This repository presents a two-part analytics project series built on the same real-world e-commerce dataset.
Together, the two parts demonstrate a complete progression from core regression modeling and business interpretation to a more advanced statistical continuation, all framed around a consistent business narrative.
The work is designed to mirror how analytics projects evolve in practice—starting with foundational modeling for decision support and extending into deeper statistical evaluation using the same data.
MarketPlace is a fast-growing online retailer operating across multiple product categories. Leadership wants to understand:
- What drives monthly sales performance
- How effective marketing investments truly are
- How seasonality and product mix influence revenue
- How confidently sales can be forecasted for planning and budgeting
The dataset includes sales, marketing activity, website traffic, pricing competitiveness, customer ratings, inventory levels, social engagement, and categorical variables for season and product type.
Focus:
Identify key drivers of e-commerce sales using multiple regression and translate statistical results into executive-ready insights.
What this part demonstrates:
- Dummy variable creation and interpretation
- Full regression model construction
- Global and individual hypothesis testing
- Model comparison and selection
- Business interpretation of coefficients
- Sales prediction with confidence and prediction intervals
- Evaluation of marketing levers using partial tests
Artifacts included:
- R Markdown analysis (Part 1 R code)
- Rendered HTML report (interactive)
- Business executive summary (PDF)
Focus:
Extend the initial regression analysis with more advanced statistical techniques using the same dataset and business context.
What this part demonstrates:
- Deeper statistical inference and model evaluation
- Advanced hypothesis testing and diagnostics
- Continuation of the original business narrative
- Stronger emphasis on statistical rigor and interpretation
Artifacts included:
- R Markdown analysis (Part 2 R code)
- Rendered HTML report (interactive)
- Business-focused executive summary (PDF)
- Start with the HTML reports for each part to see the full analysis and results.
- Review the executive summary PDFs for concise, stakeholder-ready insights.
- Explore the R Markdown files to see the full analytical workflow and code.
- R / RStudio
- R Markdown
- Multiple Linear Regression
- Advanced Statistical Inference
- GitHub Pages
This project series demonstrates:
- How analytics work progresses beyond a single model
- Strong linkage between statistical rigor and business decision-making
- Clear communication of technical results to non-technical stakeholders
- Reproducible, well-organized analytical workflows
- Both parts use the same dataset to show continuity and analytical depth.
- AI tools were used for writing and formatting support only.
- All statistical modeling decisions and interpretations are my own.