4hvMrAsFTPBodrucBpb90Ow3G1wEIMwDoEoMTAeUPHF+PYz8CPO3Lie8e5wI8L8Np7E7zAbh5BjmROoMAnWDL7W1WzwP7ygY0dz8PAlKkFXlBIuMD2z2eICie\\u002fwCwsXjiU1r7ABoifjpbHtMBeYXky5Fe+wAqwbF9nQ7PAfmkSe0Gut8BKGmXAmACzwAn5vUKWAMjAC7B9T8OjlsBZMwmCapS0wMO0xfezw77ANDFF7mhGz8AHLwv8AzHBwG9V0wJsybjAOTPFA7Hux8AINe4wtDO4wOhJ7+jtwGnAUtOK7+pvsMCkWjaavw7EwKYR1RJDtcHAHMyV9ShewMAzQ6C4s1fFwMFVFQwav7bANMQxVJsIucB57Nv6cFvCwM61jhox0YPALba\\u002faGUxw8CxbwMsb5jDwKVqD0xyhsLA3EtG8XfntsBl464Ir5S4wBOC+QgzycHA6MGmoIF4isDCDMnOyQq3wHCzXnIv9sDADNB7rVuFusDae5CMikG\\u002fwENTW+guAsHA2p533m6VmMAoonUN8nbDwF\\u002fdungTLMTACY\\u002fgzL2R0cBldla3aEbBwFgaVk8gD37ASx\\u002fy\\u002feluxMAUoVC4mIaywCepU2+pEMXAh2NfJkamtsAFRBEb75q5wPBTn8Chy7bABZ2p3yrwssDDIPk4M8y4wIsw5Wr1BsbAHmJlLhfMmcA2FpSsNyi5wCPaRb0fY8LAfnLWvh7IpsBKFkzVRku8wLhsIvUJ28jAZcoXfbvswMBWKumlWfe\\u002fwDBvvFoOQMzA1BQftskSxcCK6nbMU0i1wAckb0fyYMHAwHR9H6jpwsDK4cVLiNnBwOxKtWpyWMHAzhX0GtUBy8BC5L7PfFDDwOD9A\\u002fA6drfA8GL+2w06ucDKQGC1ra2ywIDYZFk8WcHAgqmJTvnat8AYlq5MqOCswHZHQ+kdNLbAgcBxBUk0tcCMhTJJJuS+wFvNdi3Fcb\\u002fASIFBQB2KxMDPPKxWqMuywPpPAYtObr3AosmW5gJPqMDPTa9BaG69wJA0daF0MLnAPNZKoVUdtsCoJFBgzXazwByE\\u002f7PiKL7AjABsxOCrycBpwqoGX4+3wBytuPa9G7nAVwngzLNtu8AxAdy8AkPEwDHQ4+Q\\u002fesPAgWHFlcRdqcBZZpMoGYShwCLde+deD7PAbjIHC0Y+vcCqnqyYyTK8wK7r60xFDMDAS207pLpRwcDyh2QxlXW4wKJdJ6zagMHAxvfSbKHksMDXp0S7vGulwBWmEAjURcjApoUw12mHvsCVm0d969+zwFZbBCcOSq\\u002fAHwExm0NywsBRg6ZTAXyuwB4EedHDTcTANNx6xk6OpMAv0t38bWDIwL+GxK4Vo77As+Szeyp2vsDqFQaaUPXFwJ8sWbwB6arARrsdmgtrsMBylsx\\u002fJZjBwGCIQsJ09K7AuCLZwrMPxsBxkPkwZC26wK2S74TRsMbA1yZFeJGNvcAWWi3lqHi7wE+8SEW458nAbi\\u002fdSZoIw8A5AgRzx8S5wJLgdnC9VsPA\\u002fuZMuxA0ssDQUpY5O\\u002fGkwPCjO2L03L\\u002fAG9I\\u002f5kReqsCv9oyJnp\\u002fEwNdw4RspYMHAIPsiU680wsCtwtDBp9iiwKNEyXew+cjAvf1pInt+wcCE7xFQAcO5wMh7s3dUsbDA\\u002fEEPj4xvrsDaBpcEftC8wERvpnVCtcLAFH+Tu4HEuMBUwwIYgKGUwECBUIxXgsLAyFQkFqkQtMB3Xl0qek7AwB8WMWgpY8DAsfbBNSCspsCM1JTKrQTBwIzIGHvyIrjAVPFM5\\u002foimsA2zlAxlgK7wJINhyihbLnAMdfkWmD4tsDaxha8GDa9wO6gQRHDobDAN3JCf2cSqsBR\\u002fjPieh65wA5j5O+6eMLA8NedKe9Fq8AcHa8iywejwI4\\u002fqB6SirDAx8yIDA7twsAIXd+Q0cy\\u002fwFTGJDRkj7bAHBXH7iz4wMDetdiT5Ri0wDZKNak6mb\\u002fAp0f+SavTusCnJXgYbTuywG7WgwnSO7fAVIWBBGmRuMAwCKRtoFzDwAYomp\\u002fPG7bAgIcVGuCavcARbn3rInq6wJy11J8q6bTAYlb1ajCYtMAZMR7rkALLwJmtKDhUHrjABK\\u002fe4plKtsBmW2gm50+8wIB\\u002fz\\u002fnO+qrAAF6mCR3vxsAbwAMugEGzwKZZkw4XscLA6XjU+So5wsBi7v+tCMCcwFexwWn9P7\\u002fA1wLPEZB2wcB1YegbDxumwAw1MvQIZLrAXHfO5XuotcAoQvJxKsLAwOgdNP9YV7TADJYHiD9cu8BNbrCVxwvGwGm3nfGLVsbAQ3ChnTtyxMCY+Lx7BFKzwNCk6a\\u002f3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       "text/plain": [
        "Figure({\n",
@@ -617,7 +617,7 @@
   },
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+   "id": "8aee3e6c",
    "metadata": {},
    "source": [
     "## Model plots"
@@ -626,7 +626,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "507fa54d",
+   "id": "72fded5b",
    "metadata": {},
    "outputs": [
     {
@@ -647,7 +647,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "8ea850d6",
+   "id": "73adf7d4",
    "metadata": {},
    "outputs": [
     {
@@ -667,7 +667,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "7cef70cc",
+   "id": "c480f2b9",
    "metadata": {},
    "outputs": [
     {
@@ -687,7 +687,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0fa833e7",
+   "id": "f5333e82",
    "metadata": {},
    "source": [
     "## Saving figures\n",
diff --git a/doc/_build/jupyter_execute/guides/regression.ipynb b/doc/_build/jupyter_execute/guides/regression.ipynb
index 1cf4e0c..5845d88 100644
--- a/doc/_build/jupyter_execute/guides/regression.ipynb
+++ b/doc/_build/jupyter_execute/guides/regression.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "b7ed0f10",
+   "id": "fe636a9d",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "42afbe20",
+   "id": "b2009f68",
    "metadata": {},
    "source": [
     "# Regression\n",
@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "027aca93",
+   "id": "863f67e4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ea55826e",
+   "id": "d61eb99b",
    "metadata": {},
    "source": [
     "## Coefficient table (with confidence intervals)"
@@ -59,7 +59,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "1ee20735",
+   "id": "a23d93d3",
    "metadata": {},
    "outputs": [
     {
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6b7832df",
+   "id": "9df71b0f",
    "metadata": {},
    "source": [
     "Change the confidence level with `conf_level=` (e.g. `0.99`).\n",
@@ -109,7 +109,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "13a102b1",
+   "id": "0d244f37",
    "metadata": {},
    "outputs": [
     {
@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d536d1f6",
+   "id": "dfb8c49c",
    "metadata": {},
    "source": [
     "## Model-fit summaries"
@@ -160,7 +160,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "e1b99cdf",
+   "id": "11e7d2a8",
    "metadata": {},
    "outputs": [
     {
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f73467f1",
+   "id": "b6c285f0",
    "metadata": {},
    "source": [
     "## Correlation"
@@ -207,7 +207,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "6de7c700",
+   "id": "d83a3352",
    "metadata": {},
    "outputs": [
     {
@@ -245,7 +245,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "707696a7",
+   "id": "18a03591",
    "metadata": {},
    "source": [
     "## Visualizing models\n",
@@ -256,7 +256,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "380f5d74",
+   "id": "bbeeeecc",
    "metadata": {},
    "outputs": [
     {
@@ -283,7 +283,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78a24d2b",
+   "id": "617395b0",
    "metadata": {},
    "source": [
     "For the plotnine engine you can also drop the parallel-slopes lines onto your own\n",
@@ -299,7 +299,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "ebe56ce9",
+   "id": "afe0e404",
    "metadata": {},
    "outputs": [
     {
@@ -307,7 +307,7 @@
       "image/png": 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",
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a88LSalAG1ZBGF3YncCUGEdLPp2lQAKcS2Zlp8BAIO+dWpSNWUCmpkPozg+WQG4+n\\u002flAx6XAWC9\\u002fJrLZisBQRacfIIpwQGwauLZEtrNAeMUaI2h3hUDcMDzp4VC5wLC78aMpOXNAzXSZ8pwOk0CK0kzT6Le8QOSFUmrcs6nALP\\u002f51c4ns0CsNNHtsbqzQBAlOllrssBAcICtiUYDhkAX0t5iX0qhwEbuYbQnirPAlciSNVvgp8CoEmJ70zWSQNoREH7usLfA6MoucDpakUDsnTwHKkqUQJpmQ4f90aXAOHrdP9gWgUBiBGzb092gwCAUTGXc\\u002fmpAiBnTpNytlEAUNges+9uvQCSX+Q6GLHrAAItTT91Ku8AZfjhlY0GUwAD6z4C8ZJRAHiudadJMtMALEjn6H6yfwH+dH2tV0bDANAXRZ6WEnUCoAjuJ8zWUwG66ibjjQJnAX1Cn04gxtcAyRdLuL06mwALzWlY8DbDAvSmz8i5rtEAYmjmN5EamQDpaWDx7wsHAaG7ZAI5susBgvXH7UKl8QJB\\u002fsY5VtbPAwD2y2ypOcECo4o6+7i2KwEjuvu+SLYhA\\u002fC\\u002f63tZth0CwpeceXNKmQGDoQXsd3LbA5B7vK3HHtkBIAotss66EwNIw6uqmG61AVh5KqXBcr8AavbMJmKauwPRC33FEfrJA0iJ+2i5tpEAkYV++YWieQLQykDISgYVASmjzzQwYxUCUoSgd7niqQPlSIaOoUrVAZsYGHsnNmEDPj4eD8pO+wEa9JTIcrKdAUNNhZ1lQgkCc2RMLy26qQMuRQYESz7ZAKaXQMkdbwcA4E2\\u002fbzY+8QBtCms3nGKBAF3tvHc9Fg0DOP43J4uTBQCjWYIvyHZ\\u002fAOFDV7CiqrsDDIEDvcvqgQA8eU1u7S69ApSG66jZXsUAfk0hCE8KqQKARjSQqhKNAKvRBJZ0sh8BFpeioLgWkwJw8BhDItZbAgdZmuXozokBft6KNH8SNQH8VDgljuZBAZZnznffvlMAq+GA+3ySVQORH1jdP66dA9v7FPcWRksAhKFp153G+wI5NtgLewrlA0l\\u002fbVvyevcC+N6rRLUGSwIrdv\\u002fBmnbZArmccw+aSksDwueD0MMtvwPRGn35T2XHAECOhLHiEssBErlm5PXKaQFxwvdppZInAiKIA3FIpkcCO6MVC1GicQHsiIRPrDrzA9lacmmmMp8Dixtqjr+KnQMy1AF4+4JJAklLc67hPvEBYTamSEGC0QO7AQV8PEpbAbAuuVdn4uMDCjmGGHjekQLQxZJFApKBAqIG+EUhXnEBwUqzQR528QBCkWaafnsXAKK7XMGxyu0DYrqOIUUi2QMmTbv16KKXAVjEIoYnZrkB4\\u002fJJ1wsa8wJAWcH+QBqVA9QI85ljIskBJjX6VOYehQLQof1+41aVAXZZ3F9cstMBpJX6j9mmswMoanm8OkKTAWsEYFPHsmUCSnb+BNAaHwCKgkhj\\u002feaVAtItY2kSAfMAPslXp6Z2zQIaIdpWba7BA4fV8h2n+o8A+X8lZH2y1QKAPWgRUHFZAMOpQs6oRacCQxcZfHgl8QJIg6fRFkZ3AEgyVaOXNp8AI+iT2kNyzwBKRrVZHHKxARO5Q5YmhlMAmYHhi9n7DwHoOIFjurpJAGCwZenWZcsCmOJIUhxaywAWIrcto4sFAKFYP1blNgcCchcUOJX2fwNaXSqOnxpHAdtCC8f+QmMAovLZe5AizwGT8sh5PiqfAkPhSqaK0tcCfY61bxLa2wMx8nm4N4ppAcC7PaLu8fkABg5LcnVu5QBBKYWLed2lAHQUqP91sosAIHj8o76qwwC4iOP2cdbhAYOY9LHxje8AkCICsyU+GwEVSaYQJHKtAestORw76pkBNQYlOFLu6QMhOdqKUzJXAhkLP+k4SdMDNKbVWBMSiQDBeB9MaLrBA\\u002fIzETTgYlMCFj3yGSIaVwAoILed19bJA16Oqcv6TscC4hBOoLruFwDzm443DEbpAdq0e2t9MpMD5vH0\\u002fppmowCKUcB6ms41Am9lxekwepUDZrN6ObuygQHOmxIQsQq9AANS3aL3rlMBCZiXM\\u002ffCpQADi\\u002fJxtERHAHO433X5QmcDpdPoZfaSywA5iLlHkSKrACpgSxM\\u002fntsDWzM5QsUClwGkIaHOzn6tAygtbPw38kMCGNnr2aN23QNNRFBiXir1AKF4ACWWDrMA26uq+jvhzQPgW0\\u002fERFbhAB0TjhZ5PtUAbGIbRMN2wwES0+jW3TbXAbEpnBXFXuUAw8u25DPKeQFwnAendNZRAYSTLKKMmwkDWkI7DrWOiwAATq0AueprAwXWefCVCs0CQmALiVnqEwNofISTZmI\\u002fACvz4xdkMpUANOZq7VZTBwGYmWWdzH7nA7KqG6TQ+gsAAxRP\\u002figJcQCAveWlBNlFAkJ7eE2zjsEAebUa48tavwA67GZAw+qbACLZTTkaqm8DseOLKSFipwEATQJFNA0rA++o93WgNnkA8iqU+EuSjQBEB+OrJSr7ArIADtIR5n0C9x3aC\\u002fQ+iQDLqjK8ZR5dANpkXr9havUAkAA806IqaQBHui6CXaopAyCOL8q0erEBYVpq98MuFQFSNjUySsY1AtpXS2ktvokDaaDCYNUucwLrvuomJurRAvZsbt70oo0AsjwrantRyQFe6OQFFMqzA4DpeMyKyZUDwJchRlZ5zwJ3VyqOPqp5ApMZHxXNQtsBAu0aF3rWAQAk9\\u002fmMrgZNAgxFpFDhmrEBADKyJgkORQEk23FZoI7fAbCEP8VvtiMBtEkVsLQS+QLAYT9XJGYLAcIQGMHxNoMCSvldSTJevwG4SpIT7RqHAYbUdQQhZy8AjbTEZXN26QOColyMXqsnAuIMGkLfhfcC48cdYB1CtwENWMUG5MppAPl8r81YQlsBzjkBbpmqrQHUygPBFV6\\u002fAHGCPwZ5ksMBKlPtLOgOywBpBnR7purbAuEuMDbSrg8DzKUwf532iQA5iZZuguLnAAgZqUiLKmEDJH4VI\\u002fia6wDQ1W0DsfoLA+u7UGItUrUDwxlsdDGnCQP98+CimHrXAAJdVzC+kr0BcpC6MJWrAQEBDBrYYnaNAY\\u002f\\u002fXqpdSr0AYtsSd4u6zQPHcYam3sqDAvqNe7q5+scB87FbgSXOMQNuxc6Z8urdA1BsCNH0vvcA9oUW0scmbQJCoNJHbHGjAcCeyUvzTsECY2syanbCWwHvjIRdueZdAX1tU\\u002fbqDsEAI+PzymmKtQJF83t9T8ZjAsq64iRgKlcC7G0ZXlie8wLVFGcwXyahA5LZdQReimcDMZB9DCNqfQOB4M2CmgX1AgXzS8hJLuMCQddy5ZytxQMqzVCXxDrPAht1dAdeswsD0C0xgNs+hQAnWv\\u002fymgZRAAaA+NnI\\u002fs8D5ktNoBKO\\u002fwBYrtAJoUbhA4hUKRNY8xkAamlz8slKwQNbLc4VoC57A+i8facjiuMB0GMdr38G8wEpEd\\u002fvJk7JAVmtlm3cgnkDd1HecgwO2QKiSs5faYZDAu5zGRQ17tEDqIPNh4pSpQLWdDYpkIqjAcp09duoYo0AW5\\u002fnVwna8QGZ1e8yK86PATmrYJxKArsAuBLtj+7C4QPRJvtLXoabAMN7XkX\\u002f8fMCS32a18cfDQADWKkInsx1AvhTKnb8VtcDZGDtrhzOcQMIvlhFv9YNAJH8\\u002fCcS\\u002fckDCXOdJs9SUQEAvDTNYIqdA\\u002ffdRCqswkUAa1WvTxd+8wDg1783uzbPAwWVCxm+klMDK+cOE6weqwJglOv1bM1vAABNDUZ0AgUCpv7DOyE+cwFi9J+ede6fAWuOwjirPkkDRb6KKYpa5wEJ55udpcqNAg5AecB6Iw0Dm1dPIHXWzQB8TfL8b37nAqplpSwY3wUDjQfzjNvKtQGzaO+57sqJAbEjqmNWNtsCrBoCS50LCQLi0lnC4XrfA9aZmSoLnp0BozvYWjBG3QJ2Zd0lBUa1AVYXuJlo3oEBOEofrovSgwBsyOGTEfqjAAsZoOTeLucDGj+8jYsSTQPf+1DyClMHAuBediS+8r0A\\u002fVkuaGULCwGbAFr9rUJdATh6pV1dKnEDWN8n4Qx+sQPisl9eo9qXAPBNo20yUt0A1c27MjWKjwADj\\u002fc+OZoPACBSdfCOHrkBDH5Qu+pqwQOjg1sUXk6pAABVCY8ietkCpS846R32XwOYIez8scqdAjA\\u002fZqn31eUCARY2lyAg9wPR+3hLqTorAr9uZluBtpsDUhCHt8RKUwD9asLeQyrTAYAVfxoBAuUDWIGYpxf+zwE133MwPvqTA7KKNxSMcrsCU9LBArLR9wMlPPLIwu6pAflyVeiKlrsDIR0XZJOG1wJx9GWXAs6BAPufjWBBdmkAexGhluNCowC86QLf5LbtACh8jr+NbuMCbozJwQWejQJ7EDynhB7NA0i4Hmd0RkcB3Kx9PbOuuwOjKyJK3763A4FKD5ywDaUCkQgS4fqGIQOMQLoJqGp3A4CVZ0h4akkAiMKZn+QijQEBsyk1ObLpA8zbG9YUKo8AM6aI0OH2kwLA7njsZvbFAk9Vf1GpiocD4Xa21v8aFwLyJIxyxd6BA5Ea5W+oHkMDYvY7RrmyGQIZCHXbsmqTAYuSzeLE7msBwUVvnzUyBQEn+N3XHnLXAubbHhJIxkcDRdfUOT3qoQNi\\u002ft5diIppAJxEGOt\\u002fspUDwmZ5v0BhywAT3AqjUFbHAnFGFyXDsqkBton+5Q7mRwI7yWjpP4KFA6Jg+DW3It8C+SbGiKFyIQBjjrp18B6jAAujI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a88LSalAG1ZBGF3YncCUGEdLPp2lQAKcS2Zlp8BAIO+dWpSNWUCmpkPozg+WQG4+n\\u002flAx6XAWC9\\u002fJrLZisBQRacfIIpwQGwauLZEtrNAeMUaI2h3hUDcMDzp4VC5wLC78aMpOXNAzXSZ8pwOk0CK0kzT6Le8QOSFUmrcs6nALP\\u002f51c4ns0CsNNHtsbqzQBAlOllrssBAcICtiUYDhkAX0t5iX0qhwEbuYbQnirPAlciSNVvgp8CoEmJ70zWSQNoREH7usLfA6MoucDpakUDsnTwHKkqUQJpmQ4f90aXAOHrdP9gWgUBiBGzb092gwCAUTGXc\\u002fmpAiBnTpNytlEAUNges+9uvQCSX+Q6GLHrAAItTT91Ku8AZfjhlY0GUwAD6z4C8ZJRAHiudadJMtMALEjn6H6yfwH+dH2tV0bDANAXRZ6WEnUCoAjuJ8zWUwG66ibjjQJnAX1Cn04gxtcAyRdLuL06mwALzWlY8DbDAvSmz8i5rtEAYmjmN5EamQDpaWDx7wsHAaG7ZAI5susBgvXH7UKl8QJB\\u002fsY5VtbPAwD2y2ypOcECo4o6+7i2KwEjuvu+SLYhA\\u002fC\\u002f63tZth0CwpeceXNKmQGDoQXsd3LbA5B7vK3HHtkBIAotss66EwNIw6uqmG61AVh5KqXBcr8AavbMJmKauwPRC33FEfrJA0iJ+2i5tpEAkYV++YWieQLQykDISgYVASmjzzQwYxUCUoSgd7niqQPlSIaOoUrVAZsYGHsnNmEDPj4eD8pO+wEa9JTIcrKdAUNNhZ1lQgkCc2RMLy26qQMuRQYESz7ZAKaXQMkdbwcA4E2\\u002fbzY+8QBtCms3nGKBAF3tvHc9Fg0DOP43J4uTBQCjWYIvyHZ\\u002fAOFDV7CiqrsDDIEDvcvqgQA8eU1u7S69ApSG66jZXsUAfk0hCE8KqQKARjSQqhKNAKvRBJZ0sh8BFpeioLgWkwJw8BhDItZbAgdZmuXozokBft6KNH8SNQH8VDgljuZBAZZnznffvlMAq+GA+3ySVQORH1jdP66dA9v7FPcWRksAhKFp153G+wI5NtgLewrlA0l\\u002fbVvyevcC+N6rRLUGSwIrdv\\u002fBmnbZArmccw+aSksDwueD0MMtvwPRGn35T2XHAECOhLHiEssBErlm5PXKaQFxwvdppZInAiKIA3FIpkcCO6MVC1GicQHsiIRPrDrzA9lacmmmMp8Dixtqjr+KnQMy1AF4+4JJAklLc67hPvEBYTamSEGC0QO7AQV8PEpbAbAuuVdn4uMDCjmGGHjekQLQxZJFApKBAqIG+EUhXnEBwUqzQR528QBCkWaafnsXAKK7XMGxyu0DYrqOIUUi2QMmTbv16KKXAVjEIoYnZrkB4\\u002fJJ1wsa8wJAWcH+QBqVA9QI85ljIskBJjX6VOYehQLQof1+41aVAXZZ3F9cstMBpJX6j9mmswMoanm8OkKTAWsEYFPHsmUCSnb+BNAaHwCKgkhj\\u002feaVAtItY2kSAfMAPslXp6Z2zQIaIdpWba7BA4fV8h2n+o8A+X8lZH2y1QKAPWgRUHFZAMOpQs6oRacCQxcZfHgl8QJIg6fRFkZ3AEgyVaOXNp8AI+iT2kNyzwBKRrVZHHKxARO5Q5YmhlMAmYHhi9n7DwHoOIFjurpJAGCwZenWZcsCmOJIUhxaywAWIrcto4sFAKFYP1blNgcCchcUOJX2fwNaXSqOnxpHAdtCC8f+QmMAovLZe5AizwGT8sh5PiqfAkPhSqaK0tcCfY61bxLa2wMx8nm4N4ppAcC7PaLu8fkABg5LcnVu5QBBKYWLed2lAHQUqP91sosAIHj8o76qwwC4iOP2cdbhAYOY9LHxje8AkCICsyU+GwEVSaYQJHKtAestORw76pkBNQYlOFLu6QMhOdqKUzJXAhkLP+k4SdMDNKbVWBMSiQDBeB9MaLrBA\\u002fIzETTgYlMCFj3yGSIaVwAoILed19bJA16Oqcv6TscC4hBOoLruFwDzm443DEbpAdq0e2t9MpMD5vH0\\u002fppmowCKUcB6ms41Am9lxekwepUDZrN6ObuygQHOmxIQsQq9AANS3aL3rlMBCZiXM\\u002ffCpQADi\\u002fJxtERHAHO433X5QmcDpdPoZfaSywA5iLlHkSKrACpgSxM\\u002fntsDWzM5QsUClwGkIaHOzn6tAygtbPw38kMCGNnr2aN23QNNRFBiXir1AKF4ACWWDrMA26uq+jvhzQPgW0\\u002fERFbhAB0TjhZ5PtUAbGIbRMN2wwES0+jW3TbXAbEpnBXFXuUAw8u25DPKeQFwnAendNZRAYSTLKKMmwkDWkI7DrWOiwAATq0AueprAwXWefCVCs0CQmALiVnqEwNofISTZmI\\u002fACvz4xdkMpUANOZq7VZTBwGYmWWdzH7nA7KqG6TQ+gsAAxRP\\u002figJcQCAveWlBNlFAkJ7eE2zjsEAebUa48tavwA67GZAw+qbACLZTTkaqm8DseOLKSFipwEATQJFNA0rA++o93WgNnkA8iqU+EuSjQBEB+OrJSr7ArIADtIR5n0C9x3aC\\u002fQ+iQDLqjK8ZR5dANpkXr9havUAkAA806IqaQBHui6CXaopAyCOL8q0erEBYVpq98MuFQFSNjUySsY1AtpXS2ktvokDaaDCYNUucwLrvuomJurRAvZsbt70oo0AsjwrantRyQFe6OQFFMqzA4DpeMyKyZUDwJchRlZ5zwJ3VyqOPqp5ApMZHxXNQtsBAu0aF3rWAQAk9\\u002fmMrgZNAgxFpFDhmrEBADKyJgkORQEk23FZoI7fAbCEP8VvtiMBtEkVsLQS+QLAYT9XJGYLAcIQGMHxNoMCSvldSTJevwG4SpIT7RqHAYbUdQQhZy8AjbTEZXN26QOColyMXqsnAuIMGkLfhfcC48cdYB1CtwENWMUG5MppAPl8r81YQlsBzjkBbpmqrQHUygPBFV6\\u002fAHGCPwZ5ksMBKlPtLOgOywBpBnR7purbAuEuMDbSrg8DzKUwf532iQA5iZZuguLnAAgZqUiLKmEDJH4VI\\u002fia6wDQ1W0DsfoLA+u7UGItUrUDwxlsdDGnCQP98+CimHrXAAJdVzC+kr0BcpC6MJWrAQEBDBrYYnaNAY\\u002f\\u002fXqpdSr0AYtsSd4u6zQPHcYam3sqDAvqNe7q5+scB87FbgSXOMQNuxc6Z8urdA1BsCNH0vvcA9oUW0scmbQJCoNJHbHGjAcCeyUvzTsECY2syanbCWwHvjIRdueZdAX1tU\\u002fbqDsEAI+PzymmKtQJF83t9T8ZjAsq64iRgKlcC7G0ZXlie8wLVFGcwXyahA5LZdQReimcDMZB9DCNqfQOB4M2CmgX1AgXzS8hJLuMCQddy5ZytxQMqzVCXxDrPAht1dAdeswsD0C0xgNs+hQAnWv\\u002fymgZRAAaA+NnI\\u002fs8D5ktNoBKO\\u002fwBYrtAJoUbhA4hUKRNY8xkAamlz8slKwQNbLc4VoC57A+i8facjiuMB0GMdr38G8wEpEd\\u002fvJk7JAVmtlm3cgnkDd1HecgwO2QKiSs5faYZDAu5zGRQ17tEDqIPNh4pSpQLWdDYpkIqjAcp09duoYo0AW5\\u002fnVwna8QGZ1e8yK86PATmrYJxKArsAuBLtj+7C4QPRJvtLXoabAMN7XkX\\u002f8fMCS32a18cfDQADWKkInsx1AvhTKnb8VtcDZGDtrhzOcQMIvlhFv9YNAJH8\\u002fCcS\\u002fckDCXOdJs9SUQEAvDTNYIqdA\\u002ffdRCqswkUAa1WvTxd+8wDg1783uzbPAwWVCxm+klMDK+cOE6weqwJglOv1bM1vAABNDUZ0AgUCpv7DOyE+cwFi9J+ede6fAWuOwjirPkkDRb6KKYpa5wEJ55udpcqNAg5AecB6Iw0Dm1dPIHXWzQB8TfL8b37nAqplpSwY3wUDjQfzjNvKtQGzaO+57sqJAbEjqmNWNtsCrBoCS50LCQLi0lnC4XrfA9aZmSoLnp0BozvYWjBG3QJ2Zd0lBUa1AVYXuJlo3oEBOEofrovSgwBsyOGTEfqjAAsZoOTeLucDGj+8jYsSTQPf+1DyClMHAuBediS+8r0A\\u002fVkuaGULCwGbAFr9rUJdATh6pV1dKnEDWN8n4Qx+sQPisl9eo9qXAPBNo20yUt0A1c27MjWKjwADj\\u002fc+OZoPACBSdfCOHrkBDH5Qu+pqwQOjg1sUXk6pAABVCY8ietkCpS846R32XwOYIez8scqdAjA\\u002fZqn31eUCARY2lyAg9wPR+3hLqTorAr9uZluBtpsDUhCHt8RKUwD9asLeQyrTAYAVfxoBAuUDWIGYpxf+zwE133MwPvqTA7KKNxSMcrsCU9LBArLR9wMlPPLIwu6pAflyVeiKlrsDIR0XZJOG1wJx9GWXAs6BAPufjWBBdmkAexGhluNCowC86QLf5LbtACh8jr+NbuMCbozJwQWejQJ7EDynhB7NA0i4Hmd0RkcB3Kx9PbOuuwOjKyJK3763A4FKD5ywDaUCkQgS4fqGIQOMQLoJqGp3A4CVZ0h4akkAiMKZn+QijQEBsyk1ObLpA8zbG9YUKo8AM6aI0OH2kwLA7njsZvbFAk9Vf1GpiocD4Xa21v8aFwLyJIxyxd6BA5Ea5W+oHkMDYvY7RrmyGQIZCHXbsmqTAYuSzeLE7msBwUVvnzUyBQEn+N3XHnLXAubbHhJIxkcDRdfUOT3qoQNi\\u002ft5diIppAJxEGOt\\u002fspUDwmZ5v0BhywAT3AqjUFbHAnFGFyXDsqkBton+5Q7mRwI7yWjpP4KFA6Jg+DW3It8C+SbGiKFyIQBjjrp18B6jAAujI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       ],
       "text/plain": [
        "Figure({\n",
@@ -509,7 +509,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "deaa4248",
+   "id": "ba8c0961",
    "metadata": {},
    "source": [
     "Per-facet shading works in both engines: `shade_p_value` / `shade_confidence_interval`\n",
diff --git a/doc/_build/jupyter_execute/guides/sampling.ipynb b/doc/_build/jupyter_execute/guides/sampling.ipynb
index 24fc7ad..46f1c51 100644
--- a/doc/_build/jupyter_execute/guides/sampling.ipynb
+++ b/doc/_build/jupyter_execute/guides/sampling.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "0db12f0d",
+   "id": "7048d552",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "75743494",
+   "id": "2e248f5b",
    "metadata": {},
    "source": [
     "# Sampling\n",
@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "778be711",
+   "id": "abd5d97e",
    "metadata": {},
    "outputs": [
     {
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "823a2021",
+   "id": "b0fe3200",
    "metadata": {},
    "source": [
     "## One virtual sample"
@@ -89,7 +89,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "8e879ec1",
+   "id": "44fe2a6a",
    "metadata": {},
    "outputs": [
     {
@@ -130,7 +130,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c9bca22a",
+   "id": "5661f0ec",
    "metadata": {},
    "source": [
     "## Many samples → a sampling distribution\n",
@@ -141,7 +141,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "7b87f1de",
+   "id": "133800e3",
    "metadata": {},
    "outputs": [
     {
@@ -189,7 +189,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "370e9f26",
+   "id": "b1048568",
    "metadata": {},
    "source": [
     "That `prop_red` column is a **sampling distribution**. Visualize its spread the\n",
@@ -205,7 +205,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "4e7c65c0",
+   "id": "7a5431bc",
    "metadata": {},
    "outputs": [
     {
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "245dcc87",
+   "id": "cbd208e1",
    "metadata": {},
    "source": [
     "## Tactile samples\n",
@@ -263,7 +263,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "daa58281",
+   "id": "ba9ee8ab",
    "metadata": {},
    "outputs": [
     {
@@ -310,7 +310,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9932aac9",
+   "id": "ad7a49b7",
    "metadata": {},
    "source": [
     "```{seealso}\n",
diff --git a/doc/_build/jupyter_execute/guides/theory-based.ipynb b/doc/_build/jupyter_execute/guides/theory-based.ipynb
index 59b7e33..aa94f20 100644
--- a/doc/_build/jupyter_execute/guides/theory-based.ipynb
+++ b/doc/_build/jupyter_execute/guides/theory-based.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "4f556925",
+   "id": "ae699083",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5cadb0e",
+   "id": "a7cf35d6",
    "metadata": {},
    "source": [
     "# Theory-based inference\n",
@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "6dc70ab2",
+   "id": "b7c934a4",
    "metadata": {},
    "outputs": [
     {
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "38373ddd",
+   "id": "5bc0cc59",
    "metadata": {},
    "source": [
     "`t_stat` and `chisq_stat` return just the test statistic if that's all you need.\n",
@@ -103,14 +103,14 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "cb7f728a",
+   "id": "8b24f1c1",
    "metadata": {},
    "outputs": [
     {
      "data": {
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       "text/plain": [
-       "<plotnine.ggplot.ggplot object at 0x139d1a060>"
+       "<plotnine.ggplot.ggplot object at 0x136eea060>"
       ]
      },
      "execution_count": 3,
@@ -137,7 +137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "10106896",
+   "id": "23cbed8b",
    "metadata": {},
    "source": [
     "Supported distributions: `\"t\"`, `\"z\"`, `\"F\"` (pass `df=(df1, df2)`), and\n",
@@ -152,7 +152,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "52d705d4",
+   "id": "7e00d929",
    "metadata": {},
    "outputs": [
     {
@@ -160,7 +160,7 @@
       "image/png": 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",
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{\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"white\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"white\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"#C8D4E3\",\"linecolor\":\"#C8D4E3\",\"minorgridcolor\":\"#C8D4E3\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"#C8D4E3\",\"linecolor\":\"#C8D4E3\",\"minorgridcolor\":\"#C8D4E3\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scattermap\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermap\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"white\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"#C8D4E3\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"white\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\"},\"bgcolor\":\"white\",\"radialaxis\":{\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"},\"yaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"},\"zaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"},\"bgcolor\":\"white\",\"caxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#EBF0F8\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#EBF0F8\",\"zerolinewidth\":2}}},\"title\":{\"text\":\"Theoretical Distribution\"},\"xaxis\":{\"title\":{\"text\":\"mean\"}},\"yaxis\":{\"title\":{\"text\":\"density\"}},\"bargap\":0.02,\"showlegend\":false},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cfce35e8",
+   "id": "65009c85",
    "metadata": {},
    "source": [
     "## Population standard deviation\n",
@@ -211,7 +211,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "aa38fce9",
+   "id": "3460dea2",
    "metadata": {},
    "outputs": [
     {
diff --git a/doc/_build/jupyter_execute/index.ipynb b/doc/_build/jupyter_execute/index.ipynb
index ee2a11e..06e9eff 100644
--- a/doc/_build/jupyter_execute/index.ipynb
+++ b/doc/_build/jupyter_execute/index.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "f2ced15f",
+   "id": "5f157838",
    "metadata": {
     "tags": [
      "remove-input"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d344f744",
+   "id": "7c02e47e",
    "metadata": {},
    "source": [
     "# moderndive (Python)\n",
@@ -67,7 +67,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "4f43cb39",
+   "id": "971ef150",
    "metadata": {},
    "outputs": [
     {
@@ -98,7 +98,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "7c52a3b0",
+   "id": "1a0f99e2",
    "metadata": {},
    "outputs": [
     {
@@ -143,7 +143,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "51622caa",
+   "id": "bd9882b5",
    "metadata": {},
    "outputs": [
     {
@@ -151,7 +151,7 @@
       "image/png": 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Null Distribution\"},\"xaxis\":{\"title\":{\"text\":\"diff in props\"}},\"yaxis\":{\"title\":{\"text\":\"count\"}},\"shapes\":[{\"line\":{\"color\":\"#d62728\",\"dash\":\"solid\",\"width\":2},\"type\":\"line\",\"x0\":0.03399999999999992,\"x1\":0.03399999999999992,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"},{\"fillcolor\":\"#d62728\",\"line\":{\"width\":0},\"opacity\":0.3,\"type\":\"rect\",\"x0\":0.03399999999999992,\"x1\":0.07509999999999994,\"xref\":\"x\",\"y0\":0,\"y1\":1,\"yref\":\"y domain\"}]},                        {\"responsive\": true}                    )                };            </script>        </div>"
       ],
       "text/plain": [
        "Figure({\n",
@@ -199,7 +199,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61bfc1e3",
+   "id": "32f39cc5",
    "metadata": {},
    "source": [
     "```{note}\n",

From a09f5790f7ef95619f09515cfe1265ab24a59dc5 Mon Sep 17 00:00:00 2001
From: Chester Ismay <chester.ismay@gmail.com>
Date: Fri, 19 Jun 2026 08:44:19 -0700
Subject: [PATCH 9/9] style: ruff format check_parity.py and
 test_parity_additions.py

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017CTL1QSTg1DmDUpqYuPEog
---
 tests/test_parity_additions.py |  4 +---
 tools/check_parity.py          | 25 +++++++++++++++++--------
 2 files changed, 18 insertions(+), 11 deletions(-)

diff --git a/tests/test_parity_additions.py b/tests/test_parity_additions.py
index f344aa0..aa7a737 100644
--- a/tests/test_parity_additions.py
+++ b/tests/test_parity_additions.py
@@ -137,9 +137,7 @@ def test_geom_categorical_model_is_alias():
     ev = md.load_evals()
     assert md.geom_categorical_model is md.gg_categorical_model
     assert isinstance(md.geom_categorical_model(ev, "score", "rank", engine="plotly"), go.Figure)
-    assert isinstance(
-        md.geom_categorical_model(ev, "score", "rank", engine="plotnine"), ggplot
-    )
+    assert isinstance(md.geom_categorical_model(ev, "score", "rank", engine="plotnine"), ggplot)
 
 
 # ============================ DataFrame .specify() accessor ===============
diff --git a/tools/check_parity.py b/tools/check_parity.py
index 34ac19e..d732c93 100644
--- a/tools/check_parity.py
+++ b/tools/check_parity.py
@@ -57,12 +57,16 @@ def _diff(upstream: dict, manifest: dict, py: dict) -> list[str]:
         for ds, meta in info["datasets"].items():
             r_dims = [meta["rows"], meta["cols"]]
             if ds not in py_names:
-                lines.append(f"- dataset `{ds}` ({pkg}) is **missing** from the Python package "
-                             f"(R dims {r_dims})")
+                lines.append(
+                    f"- dataset `{ds}` ({pkg}) is **missing** from the Python package "
+                    f"(R dims {r_dims})"
+                )
                 continue
             if [py[ds]["rows"], py[ds]["cols"]] != r_dims:
-                lines.append(f"- dataset `{ds}`: dims differ — Python "
-                             f"{[py[ds]['rows'], py[ds]['cols']]} vs R {r_dims}")
+                lines.append(
+                    f"- dataset `{ds}`: dims differ — Python "
+                    f"{[py[ds]['rows'], py[ds]['cols']]} vs R {r_dims}"
+                )
             missing_cols = sorted(set(meta["columns"]) - set(py[ds]["columns"]))
             if missing_cols:
                 lines.append(f"- dataset `{ds}`: columns missing in Python: {missing_cols}")
@@ -72,8 +76,11 @@ def _diff(upstream: dict, manifest: dict, py: dict) -> list[str]:
 def main() -> int:
     ap = argparse.ArgumentParser(description=__doc__)
     ap.add_argument("upstream", help="JSON produced by tools/parity_probe.R")
-    ap.add_argument("--update-manifest", action="store_true",
-                    help="overwrite the manifest with the current upstream snapshot")
+    ap.add_argument(
+        "--update-manifest",
+        action="store_true",
+        help="overwrite the manifest with the current upstream snapshot",
+    )
     args = ap.parse_args()
 
     upstream = json.loads(Path(args.upstream).read_text())
@@ -89,8 +96,10 @@ def main() -> int:
     if lines:
         print("# Upstream parity drift detected\n")
         print("\n".join(lines))
-        print("\nPort the changes into the Python package, then run "
-              "`python tools/check_parity.py <upstream.json> --update-manifest` to accept.")
+        print(
+            "\nPort the changes into the Python package, then run "
+            "`python tools/check_parity.py <upstream.json> --update-manifest` to accept."
+        )
         return 1
     print("No parity drift: the Python port matches upstream moderndive/infer.")
     return 0