diff --git a/your-code/.ipynb_checkpoints/main-checkpoint.ipynb b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Understanding Descriptive Statistics\n",
+    "\n",
+    "Import the necessary libraries here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Libraries\n",
+    "import random\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 1\n",
+    "#### 1.- Define a function that simulates rolling a dice 10 times. Save the information in a dataframe.\n",
+    "**Hint**: you can use the *choices* function from module *random* to help you with the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Roll</th>\n",
+       "      <th>Result</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Roll  Result\n",
+       "0     1       5\n",
+       "1     2       1\n",
+       "2     3       1\n",
+       "3     4       6\n",
+       "4     5       1\n",
+       "5     6       3\n",
+       "6     7       2\n",
+       "7     8       4\n",
+       "8     9       2\n",
+       "9    10       6"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "def dice(rolls, sides):\n",
+    "    results = random.choices(range(1, sides + 1), k = rolls)\n",
+    "    \n",
+    "    df = pd.DataFrame({'Roll': range(1, rolls + 1), 'Result': results})\n",
+    "    return df\n",
+    "\n",
+    "df = pd.DataFrame(dice(rolls=10, sides=6))\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Plot the results sorted by value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Roll</th>\n",
+       "      <th>Result</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Roll  Result\n",
+       "1     2       1\n",
+       "2     3       1\n",
+       "4     5       1\n",
+       "6     7       2\n",
+       "8     9       2\n",
+       "5     6       3\n",
+       "7     8       4\n",
+       "0     1       5\n",
+       "3     4       6\n",
+       "9    10       6"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted_df = df.sort_values(by = \"Result\")\n",
+    "sorted_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "x = sorted_df[\"Roll\"]\n",
+    "y = sorted_df[\"Result\"]\n",
+    "\n",
+    "plt.scatter(x,y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "plt.plot(df[\"Result\"].values)\n",
+    "plt.xlabel(\"Frequency\")\n",
+    "plt.ylabel(\"Dice roll number\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"The difference between the two plots relies on the sorted func.\n",
+    "The first plot illustrates the ordered sequence of dice numbers, while the second plot shows the actual individual \n",
+    "observations in their original random way.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 2\n",
+    "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n",
+    "\n",
+    "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "def manual_mean():\n",
+    "  total_sum = 0\n",
+    "  total_count = 0\n",
+    "  for value in df[\"Result\"]:\n",
+    "    total_sum += value\n",
+    "    total_count += 1\n",
+    "\n",
+    "  mean = total_sum/total_count\n",
+    "  return mean\n",
+    "\n",
+    "round(manual_mean())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "freq = {}\n",
+    "for value in df[\"Result\"]:\n",
+    "    if value in freq:\n",
+    "        freq[value] += 1\n",
+    "    else:\n",
+    "        freq[value] = 1\n",
+    "#print(\"Frequency is: \", freq)\n",
+    "\n",
+    "sum_values = 0\n",
+    "total_count = 0\n",
+    "\n",
+    "for value, count in freq.items():\n",
+    "  sum_values += value * count\n",
+    "  total_count += count\n",
+    "mean = sum_values/total_count\n",
+    "round(mean)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n",
+    "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "def manual_median(sorted_df):\n",
+    "  total_count = 0\n",
+    "  for value in sorted_df:\n",
+    "    total_count += 1\n",
+    "    if total_count % 2 == 0:\n",
+    "      median = (sorted_df[total_count // 2 - 1] + sorted_df[total_count // 2]) / 2\n",
+    "    else:\n",
+    "      median = (sorted_df[total_count // 2])\n",
+    "\n",
+    "  return median\n",
+    "\n",
+    "print((manual_median(sorted_df[\"Result\"])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Manual median is:  [7.5 2.5]\n",
+      "Method median is: 4.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "sorted_df_array = sorted_df.values\n",
+    "def manual_median(sorted_df_array):\n",
+    "  median = (sorted_df_array[4] + sorted_df_array[5])/2\n",
+    "  return median\n",
+    "manual_median(sorted_df_array)\n",
+    "\n",
+    "print(\"Manual median is: \", (sorted_df_array[4] + sorted_df_array[5])/2)\n",
+    "print(\"Method median is:\", np.median(sorted_df))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([7.5, 2.5]), array([7.5, 2.5]))"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def manual_quartiles(sorted_df_array):\n",
+    "    q2 = manual_median(sorted_df_array)\n",
+    "    for_q1 = sorted_df_array[:6]\n",
+    "    for_q3 = sorted_df_array[:6:]\n",
+    "    q1 = manual_median(for_q1)\n",
+    "    q3 = manual_median(for_q3)\n",
+    "    return q1, q3\n",
+    "\n",
+    "manual_quartiles(sorted_df_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 3\n",
+    "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n",
+    "#### 1.- Sort the values and plot them. What do you see?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>roll</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0  roll  value\n",
+       "0           0     0      1\n",
+       "1           1     1      2\n",
+       "2           2     2      6\n",
+       "3           3     3      1\n",
+       "4           4     4      6"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "data = pd.read_csv(\"roll_the_dice_hundred.csv\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x12d3050fa50>"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data = data.sort_values(by = \"value\")\n",
+    "data_plot = plt.scatter( x = sorted_data[\"roll\"], y = sorted_data[\"value\"])\n",
+    "data_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "def manual_mean():\n",
+    "  total_sum = 0\n",
+    "  total_count = 0\n",
+    "  for value in data[\"value\"]:\n",
+    "    total_sum += value\n",
+    "    total_count += 1\n",
+    "\n",
+    "  mean = total_sum/total_count\n",
+    "  return mean\n",
+    "\n",
+    "round(manual_mean())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Now, calculate the frequency distribution.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Frequency is:  {1: 12, 2: 17, 6: 23, 5: 12, 4: 22, 3: 14}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "freq = {}\n",
+    "for value in data[\"value\"]:\n",
+    "    if value in freq:\n",
+    "        freq[value] += 1\n",
+    "    else:\n",
+    "        freq[value] = 1\n",
+    "print(\"Frequency is: \", freq)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "plt.hist(data[\"value\"])\n",
+    "plt.xlabel(\"value\")\n",
+    "plt.ylabel(\"roll\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\" Because the mean will be the most observed event,\n",
+    "it makes sense that the bar corresponding to 4 will be the one with > frequency.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "data_2 = pd.read_csv(\"roll_the_dice_thousand.csv\")\n",
+    "\n",
+    "plt.hist(data_2[\"value\"])\n",
+    "plt.xlabel(\"value\")\n",
+    "plt.ylabel(\"roll\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A think because now we have a larger dataset, the frequency distribution will behave more evenly and more consistently. Also, this provides more~\\nconfidence in the \"random\" of the dice, instead of looking like some bias in some numbers.'"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"Because now we have a larger dataset, the frequency distribution will behave more evenly and more consistently. Also, this provides more~\n",
+    "confidence in the \"random\" of the dice, instead of looking like some bias in some numbers.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 4\n",
+    "In the `data` folder of this repository you will find three different files with the prefix `ages_population`. These files contain information about a poll answered by a thousand people regarding their age. Each file corresponds to the poll answers in different neighbourhoods of Barcelona.\n",
+    "\n",
+    "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "ages_pop = pd.read_csv(\"ages_population.csv\")\n",
+    "plt.hist(ages_pop[\"observation\"], bins = 30)\n",
+    "plt.xlabel(\"age\")\n",
+    "plt.ylabel(\"freq\")\n",
+    "plt.show()\n",
+    "# mean around 40 years old\n",
+    "# std around 14"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean is:  36.56\n",
+      "Std is:  12.816499625976762\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "print(\"Mean is: \", ages_pop[\"observation\"].mean())\n",
+    "print(\"Std is: \",ages_pop[\"observation\"].std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "ages_pop_2 = pd.read_csv(\"ages_population2.csv\")\n",
+    "plt.hist(ages_pop_2[\"observation\"], bins = 30)\n",
+    "plt.xlabel(\"age\")\n",
+    "plt.ylabel(\"freq\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "####  4.- What do you see? Is there any difference with the frequency distribution in step 1?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Very different, even according to the range of ages (suggesting less variability). Now we will have a lower mean age,\\n so the central tendency of this dataset is lower than the one before. '"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"Very different, even according to the range of ages (suggesting less variability). Now we will have a lower mean age,\n",
+    " so the central tendency of this dataset is lower than the one before. \"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean is:  27.155\n",
+      "Std is:  2.969813932689186\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "print(\"Mean is: \", ages_pop_2[\"observation\"].mean())\n",
+    "print(\"Std is: \",ages_pop_2[\"observation\"].std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' The mean age of  27.155 indicates that the central tendency of the dataset is lower than that of the previous dataset.\\nAlso suggests a younger population compared to the first dataset.\\n\\nA really lower std of around 2.97 indicates that the ages in this dataset are more tightly clustered around the mean.\\nThe previous set had a bigger range -> where the values were more spread out.'"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "\"\"\" The mean age of  27.155 indicates that the central tendency of the dataset is lower than that of the previous dataset.\n",
+    "Also suggests a younger population compared to the first dataset.\n",
+    "\n",
+    "A really lower std of around 2.97 indicates that the ages in this dataset are more tightly clustered around the mean.\n",
+    "The previous set had a bigger range -> where the values were more spread out.\"\"\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 5\n",
+    "Now is the turn of `ages_population3.csv`.\n",
+    "\n",
+    "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Nj3/8Y02ePFl//OMfdeTIER05ckR//OMf9cgjjyg9Pb01agQAALhgzb7m5sUXX9TPf/5zPfDAA/r6669PnyQiQo888ogWL17c4gUCAAA0R7PDTWRkpF544QUtXrxYH330kYwxuv7669WtW7fWqA8AAKBZLvohflVVVaqqqlK/fv3UrVs3GWNasi4AAICL0uxw89lnn+mOO+5Qv379NHbsWFVVVUmSHn300Uu6DTw3N1cul0uZmZnBMWOM/H6/fD6funbtqtGjR6usrOyifwYAALBfs8PNU089pU6dOunw4cOKjIwMjt97773auHHjRRVRWlqqZcuWafDgwSHjixYtUl5enpYsWaLS0lJ5vV6lpqZyVxYAADivZoebwsJCLVy4UNdee23IeEJCgioqKppdwJdffqn7779fy5cvD3l2jjFG+fn5ys7OVnp6uhITE1VQUKATJ05ozZo1zf45AACgfWh2uDl+/HjIis0ZR48eldvtbnYB06dP17hx43TnnXeGjJeXlysQCCgtLS045na7lZKSopKSkvOer76+XrW1tSEbAABoP5p9t9Ttt9+uVatW6Ve/+pWk0++Qamxs1OLFi/WDH/ygWedau3atduzYodLS0iZzgUBAkuTxeELGPR7PP1whys3N1dy5c5tVBwC0B33mvH7Rx368YFwLVgK0rmaHm8WLF2v06NHatm2bGhoaNGvWLJWVlenzzz/X3/72tws+T2VlpWbMmKHCwkJ16dLlvPt9+wWcxph/+FLOrKyskDeX19bWKi4u7oLrAgAAbVuzv5YaMGCAdu/erVtuuUWpqak6fvy40tPTtXPnTl133XUXfJ7t27erurpaw4cPV0REhCIiIlRcXKzf/va3ioiICK7YnFnBOaO6urrJas7Z3G63oqOjQzYAANB+NGvl5uuvv1ZaWpp+//vfX/JXP3fccYf27NkTMjZ58mT1799fs2fPVt++feX1elVUVKShQ4dKkhoaGlRcXKyFCxde0s8GAAD2ala46dSpk/bu3fsPvxa6UFFRUUpMTAwZ69atm3r06BEcz8zMVE5OjhISEpSQkKCcnBxFRkYqIyPjkn8+AACwU7O/lnrooYe0YsWK1qiliVmzZikzM1PTpk1TUlKSPvnkExUWFioqKuqy/HwAAND2NPuC4oaGBr300ksqKipSUlJSk3dK5eXlXXQx7777bshnl8slv98vv99/0ecEAADtywWFm927dysxMVEdOnTQ3r17NWzYMEnSgQMHQvZria+rAAAALsUFhZuhQ4eqqqpKPXv2VEVFhUpLS9WjR4/Wrg0AAKDZLuiamyuuuELl5eWSpI8//liNjY2tWhQAAMDFuqCVm7vuukspKSmKjY2Vy+VSUlKSOnbseM59Dx061KIFAgAANMcFhZtly5YpPT1dH374oZ588kk99thj3LEEAADC0gXfLfXP//zPkk4/WXjGjBmEGwAAEJaafSv4ypUrW6MOAACAFtHscAO0NW3xTciXUjMAtHfNfkIxAABAOCPcAAAAqxBuAACAVQg3AADAKoQbAABgFcINAACwCuEGAABYhXADAACswkP8AKAN4QGPwHdj5QYAAFiFcAMAAKxCuAEAAFbhmhsAAL6Fa5vaNlZuAACAVQg3AADAKoQbAABgFcINAACwCuEGAABYhXADAACsQrgBAABWIdwAAACrEG4AAIBVCDcAAMAqhBsAAGAVwg0AALAK4QYAAFiFcAMAAKziaLhZunSpBg8erOjoaEVHR2vkyJF64403gvPGGPn9fvl8PnXt2lWjR49WWVmZgxUDAIBw52i4ufbaa7VgwQJt27ZN27Zt0z/90z/pX/7lX4IBZtGiRcrLy9OSJUtUWloqr9er1NRU1dXVOVk2AAAIY46GmwkTJmjs2LHq16+f+vXrp/nz56t79+7asmWLjDHKz89Xdna20tPTlZiYqIKCAp04cUJr1qxxsmwAABDGwuaam1OnTmnt2rU6fvy4Ro4cqfLycgUCAaWlpQX3cbvdSklJUUlJyXnPU19fr9ra2pANAAC0HxFOF7Bnzx6NHDlSX331lbp3767XXntNAwYMCAYYj8cTsr/H41FFRcV5z5ebm6u5c+e2as0AALSGPnNev+hjP14wrgUradscX7m54YYbtGvXLm3ZskU/+clPNGnSJO3bty8473K5QvY3xjQZO1tWVpZqamqCW2VlZavVDgAAwo/jKzedO3fW9ddfL0lKSkpSaWmpfvOb32j27NmSpEAgoNjY2OD+1dXVTVZzzuZ2u+V2u1u3aAAAELYcX7n5NmOM6uvrFR8fL6/Xq6KiouBcQ0ODiouLlZyc7GCFAAAgnDm6cvP0009rzJgxiouLU11dndauXat3331XGzdulMvlUmZmpnJycpSQkKCEhATl5OQoMjJSGRkZTpYNAADCmKPh5tNPP9WDDz6oqqoqxcTEaPDgwdq4caNSU1MlSbNmzdLJkyc1bdo0HTt2TCNGjFBhYaGioqKcLBsAAIQxR8PNihUr/uG8y+WS3++X3++/PAUBAIA2L+yuuQEAALgUhBsAAGAVwg0AALAK4QYAAFiFcAMAAKxCuAEAAFZx/PULAAC0hkt5CSXaNlZuAACAVQg3AADAKoQbAABgFcINAACwCuEGAABYhXADAACsQrgBAABWIdwAAACrEG4AAIBVCDcAAMAqhBsAAGAVwg0AALAK4QYAAFiFcAMAAKxCuAEAAFYh3AAAAKsQbgAAgFUINwAAwCoRThcAAAh/fea8ftHHfrxgXAtWAnw3Vm4AAIBVCDcAAMAqhBsAAGAVrrnBZdMWv7O/lJoBnNYW/99H28bKDQAAsArhBgAAWIVwAwAArEK4AQAAVnE03OTm5urmm29WVFSUevbsqR/96Efav39/yD7GGPn9fvl8PnXt2lWjR49WWVmZQxUDAIBw52i4KS4u1vTp07VlyxYVFRXpm2++UVpamo4fPx7cZ9GiRcrLy9OSJUtUWloqr9er1NRU1dXVOVg5AAAIV47eCr5x48aQzytXrlTPnj21fft23X777TLGKD8/X9nZ2UpPT5ckFRQUyOPxaM2aNZo6daoTZQMAgDAWVtfc1NTUSJKuuuoqSVJ5ebkCgYDS0tKC+7jdbqWkpKikpOSc56ivr1dtbW3IBgAA2o+weYifMUYzZ87UqFGjlJiYKEkKBAKSJI/HE7Kvx+NRRUXFOc+Tm5uruXPntm6xAACEGR6W+P/CZuXmiSee0O7du/Wf//mfTeZcLlfIZ2NMk7EzsrKyVFNTE9wqKytbpV4AABCewmLl5qc//ak2bNig9957T9dee21w3Ov1Sjq9ghMbGxscr66ubrKac4bb7Zbb7W7dggEAQNhydOXGGKMnnnhC69at09tvv634+PiQ+fj4eHm9XhUVFQXHGhoaVFxcrOTk5MtdLgAAaAMcXbmZPn261qxZo//+7/9WVFRU8BqbmJgYde3aVS6XS5mZmcrJyVFCQoISEhKUk5OjyMhIZWRkOFk6AAAIU46Gm6VLl0qSRo8eHTK+cuVKPfzww5KkWbNm6eTJk5o2bZqOHTumESNGqLCwUFFRUZe5WjiJt3MDAC6Uo+HGGPOd+7hcLvn9fvn9/tYvCAAAtHlhc7cUAABASyDcAAAAqxBuAACAVQg3AADAKoQbAABgFcINAACwCuEGAABYhXADAACsQrgBAABWIdwAAACrEG4AAIBVCDcAAMAqhBsAAGAVwg0AALAK4QYAAFiFcAMAAKxCuAEAAFYh3AAAAKsQbgAAgFUINwAAwCqEGwAAYBXCDQAAsArhBgAAWIVwAwAArEK4AQAAViHcAAAAqxBuAACAVQg3AADAKoQbAABgFcINAACwSoTTBQAAcD595rzudAlog1i5AQAAViHcAAAAqxBuAACAVQg3AADAKo6Gm/fee08TJkyQz+eTy+XS+vXrQ+aNMfL7/fL5fOratatGjx6tsrIyZ4oFAABtgqPh5vjx47rpppu0ZMmSc84vWrRIeXl5WrJkiUpLS+X1epWamqq6urrLXCkAAGgrHL0VfMyYMRozZsw554wxys/PV3Z2ttLT0yVJBQUF8ng8WrNmjaZOnXo5SwUAAG1E2F5zU15erkAgoLS0tOCY2+1WSkqKSkpKzntcfX29amtrQzYAANB+hG24CQQCkiSPxxMy7vF4gnPnkpubq5iYmOAWFxfXqnUCAIDwErbh5gyXyxXy2RjTZOxsWVlZqqmpCW6VlZWtXSIAAAgjYfv6Ba/XK+n0Ck5sbGxwvLq6uslqztncbrfcbner1wcAAMJT2K7cxMfHy+v1qqioKDjW0NCg4uJiJScnO1gZAAAIZ46u3Hz55Zf68MMPg5/Ly8u1a9cuXXXVVerVq5cyMzOVk5OjhIQEJSQkKCcnR5GRkcrIyHCwagAAEM4cDTfbtm3TD37wg+DnmTNnSpImTZqkP/zhD5o1a5ZOnjypadOm6dixYxoxYoQKCwsVFRXlVMkAACDMuYwxxukiWlNtba1iYmJUU1Oj6Ohop8tp1/rMed3pEgAA5/DxgnFOl9DEpfz7HbbX3AAAAFwMwg0AALAK4QYAAFiFcAMAAKxCuAEAAFYh3AAAAKsQbgAAgFUINwAAwCqEGwAAYBXCDQAAsArhBgAAWMXRF2ei7eH9UACAcMfKDQAAsArhBgAAWIVwAwAArEK4AQAAVuGC4naIi4IBAGe7lH8XPl4wrgUraRms3AAAAKsQbgAAgFUINwAAwCqEGwAAYBXCDQAAsArhBgAAWIVwAwAArEK4AQAAVuEhfpfIqQfiheNDkwAACAes3AAAAKsQbgAAgFUINwAAwCqEGwAAYBUuKG6jeLM3AADnxsoNAACwCuEGAABYhXADAACsQrgBAABWaRPh5oUXXlB8fLy6dOmi4cOH669//avTJQEAgDAV9uHmlVdeUWZmprKzs7Vz507ddtttGjNmjA4fPux0aQAAIAyFfbjJy8vTI488okcffVQ33nij8vPzFRcXp6VLlzpdGgAACENh/ZybhoYGbd++XXPmzAkZT0tLU0lJyTmPqa+vV319ffBzTU2NJKm2trZVamysP9Eq5wUAoC1orX9fz5zXGNPsY8M63Bw9elSnTp2Sx+MJGfd4PAoEAuc8Jjc3V3Pnzm0yHhcX1yo1AgDQnsXkt+756+rqFBMT06xjwjrcnOFyuUI+G2OajJ2RlZWlmTNnBj83Njbq888/V48ePc57zHepra1VXFycKisrFR0dfVHnaCvo1V7tqV96tVd76re992qMUV1dnXw+X7PPF9bh5uqrr1bHjh2brNJUV1c3Wc05w+12y+12h4xdccUVLVJPdHS09X/AzqBXe7WnfunVXu2p3/bca3NXbM4I6wuKO3furOHDh6uoqChkvKioSMnJyQ5VBQAAwllYr9xI0syZM/Xggw8qKSlJI0eO1LJly3T48GE9/vjjTpcGAADCUNiHm3vvvVefffaZnnvuOVVVVSkxMVF/+ctf1Lt378tWg9vt1rPPPtvk6y4b0au92lO/9Gqv9tQvvV48l7mYe6wAAADCVFhfcwMAANBchBsAAGAVwg0AALAK4QYAAFiFcPMdXnjhBcXHx6tLly4aPny4/vrXvzpdUot47733NGHCBPl8PrlcLq1fvz5k3hgjv98vn8+nrl27avTo0SorK3Om2EuQm5urm2++WVFRUerZs6d+9KMfaf/+/SH72NKrJC1dulSDBw8OPghr5MiReuONN4LzNvX6bbm5uXK5XMrMzAyO2dKv3++Xy+UK2bxeb3Delj7P9sknn+iBBx5Qjx49FBkZqSFDhmj79u3BeVt67tOnT5Pfrcvl0vTp0yXZ06ckffPNN/rlL3+p+Ph4de3aVX379tVzzz2nxsbG4D4t1q/Bea1du9Z06tTJLF++3Ozbt8/MmDHDdOvWzVRUVDhd2iX7y1/+YrKzs82rr75qJJnXXnstZH7BggUmKirKvPrqq2bPnj3m3nvvNbGxsaa2ttaZgi/SD3/4Q7Ny5Uqzd+9es2vXLjNu3DjTq1cv8+WXXwb3saVXY4zZsGGDef31183+/fvN/v37zdNPP206depk9u7da4yxq9ezbd261fTp08cMHjzYzJgxIzhuS7/PPvusGThwoKmqqgpu1dXVwXlb+jzj888/N7179zYPP/yw+fvf/27Ky8vNm2++aT788MPgPrb0XF1dHfJ7LSoqMpLMO++8Y4yxp09jjJk3b57p0aOH+fOf/2zKy8vNf/3Xf5nu3bub/Pz84D4t1S/h5h+45ZZbzOOPPx4y1r9/fzNnzhyHKmod3w43jY2Nxuv1mgULFgTHvvrqKxMTE2NefPFFBypsOdXV1UaSKS4uNsbY3esZV155pXnppZes7bWurs4kJCSYoqIik5KSEgw3NvX77LPPmptuuumcczb1ecbs2bPNqFGjzjtvY89nzJgxw1x33XWmsbHRuj7HjRtnpkyZEjKWnp5uHnjgAWNMy/5e+VrqPBoaGrR9+3alpaWFjKelpamkpMShqi6P8vJyBQKBkN7dbrdSUlLafO81NTWSpKuuukqS3b2eOnVKa9eu1fHjxzVy5Ehre50+fbrGjRunO++8M2Tctn4PHjwon8+n+Ph43XfffTp06JAk+/qUpA0bNigpKUl33323evbsqaFDh2r58uXBeRt7lk7/u7N69WpNmTJFLpfLuj5HjRqlt956SwcOHJAkvf/++9q8ebPGjh0rqWV/r2H/hGKnHD16VKdOnWrygk6Px9PkRZ62OdPfuXqvqKhwoqQWYYzRzJkzNWrUKCUmJkqys9c9e/Zo5MiR+uqrr9S9e3e99tprGjBgQPAvB5t6Xbt2rXbs2KHS0tImczb9bkeMGKFVq1apX79++vTTTzVv3jwlJyerrKzMqj7POHTokJYuXaqZM2fq6aef1tatW/Xkk0/K7XbroYcesrJnSVq/fr2++OILPfzww5Ls+jMsSbNnz1ZNTY369++vjh076tSpU5o/f74mTpwoqWX7Jdx8B5fLFfLZGNNkzFa29f7EE09o9+7d2rx5c5M5m3q94YYbtGvXLn3xxRd69dVXNWnSJBUXFwfnbem1srJSM2bMUGFhobp06XLe/Wzod8yYMcH/HjRokEaOHKnrrrtOBQUF+v73vy/Jjj7PaGxsVFJSknJyciRJQ4cOVVlZmZYuXaqHHnoouJ9NPUvSihUrNGbMGPl8vpBxW/p85ZVXtHr1aq1Zs0YDBw7Url27lJmZKZ/Pp0mTJgX3a4l++VrqPK6++mp17NixySpNdXV1k1RpmzN3YdjU+09/+lNt2LBB77zzjq699trguI29du7cWddff72SkpKUm5urm266Sb/5zW+s63X79u2qrq7W8OHDFRERoYiICBUXF+u3v/2tIiIigj3Z0u/ZunXrpkGDBungwYPW/V4lKTY2VgMGDAgZu/HGG3X48GFJdv5/W1FRoTfffFOPPvpocMy2Pn/xi19ozpw5uu+++zRo0CA9+OCDeuqpp5SbmyupZfsl3JxH586dNXz4cBUVFYWMFxUVKTk52aGqLo/4+Hh5vd6Q3hsaGlRcXNzmejfG6IknntC6dev09ttvKz4+PmTepl7Pxxij+vp663q94447tGfPHu3atSu4JSUl6f7779euXbvUt29fq/o9W319vT744APFxsZa93uVpFtvvbXJIxsOHDgQfGGyjT2vXLlSPXv21Lhx44JjtvV54sQJdegQGjs6duwYvBW8Rfu9uGue24czt4KvWLHC7Nu3z2RmZppu3bqZjz/+2OnSLlldXZ3ZuXOn2blzp5Fk8vLyzM6dO4O3uS9YsMDExMSYdevWmT179piJEye2ydsPf/KTn5iYmBjz7rvvhtxueeLEieA+tvRqjDFZWVnmvffeM+Xl5Wb37t3m6aefNh06dDCFhYXGGLt6PZez75Yyxp5+f/azn5l3333XHDp0yGzZssWMHz/eREVFBf8usqXPM7Zu3WoiIiLM/PnzzcGDB83LL79sIiMjzerVq4P72NTzqVOnTK9evczs2bObzNnU56RJk8z3vve94K3g69atM1dffbWZNWtWcJ+W6pdw8x1+97vfmd69e5vOnTubYcOGBW8hbuveeecdI6nJNmnSJGPM6Vvynn32WeP1eo3b7Ta333672bNnj7NFX4Rz9SjJrFy5MriPLb0aY8yUKVOCf16vueYac8cddwSDjTF29Xou3w43tvR75lkfnTp1Mj6fz6Snp5uysrLgvC19nu1Pf/qTSUxMNG632/Tv398sW7YsZN6mnjdt2mQkmf379zeZs6nP2tpaM2PGDNOrVy/TpUsX07dvX5OdnW3q6+uD+7RUvy5jjLmY5SUAAIBwxDU3AADAKoQbAABgFcINAACwCuEGAABYhXADAACsQrgBAABWIdwAAACrEG4AAIBVCDcAAMAqhBsAAGAVwg2AsLNx40aNGjVKV1xxhXr06KHx48fro48+Cs6XlJRoyJAh6tKli5KSkrR+/Xq5XC7t2rUruM++ffs0duxYde/eXR6PRw8++KCOHj3qQDcALjfCDYCwc/z4cc2cOVOlpaV666231KFDB/34xz9WY2Oj6urqNGHCBA0aNEg7duzQr371K82ePTvk+KqqKqWkpGjIkCHatm2bNm7cqE8//VT33HOPQx0BuJx4cSaAsPe///u/6tmzp/bs2aPNmzfrl7/8pY4cOaIuXbpIkl566SU99thj2rlzp4YMGaJnnnlGf//737Vp06bgOY4cOaK4uDjt379f/fr1c6oVAJcBKzcAws5HH32kjIwM9e3bV9HR0YqPj5ckHT58WPv379fgwYODwUaSbrnllpDjt2/frnfeeUfdu3cPbv379w+eG4DdIpwuAAC+bcKECYqLi9Py5cvl8/nU2NioxMRENTQ0yBgjl8sVsv+3F6AbGxs1YcIELVy4sMm5Y2NjW7V2AM4j3AAIK5999pk++OAD/f73v9dtt90mSdq8eXNwvn///nr55ZdVX18vt9stSdq2bVvIOYYNG6ZXX31Vffr0UUQEf80B7Q1fSwEIK1deeaV69OihZcuW6cMPP9Tbb7+tmTNnBuczMjLU2Niof/3Xf9UHH3ygTZs26de//rUkBVd0pk+frs8//1wTJ07U1q1bdejQIRUWFmrKlCk6deqUI30BuHwINwDCSocOHbR27Vpt375diYmJeuqpp7R48eLgfHR0tP70pz9p165dGjJkiLKzs/XMM89IUvA6HJ/Pp7/97W86deqUfvjDHyoxMVEzZsxQTEyMOnTgrz3AdtwtBaDNe/nllzV58mTV1NSoa9euTpcDwGF8GQ2gzVm1apX69u2r733ve3r//fc1e/Zs3XPPPQQbAJIINwDaoEAgoGeeeUaBQECxsbG6++67NX/+fKfLAhAm+FoKAABYhSvrAACAVQg3AADAKoQbAABgFcINAACwCuEGAABYhXADAACsQrgBAABWIdwAAACrEG4AAIBV/g9FP1Uxlg5XRAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "ages_pop_3 = pd.read_csv(\"ages_population3.csv\")\n",
+    "plt.hist(ages_pop_3[\"observation\"], bins = 30)\n",
+    "plt.xlabel(\"age\")\n",
+    "plt.ylabel(\"freq\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean is:  41.989\n",
+      "Std is:  16.144705959865934\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "print(\"Mean is: \", ages_pop_3[\"observation\"].mean())\n",
+    "print(\"Std is: \",ages_pop_3[\"observation\"].std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\" Now with a mean age of 41.989, this indicates indicates that the central tendency of this dataset is more similar with the one in Step 1.\n",
+    "And also sugests a similar population as in Step 1.\n",
+    "\n",
+    "With a std of  16.144, this surelly puts us in a population with the age range really similar to step 1, a dataset not so tightly clustered around the mean.\n",
+    "Now we have values more spread out.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the first quartile is:  30.0\n",
+      "the second quartile is:  40.0\n",
+      "the third quartile is:  53.0\n",
+      "the fourth quartile is:  77.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "q1 = np.quantile(ages_pop_3, 0.25)\n",
+    "q2 = np.quantile(ages_pop_3, 0.50)\n",
+    "q3 = np.quantile(ages_pop_3, 0.75)\n",
+    "q4 = np.quantile(ages_pop_3, 1)\n",
+    "\n",
+    "print(\"the first quartile is: \", q1)\n",
+    "print(\"the second quartile is: \", q2)\n",
+    "print(\"the third quartile is: \", q3)\n",
+    "print(\"the fourth quartile is: \", q4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"the median and mean are really similar, not equal, probably because of some outliers/skewness - lot of observations between q3 and q4\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The percentile 10 is:  22.0\n",
+      "The percentile 15 is:  25.0\n",
+      "The percentile 85 is:  64.0\n",
+      "The percentile 90 is:  67.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "p10 = np.quantile(ages_pop_3, 0.10)\n",
+    "p15 = np.quantile(ages_pop_3, 0.15)\n",
+    "p85 = np.quantile(ages_pop_3, 0.85)\n",
+    "p90 = np.quantile(ages_pop_3, 0.90)\n",
+    "\n",
+    "\n",
+    "print(\"The percentile 10 is: \", p10)\n",
+    "print(\"The percentile 15 is: \", p15)\n",
+    "print(\"The percentile 85 is: \", p85)\n",
+    "print(\"The percentile 90 is: \", p90)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"maybe if we had younger ages in p10 and p15, we could get a sample with a younger mean.\n",
+    "p10 is 22 years away from the mean, as for p90 is 27 years away, pushing the mean value a litlle up\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus challenge\n",
+    "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/your-code/ages_population.csv b/your-code/ages_population.csv
new file mode 100644
index 0000000..64d8a0a
--- /dev/null
+++ b/your-code/ages_population.csv
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diff --git a/your-code/ages_population2.csv b/your-code/ages_population2.csv
new file mode 100644
index 0000000..00860cb
--- /dev/null
+++ b/your-code/ages_population2.csv
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diff --git a/your-code/ages_population3.csv b/your-code/ages_population3.csv
new file mode 100644
index 0000000..6339a1d
--- /dev/null
+++ b/your-code/ages_population3.csv
@@ -0,0 +1,1001 @@
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diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index a0a5b66..58cb29f 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -11,11 +11,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "# Libraries\n",
+    "import random\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
    ]
   },
   {
@@ -29,11 +33,119 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Roll</th>\n",
+       "      <th>Result</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Roll  Result\n",
+       "0     1       5\n",
+       "1     2       1\n",
+       "2     3       1\n",
+       "3     4       6\n",
+       "4     5       1\n",
+       "5     6       3\n",
+       "6     7       2\n",
+       "7     8       4\n",
+       "8     9       2\n",
+       "9    10       6"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "def dice(rolls, sides):\n",
+    "    results = random.choices(range(1, sides + 1), k = rolls)\n",
+    "    \n",
+    "    df = pd.DataFrame({'Roll': range(1, rolls + 1), 'Result': results})\n",
+    "    return df\n",
+    "\n",
+    "df = pd.DataFrame(dice(rolls=10, sides=6))\n",
+    "df"
    ]
   },
   {
@@ -45,11 +157,136 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Roll</th>\n",
+       "      <th>Result</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Roll  Result\n",
+       "1     2       1\n",
+       "2     3       1\n",
+       "4     5       1\n",
+       "6     7       2\n",
+       "8     9       2\n",
+       "5     6       3\n",
+       "7     8       4\n",
+       "0     1       5\n",
+       "3     4       6\n",
+       "9    10       6"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "sorted_df = df.sort_values(by = \"Result\")\n",
+    "sorted_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "x = sorted_df[\"Roll\"]\n",
+    "y = sorted_df[\"Result\"]\n",
+    "\n",
+    "plt.scatter(x,y)\n",
+    "plt.show()"
    ]
   },
   {
@@ -61,11 +298,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 75,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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iNGVyfkbSdg8yUTkOe5oRZdnhJXOsU6Gjdbt96HT5AMQ3Uel0+dDt9sX88YlI+5K9kBZgonJCLKilkYiTjuLMNFhMse/0t5oNKLKnDXkuIqJoJPNEWoGJygkMTKhlokJDiSLXeJymCCyoJaLxqEniHT8CE5UT4M4fGkk861MEUaTLExUiipbbF0BdR/hnB09UNEx8cfe19sAXCCkcDalJIhKVShbUEtEY7W7pgSwD+TYz8m1mpcMZMyYqJ1CWnQ5bmgH+oIz9rZwQSgPEILbJ/RNk46Eyn0PfiGhsdmng2gdgonJCkiRxngodIxSScTARVz954SToYLsLoZAct+chIu3RQscPwERlVGaw84eOcqTHgz5/EAadFGlhj4ey7HQYdBL6/EEc6fHE7XmISHuSfXS+wERlFEQ2yoJaEkQXzoRcCwz6+P01Mup1mJBjGfKcREQnEgzJ2N3cAyC5C2kBJiqjErn6aXFClnn8TgM1I/FYRni0Ci4nJKIoHexwoc8fRJpRF9fr6URgojIKUwszYNBJ6Hb70ezg8TsBdQmYoSJERunzRIWIRklc+0wvskOvkxSOZnyYqIyC2aDHlIJwUSOvfwgA6trDHWCVcez4EcRziOckIjoR8W9Vsl/7AExURo2j9GmwRMxQEbickIiipZWOH4CJyqhxlD4JvkAI9V19ABJToyJmqdR39XHoIBGNilZmqABMVEaNLcokHO50IxiSYTXpEzLtscBmhsWkRzAk43CnO+7PR0TJrb3Xi9YeLyQJmF5kUzqccWOiMkoiUTnU4UaPx69wNKSkyLVPvhWSFP8iNUmSeP1DRKMmTv4n5VphNcd+s3uiMVEZpRyrCcWZaQCAPS09CkdDShJFrWJqbCIMJCosqCWi49PStQ/ARCUqLKglYOBUIxH1KcJA5w9PVIjo+Go0VEgLMFGJSqROhS3KKU1MiBVFrokgkiJOpyWiExEvpmcUJ399CsBEJSqRUfo8UUlptQlsTRZYo0JEo+HxB3Gg/wVNVXGmwtHEBhOVKIirnz0tPQgE2Saaino8frT1eAEAkxKYqIjnau3xspibiEa070gvgiEZOVYTCu3x70pMBCYqUZiQY4HVpIc3EOIr2xR1sD3cHpyXYYY9zZiw581MNyIvwzQkBiKio+1qdgAIv7BORFdiIjBRiYJOJ2E6C2pTWq0Yna/Akq+B5YTs/CGi4Q2MztdGfQrARCVq7PxJbZGOnwQW0gqVeez8IaLj09LofIGJSpTY+ZPaErnj52gV+SyoJaKRhUIyaprDc760UkgLMFGJWqTzp8kJWZYVjoYSTbQHK5KosPOHiI6joasPvd4ATHqdIqe+8cJEJUrTCm3QSUCHyxfp/qDUIMuywlc/A7NUmCQT0dFEIe1JRRkw6rXzz7t2/ksSJN2kj0wJZZ1Kamnr9aLXG4BOAspzLAl//gm5FkgS0OsNoK2XSTIRDbUrcu2jnfoUgInKmHCTcmqq67/2Kcu2wGzQJ/z5zQY9yrLTh8RCRCRobcePwERlDKpYUJuSlLz2Edj5Q0QjqYmMzmeikvJEQW0NT1RSipIdPwILaoloON1uHxq7+wAAMzTUmgxEmagEAgH85S9/QUtLS7ziSQpikE5tuwtuX0DhaChRxP4MJYa9CeI0p5aJChENItqSy3PSEzo1OxGiSlQMBgP+7d/+DV5vahfyFdjSkJdhhiyH9/5Qaqjrnwhb0X/9ooTIdNo2TqclogGRjclF2jpNAcZw9XPKKadg69atcQgluXCTcmoJBEM43BnesVOhYI2KSFQOd7q5GJOIIiKFtBq79gEAQ7R/4N///d+xfPly1NfXY8GCBbBah/7QnjNnTsyCU7OqYjs27G1jnUqKaOzugz8oI82oQ7E9TbE4SjLTYTbo4A2E0Njdh4m52hnqRERjJ/4t0lrHDzCGROXaa68FANx5552R90mSBFmWIUkSgsFg7KJTMVGnws6f1CBqQiblWqHTKbeRVKeTUJFnxe6WHtS2u5ioEBF8gRD2tfbPUOGJClBXVxePOJLOzP5vht0tPQiFZEX/8aL4E3NL1DCWWiQqdW0unDtN6WiISGn7W3vhD8qwpxlQmpWudDgxF3WiMnHixHjEkXQq8jKQZtTB7QviUKdb0ZZVir/aSCGt8l/nSEFtOwtqiWhQIW2xHZKkvRfNY5qj8te//hWnn346SkpKcOjQIQDAww8/jJdffjmmwamZXidhWiGvf1LFwAwV5Tp+BM5SIaLBIvUpGrz2AcaQqKxcuRLLly/HokWL0N3dHalJycrKwsMPPxzr+FRtoPPHoXAkFG91Cm5NPpq4fuIYfSICBl4sa20irRB1ovLHP/4Rjz/+OH784x9Drx/Yd7Jw4ULs2LEjpsGpHUfpp4Y+XxBNDg8AYLIKalTEGP0mhwd9vtQoXiei4cmyHLn60WLHDzCGRKWurg7z588/5v1msxkuV2q9whsYpc+hb1p2sCP8fZ1tMSLLYlI4GiDbakKWJTx5UsRGRKmp2eGBo88Pg07C1ELlr6bjIepEpaKiYtiBb+vWrUNVVVUsYkoa0/onALY4PejoTe1pvVpWq6JrH4F1KkQEDJzoTynIUGSreyJE3fVzzz33YNmyZfB4PJBlGZ9//jmee+45rFixAn/+85/jEaNqZZgNmJRrwcEON2qae3DGVLPSIVEcqGF0/tEq8qzYcribo/SJUpzWr32AMSQq3/rWtxAIBPD9738fbrcbN9xwA0pLS/HII4/guuuui0eMqlZVYu9PVJw4Y2qe0uFQHIhhb2qYoSJU5nE5IRFpe3S+EHWiAgC33XYbbrvtNrS3tyMUCqGgoCDWcSWNGUV2vL6jhTt/NGygNVk9iYo43eHVD1Fqq2nhicqIWltbsWfPHkiSBEmSkJ+fH8u4kkakRZmdP5pVp8YTlXzWqBCluh6PH4c6wstStdqaDIyhmNbpdOKmm25CSUkJzj77bJx11lkoKSnBN77xDTgcqTdPRCQq+9t64fGzVVRrulw+dLv9AMJ7ftRCxNLt9qPL5VM4GiJSwu6WcMdpcWYasq3KdyTGS9SJyne+8x189tlneO2119Dd3Q2Hw4FXX30VGzduxG233RaPGFWtyJ6GbIsRwZCM/a0sbNQaMaa+NCsdaUb1VNSnm/QoyQxvcWadClFq0vLG5MGiTlRee+01PPnkk7joootgt9ths9lw0UUX4fHHH8drr70WjxhVTZKkyJEbr3+0R42tyUJF//UPO3+IUlMqFNICY0hUcnNzkZmZecz7MzMzkZ2dHZOgkk1kQi0LajVHjYW0AmepEKW2wcsItSzqROUnP/kJli9fjubm5sj7WlpacM899+C+++4bcyArVqyAJEm4++67x/wYShnY+cNERWvUnaiw84coVQWCoUiNitavfkbV9TN//vwhq6P37duHiRMnYsKECQCAw4cPw2w2o62tDf/6r/8adRBffPEFHnvsMcyZMyfqP6sGIputaXJClmVNrtlOVWrs+BHY+UOUuuraXfAFQrCa9JiQY1E6nLgaVaKyePHiuAXQ29uLG2+8EY8//jgefPDB436s1+uF1zswqt7pVMcJxuT8DJj0OvR4A2jo6kO5xr9pUkUoJA8kKiqaSitUDrr6CYVk6HRMkCm57Whw4C//PIjvXzQNBfY0pcNRNXGCP73Yrvm/+6NKVO6///64BbBs2TJceumluOCCC06YqKxYsQIPPPBA3GIZK5NBh6mFGdjZ5MTOJicTFY1ocvTBGwjBqJdQmp2udDjHKM1Kh1EvwRsIodnpQWmW+mIkisZP136JLYe7kWbU4cHFs5UOR9UihbQav/YBxlCjMlhvby+cTueQt2g8//zz2Lx5M1asWDGqj7/33nvhcDgib/X19WMJOy7EN0sN61Q0Q5ymTMy1Qq/CVywGvS5y5MvOH0p2B9p6seVwNwDglW3N8AY4l+p4Ijt+NN7xA4whUamrq8Oll14Kq9Ua6fTJzs5GVlZWVF0/9fX1uOuuu/C3v/0NaWmjO+Izm82w2+1D3tRiBjt/NEfNhbQCC2pJK1Zvboj8f0efH+/WtCoYjbrJspxSJypRj9C/8cYbAQBPPvkkCgsLx1w4umnTJrS2tmLBggWR9wWDQWzYsAGPPvoovF4v9Hr1DNg6EY7S1x4xQ6VSxYlKZb4VqBmIlSgZBUMyVm9uBABML7Jhd0sPXtzUgEWzixWOTJ3aerzocPmgk4BpRTalw4m7qBOV7du3Y9OmTZg2bdq4nvj888/Hjh07hrzvW9/6FqZPn44f/OAHSZWkAAMnKo3dfXD0+ZGZblQ4IhovNXf8CJWcpUIa8M8DHWh2eGBPM+C/r52HSx75EOv3tqG1x4MCG4tqj7az/+S+Mj9DVROz4yXqq5+TTz45JrUhNpsNs2bNGvJmtVqRm5uLWbNmjfvxEy0z3RgpZmSdijYMXP2or+NH4NA30oIXN4X/TbliXglmFNsxf0IWgiEZL29pUjgydUqV0flC1Ccqf/7zn3H77bejsbERs2bNgtE49OQgWWehxEJViR2N3X3Y1eTEVytzlQ6HxsEbCKKhK7yVVNU1Kv2nPQ1dbngDQZgN2n91RdrS4/HjjZ0tAIAl1WWR/91yuBsvbmrAd86s4Gyqo4gSA61PpBWiTlTa2tpw4MABfOtb34q8T5KkyKCzYHDsldoffPDBmP+sGlQV2/H2riMsqNWAwx1uhGTAZjYgL0O9W0nzM8zIMBvQ6w3gcIcbUwu1f19N2vL6jmZ4/CFMzrdiXnkWAODyOSX42au7sOdID3Y2OTGr9Ni1LakslTp+gDFc/dx6662YP38+/vnPf6K2thZ1dXVD/jeViW8aXv0kP7GRuCLfqupXc5IkRU58uEWZktGqTeEi2iULyiJ/1zItRnytqhAA8OKmhhH/bCpy+wKRq15e/Yzg0KFDWLt2LaZMmRKPeJKa+KbZd6QXvkAIJsO4xtSQgpKhNVmoyLNiR6ODdSqUdA51uPD5wU5IEnDV/NIhv7e0ugyvbW/G2m1N+NGiGfx52m9PSw9kGci3mZFvMysdTkJE/ZU/77zzsG3btnjEkvTKstNhMxvgC4ZwgAO4klpdm3pH5x8tsvOHLcqUZFb1tySfMSUPxZlDJyufOTUP+TYzOl0+vL+HM1WEVNmYPFjUJyqXX345vve972HHjh2YPXv2McW0V1xxRcyCSzaSJGFGiR2f13WiptmZUt9IWlM36OpH7dj5Q8koFJIjQ96WLig75vcNeh2uml+KxzbUYtWmBlw0syjRIapSKg16E6JOVG6//XYAwM9+9rNjfm+8xbRaUFUcTlR2NTlxdbXS0dBY1baHT8TUPOxNEKc+rFGhZPJZXScauvpgMxtwYdXwSciS6jI8tqEW7+1uRUevF7kZqXHVcTw1KVZIC4zh6icUCo34lupJCjCQ5bLzJ3k5+vxo7/UBACYlQaIyKS+876e91wunx69wNESjs6r/NOXSOcVINw3fVj+tyIbZpZkIhGSs3caZKsGQjN0tPQCAquLU6fBjdVKMRUbpNzshy7LC0dBYHOw/mSiwhVt/1c6WZowU1bFOhZKByxvA6zuaAQx/7TOY+H12/4SLj92+INKMOlUPooy1qH8KD3flM9hPf/rTMQejBVMKMmDQSeh2+9Hi9BxTIEbqlwyj849WmWdFW48Xde0uzO2fRUGkVm982QK3L4hJuRYsmHj8ZbZXzC3Bg6/tws4mJ3a3ODG9KHWuPI5W0xw+TZlWZFflRvd4iTpRWbNmzZBf+/1+1NXVwWAwYPLkySmfqKQZ9Zicn4E9R3qwq8nJRCUJ1SbB6PyjVeZb8VldJ+tUKCmIa5+rq8tOOKco22rCedML8ObOI1i1qQE/vrQqESGq0q5mB4DUKqQFxpCobNmy5Zj3OZ1O3HLLLbjqqqtiElSyqyqxRxKV82cUKh0ORam2LXkKaQV2/lCyaOhy45MDHQCAq6tLT/DRYUsXlOPNnUewZksTfnDxdBj0qVm1MNDxkzr1KUCMalTsdjt+9rOf4b777ovFwyU9ke3WtLCgNhkl07A3QZz+1LVzfg+p25r+2SmnVuaiLNsyqj9zzrR85FpNaO/1YsO+tniGp2qpNjpfiFla2t3dDYfDEauHS2piforIfil5yLKcVDNUhMiJSpuLRdykWrIsR659TlREO5hRr8OV88KnL6laVNvR68URpxeSFK5RSSVRX/384Q9/GPJrWZbR3NyMv/71r7j44otjFlgym9F/LHeww41ebyApOkcorLXHC7cvCL1OQvkoX+2pwYQcC/Q6CS5fEK09XhTa05QOiegYmw514WCHGxaTHhfPim6A25IFpXjy4zq8s6sV3W4fsizqXRYaD6KQdmKOJeX+TYn6v/a///u/h/xap9MhPz8fN998M+69996YBZbMcjPMKLKnocXpwe5mJxZOylE6JBql2v723gk5lqTaLWIy6FCenY6DHW7UtrmYqJAqidOQRbOLYY3yH9uZJZmYUWxHTbMTr2xrwk2nTopDhOoVKaRNsWsfYAyJSl1dXTzi0JyqEjtanB7UMFFJKslYnyJU5FlxsMONunYXTp2cq3Q4REP0+YJ4bXt4dsqS6tFf+wy2pLoUD77mxIubG1MuUREnKqnW8QNw4FvciOsfTqhNLqLjJzkTFRbUknq9tasFPd4ASrPScUrF2F68XTmvFHqdhG313djf2hPjCNUt0vGTgicqUScqLpcL9913H0477TRMmTIFlZWVQ94orKo4EwALapNNUp+o9Bf/1nI6LamQuPZZsqAMujEOK8u3mXHutPz+x2uMWWxq5/EHsb//RVQqLruN+urnO9/5DtavX4+bbroJxcXFJxzWk6pE1ru7pQeBYChl+/6TTWQqbRImKpWcpUIq1eLw4OP97QDC1zfjsaS6DO/UtGLNlgbcc9G0lJjQuu9IL4IhGdkWI4pSsP4s6kRl3bp1eO2113D66afHIx7NmJhjgcWkh9sXxMEOF6YUpNaAnmTkD4ZwuNMNILlakwVxCnS40w1/MAQjk2NSidVbGhCSga9MysHE3PH93TpvRgGyLEYccXrx0f52nH1SfoyiVK/BG5NT8XAg6p9k2dnZyMlhceiJ6HQSpheFk5OdvP5JCg1dfQiEZKQb9Un5qqXInoZ0ox6BkIyGrj6lwyEC0D87JXLtM77TFAAwG/S4Ym4JAEQeV+tEreOMFJufIkSdqPz85z/HT3/6U7jd7njEoymDNymT+oki1Io8a1K+atHpJEyKXP+woJbUYWt9Nw60uZBm1GHR7OKYPKYYFvfmzhY4Pf6YPKaapXIhLTCGq5+HHnoIBw4cQGFhISZNmgSj0Tjk9zdv3hyz4JKdKKgVbWWkbqIINRmvfYTKPCtqmp2obXPhvOlKR0M0sIDw4plFsKUZT/DRozO7NBNTCzKwr7UXr21vxvVfmRCTx1UjWZaHXP2koqgTlcWLF8chDG2KtCjz6icp1CZxIa0g6lS4RZnUwOMPYu3WJgDhbp9YkSQJSxaU4VfrdmPVpgZNJyoNXX3o8QZg0uswOT95NrrHUtSJyv333x+PODRpepEdOglo7/WitceDAlvy1T2kkrq25G1NFgbv/CFS2rs1rXB6AijOTMNpk/Ni+thXzS/Fb97YjY2HulDX7krqv7fHI2ocpxZmpGyBfGr+VydIukkf+cvD6x/1S+YZKoK4tmKLMqmBuPa5an5pzNuIC+1pOHNquONn9WbtFtVGNian4PwUgYlKnHGTcnJweQNocXoAAJV5yXu8Kq6tWpweuLwBhaOhVNba48H6vW0AYnvtM5goql29uRGhkDa3hqd6fQrARCXu2PmTHA52hE8gcq0mZFpiU/CnhCyLCTnW8FZZ8d9EpISXtzQhGJIxf0JW3GorvlZVCFuaAY3dffi0tiMuz6E08SI3FSfSCkxU4qwqcqLiUDgSOp5aDdSnCBWcUEsKk2U5MjJ/aZxOUwAgzajH5f0zVV7U4EwVh9uPxu7wTCQmKhQ34kSlrt2FPl9Q4WhoJFqoTxEinT8sqCWF7GxyYs+RHpgMOlw2pySuzyU2Ma/7sgW9GrvurGkJn6aUZacjMz15T3rHa1RdP8uXLx/1A/7+978fczBaVGBLQ16GCe29Puw50oN55VlKh0TDiCQqSTxDReCJCilNnG5cWFUY939gqydkoTLPitp2F9btaMY1C8vj+nyJxGufsFElKlu2bBnVgyXjNM9EmFFsx4f72rGryclERaW0MENFqOQsFVKQLxDCy1vDm43jVUQ7mJip8ts39+DFTQ3aSlTY8QNglInK+++/H+84NK2qJJyo1LCgVpVkWUZd/wr1Sg0MVBL/DXVtvZBlmS8gKKHe39OKLrcf+TYzzpwS29kpI7lqfil+99YefFbXifpON8pzLAl53nhL9dH5AmtUEiBSUMtERZU6XD44PQFIEjBBAz/gJuZaIEmA0xNAp8undDiUYsS1z9XzS2FI0ICykqx0nN4/UG6VRmaq+AIh7G8Nv4DiicooXH311aN+wNWrV485GK0S32Q1zU6EQjJ0MR58ROMjajlKs9KRZtQrHM34pRn1KMlMR2N3H+raXcjNMCsdEqWIjl4v3t/dCiAx1z6DLVlQio/2t2PV5gbced7UpP85e6CtF75gCLY0A8qy05UOR1GjSlQyMzPjHYemVeRZYTbo4PYFcajTrYnOEi3Rwuj8o1XmW9HY3YfaNhcWTspROhxKES9vbUIgJGNOWSZOKrQl9LkvmlmEDPNO1Hf24YuDnTilMjehzx9rgwtpU/36dlSJylNPPRXvODTNoNdhepEN2xocqGl2auofRC3QUiGtUJFnxYf72llQSwklrl1Ey3AiWUwGLJpdhH9sbMCqzQ1Jn6jUsJA2YswXiG1tbfjoo4/w8ccfo62tLZYxaRJH6atXXXv4HlhLCeRAi3KvwpFQqqhpdmJnkxNGvYQr5sZ3dspIli4Id/y8vqMFbl9yz1Rhx8+AqBMVl8uFW2+9FcXFxTjrrLNw5plnoqSkBN/+9rfhdrvjEaMmcJS+eokaFS10/AiRzh+eqFCCrOovoj1/eiGy+9c4JNrJk7IxIceCXm8Ab+5sUSSGWJBleSBRSfGOH2AMicry5cuxfv16vPLKK+ju7kZ3dzdefvllrF+/Hv/xH/8Rjxg1YXBBLalHMCTjYEc4wdbSiYq4xjrY4UZQo8vaSD38wRBe2toEIPFFtINJkoSrq0sBAKs2NSoWx3g1Ozzodvth0EmYUqCdF1BjFXWismrVKjzxxBO45JJLYLfbYbfbsWjRIjz++ON48cUX4xGjJkzvT1SaHR62jKpIU3cffIEQTAYdSrK0U1lfkpUOk14HXyCEpv5dIUTxsmFvG9p7vci1mnDOtHxFYxH1MR8faE/a733xgnZKQYYmOhHHK+pExe12o7Cw8Jj3FxQU8OrnODLMBkzMDc/o4KmKeohi00m5FuiTvJ1xML1Oiny/saCW4k0U0V45rxTGBM1OGUl5jgWnVORAloE1W5LzVIWj84eK+jvq1FNPxf333w+PxxN5X19fHx544AGceuqpMQ1Oa6pYUKs6YiKtlq59hEhBbRsLail+ut0+vLMrPDslnpuSoyHieHFTA2Q5+a4+WUg7VNSJysMPP4xPPvkEZWVlOP/883HBBRegvLwcn3zyCR555JF4xKgZrFNRHy0W0gosqKVEeGVbE3zBEGYU21VT+HnJ7GKkG/Woa3dh8+FupcOJWg0LaYcY1RyVwWbPno19+/bhb3/7G3bv3g1ZlnHdddfhxhtvRHq6du7442EGR+mrjrgW0eKJCpcTUiKIkflqOU0Bwlftl8wuwurNjVi1uQELJmYrHdKo9XoDkQJ/Xv2ERZWo+P1+TJs2Da+++ipuu+22eMWkWSI73t/aC28gCLOBRVJKq23T3rA3oSJfzFJhokLxsb+1B9saHDDoJFw5T5nZKSNZWl2G1Zsb8cq2Jvz0sqqkKUrd3f9CtsiehhyF2rzVJqqrH6PRCK/Xm/LjfMeqODMNWRYjAiEZ+46wbkBpHn8QTY5wV4AWT1TEf1Njdx88/qDC0ZAWvdjfAnzOtHzkqWyn1Fcrc1GalY4eTwBv7zqidDijxvkpx4q6RuW73/0ufv3rXyMQSO6pf0qQJAkzinj9oxaHOtyQZcCeZtDkK5dcqwm2NANkOfzfShRLwZCMNVvUd+0j6HQDM1XE9VQy4Oj8Y0Vdo/LZZ5/h3XffxVtvvYXZs2fDah36SpTbk4+vqsSOf9Z2sPNHBSKj8/MzNHlKKEkSKvOs2NbgQF17L6YVJXZJHGnbR/vbccTpRZbFiHOnFygdzrCuri7DH9/bjw/3teGI04NCe5rSIZ0QW5OPFXWikpWVhSVLlsQjlpRQxYJa1RBFppM1eO0jVOZnYFuDgwW1FHPilOLKuSWqrberyLNi4cRsbDzUhTVbGnH72ZOVDum4AsEQdrf0AODVz2BRJyrcpDw+Mwa1KMuyrMlX8smirk27HT/CwCwVJioUO44+P97q36Wj5Mj80ViyoAwbD3Vh1aYG/OtZlar+mXuwwwVvIASLSY+JORalw1ENZUcIpqApBRkw6iX0eAJo6ErO8c5aEWlNzk+BRIUnKhRDr21vhjcQwkmFGZhdmql0OMd16ZximA067GvtxY5Gh9LhHNfO/muf6UU26DQ0KXu8mKgkmMmgw9SCcK0Ar3+UVafhGSpCBWepUByIkflLqstUfUIBAPY0Iy6aWQRA/UW17PgZHhMVBYhvQk6oVU632xdZDjkpV/uJSqfLh243l2HS+NW1u7DpUBd0EnDV/FKlwxkVcT21dlsTvAH1tuqLQtqqYnWfUiUaExUFzODOH8WJ05Qiexqs5qhLtZKG1WxAoT0834LXPxQLq/pPJc46KR8FSdBFAwBnTMlDod2Mbrcf79W0Kh3OiGqaWUg7nHElKoMXE9LosfNHeQM7frR7miJU5nHnD8VGKCRj9aBrn2Sh10m4an44XnFtpTatPR6093qhk4BphRwlMFjUiUooFMLPf/5zlJaWIiMjA7W1tQCA++67D0888UTMA9Qikag0dPXB0edXOJrUlAr1KQJH6VOs/LO2A00OD2xpBnytqlDpcKKydEH4mur9PW1o6/EqHM2xxAl7RZ4V6SZ1tnsrJepE5cEHH8TTTz+N3/zmNzCZBqZ5zp49G3/+85+jeqyVK1dizpw5sNvtsNvtOPXUU7Fu3bpoQ0o6mRYjSrPCCxx381RFEbUp0JoscDkhxYq49rl8bknS7M4RphTYMLc8C8GQjJe3NiodzjEGrn1Yn3K0qBOVZ555Bo899hhuvPFG6PUD36hz5szB7t27o3qssrIy/OpXv8LGjRuxceNGnHfeebjyyiuxc+fOaMNKOtykrKzaFLr6iXT+cJYKjUOvN4B1X4Znp6hxZP5oiLhXbVZfoiL+LZhRzGufo0WdqDQ2NmLKlCnHvD8UCsHvj+4a4/LLL8eiRYtw0kkn4aSTTsIvfvELZGRk4NNPP402rKQjiqVYUJt4oZCMg5GrnwyFo4k/kagcbHchFJIVjkZdvmx04B8b6xHk5+WEXt/RjD5/EJV5Vswvz1I6nDG5fE4xTHodapqd2Nmkrpkqu/rj4Y6fY0WdqMycORMffvjhMe//v//7P8yfP3/MgQSDQTz//PNwuVw49dRTh/0Yr9cLp9M55C1ZiW/Gmpbk/W9IVkd6POjzB2HQSSjLTlc6nLgrz7HAoJPQ5w/iSA8L4IVgSMZtz2zE91/cjj+9v1/pcFRPXPssWaD+2SkjybKYcEFVeC/Rqk3qOVXp8wUjNWTs+DlW1H2Z999/P2666SY0NjYiFAph9erV2LNnD5555hm8+uqrUQewY8cOnHrqqfB4PMjIyMCaNWtQVVU17MeuWLECDzzwQNTPoUYiUdnb0gt/MASjnp3iiSLGyU/ItaTE592o12FCjgW17S7UtblQnKn95Gw0PtrfjmZHOHF7+J29+EpFDr5amatwVOpU3+nGZ3WdkJJodspIli4ow+s7WvDy1kbcu2i6Kn4G7DnSg5AM5GWYUWBLjpbvRIr6K3T55ZfjhRdewOuvvw5JkvDTn/4UNTU1eOWVV/C1r30t6gCmTZuGrVu34tNPP8W//du/4eabb8auXbuG/dh7770XDocj8lZfXx/186lFWXY6bGYDfMEQDrT1Kh1OSjkg6lNSoJBW4ITaY4kTggyzASEZuPO5LejoVV83iBqIlt7TJ+ehJCu5E92zpuYjL8OMDpcPH+xpUzocAIM3JrM+ZThjSiUvuugirF+/Hr29vXC73fjoo49w4YUXjikAk8mEKVOmYOHChVixYgXmzp2LRx55ZNiPNZvNkQ4h8ZasdDqJg98UkgrLCI/GnT9DOT1+vNm/VO/PNy/ElIIMtPZ48b1/bGMdz1FCITmSqCRrEe1gBr0OV80vAQC8uEkdL3Z3NffXp/DaZ1hRJypffPEFPvvss2Pe/9lnn2Hjxo3jDkiWZXi9qfGqRmTPHKWfWHXt4ROsVCikFcQslVqe3gEYWKo3tSADp1Tk4E83VMNs0GHD3jb874YDSoenKl8c7ER9Zx8yzIbIzpxkJ0bqv7e7FV0u5VdLRFqTWUg7rKgTlWXLlg175dLY2Ihly5ZF9Vg/+tGP8OGHH+LgwYPYsWMHfvzjH+ODDz7AjTfeGG1YSSnS+cNEJaFSadibwBOVoY4uDJ1WZMMDV8wEADz01l58cbBTyfBURSzyu3R2sWYGkU0vsmNWqR3+oIy125oUjSUUkiMvVpmoDC/qRGXXrl2orq4+5v3z588fsbZkJEeOHMFNN92EadOm4fzzz8dnn32GN954Y0y1LslILJ7a1eSELPO4ORF8gRDqu/oApMYMFUGM0a/v6oMvEFI4GmXVtbuwcZileteeXI4r55UgGJJx53NbVPFKW2luXwCv72gGMHAKoRViBYDSG5UPdbrh9gVhNuhS6sVTNKJOVMxmM44cOXLM+5ubm2EwRNdE9MQTT+DgwYPwer1obW3FO++8kzJJCgBMLcyAXiehy+3HEWdqXHcprb7LjWBIhtWkR4HNrHQ4CVNoN8Ni0iMYklHf5VY6HEWJXTVnTs1H4aClepIk4RdXzUZlnhXNDg/+4/9Yr/Lmzha4fEFMyLHg5EnZSocTU1fMLYFBJ2FHowN7WnoUi0OcpkwvssGggg4kNYr6s/K1r30t0n0jdHd340c/+lFKJRmxkGbUY3L/q3pRTEXxFRmdn29N2lkQYyFJ0sD1TwpPqA0v1QvPzxiuMDTDbMCjN1TDZNDhvd2t+PNHtYkOUVXEacOS6uSdnTKS3AwzzpveP1NFwUWFopmChbQjizpReeihh1BfX4+JEyfi3HPPxbnnnouKigq0tLTgoYceikeMmlbFzp+ESsVCWoF1KsCntR1o7O477lK9qhI7fnpZeJbTb97Yg82HuxIZomo0dvfhkwMdAICrq5N7dspIxHXWmi2NCASVuRIdGJ3PRGUkUScqpaWl2L59O37zm9+gqqoKCxYswCOPPIIdO3agvLw8HjFqGgtqEysVC2mFgeWEqdv58+Iol+rdeMoEXDqnGIGQjO8+uwUOd+ptOV+zuQGyDHy1MgflORalw4mLc6cVIMdqQluPFx/ua1ckhsiJChOVEUU9mRYArFYr/uVf/iXWsaQkkUWL9jSKL3H1k0rD3oSBFuXUPFEZvFRPFFKORJIk/Orq2fiy0YFDHW7854vb8NhNCzR3/TESWZYji/tO9LlKZiaDDlfMLcHTnxzEi5sbcG7/VVCidLp8aHGGpyNPZ6IyolElKmvXrsUll1wCo9GItWvXHvdjr7jiipgElipEonKww4VebwAZ5jHljjRKdSm0NfloovMnVa9+1vUv1avIs6J6QtYJP96WZsSj11djycpP8PauI3jq44O49YyK+AeqApsPd6Gu3QWLSY9Fs4uVDieuli4ow9OfHMTbu47A4fYj02JM2HOLQtqJuRb+7D+OUX1mFi9ejJaWFhQUFGDx4sUjfpwkSQgGg7GKLSXkZZhRaDfjiNOLPS1OLJiYo3RImtXrDaC1J9xdNSkFT1TEf3Nrjzclk2Jx7bM0iqV6s8sy8aNF0/Ffr+zCinU1WDAxG3OTdHNwNF7sX9h38awiWDX+fTKzxI7pRTbsbunBK9ub8I2vTkzYc/PaZ3RGVaMSCoVQUFAQ+f8jvTFJGZtIQS2vf+JKdLvkZZhhT0vcqya1yEw3Ii/DBAA4mGKnKuNZqnfzaZNw8cwi+IMy7nhuMxx92q5X8fiDeHV7eAjaUg1f+wiSJEWutxLd/cNBb6PDpm0V4M6fxBBFpKlYnyKk6nLC8SzVkyQJv146B2XZ6ajv7MMPV23X9IDGt3YdQY8ngNKs9JTZJn3l/BLodRK2HO5O6JJY0UTB1uTjiypRCYVCePLJJ3HZZZdh1qxZmD17Nq644go888wzmv6LG2/s/EmMVO74ESKJSgrt/Bm8VG/JgrG12WamG/HoDdUw6iWs+7IFf/v0UCxDVBWxXuDq6lLodKlRPFxgS8PZJ+UDGPjvjzePP4j9reG/h2xNPr5RJyqyLOOKK67Ad77zHTQ2NmL27NmYOXMmDh06hFtuuQVXXXVVPOPUNHHst6fFiWCKT8KMp0iikoKFtEJFChbUxmqp3rzyLPzg4ukAgJ+/WoMvG7U3pPGI04MP97UB0Ha3z3DEAMDVmxsT8nN4f2svAiEZWRYjijPTTvwHUtioE5Wnn34aGzZswLvvvostW7bgueeew/PPP49t27bhnXfewXvvvYdnnnkmnrFq1sRcK9KNenj8oZT6ByTRIh0/KXyiIrqdUun7TJymLJpdBItpfIWh3z6jAhfMKIQvGMIdz25Gj0db9SprtjQiJAMLJ2anXMH5+TMKkJluRIvTg08OxH+myq5B9Smp0vY+VqNOVJ577jn86Ec/wrnnnnvM75133nn44Q9/iL///e8xDS5V6HUSphfbAPD6J15kWY4U06Zia7JQOWiMfipc14aX6o1udspoSJKE310zByWZaTjY4caP1nypmc+jLMtDOqNSjdmgxxVzSwAk5vpH1CTy2ufERp2obN++HRdffPGIv3/JJZdg27ZtMQkqFXGUfny19XrR4w1AJ0GzUzZHY0KuBZIE9HgDaO/V/nbgN3e2oNcb6F+qF5vW/yyLCX+8YT70OgmvbGvCc5/Xx+Rxlba9wYH9rb0wG3RYNEfbs1NGIkbqv7GzJe6nZbvY8TNqo05UOjs7UVg4/G4MACgsLERXV2ruxIgFUVBbwxOVuBCnKWXZFpgNI49O1zqzQY+y7HDXSypc/6zqnwcS68LQBRNzcM9F0wAAD7yyUxN/b8UV2UUzi1KyfR8A5pZlYnK+FR5/CK/vaI7b88iyPNCazI6fExp1ohIMBmEwjHy/q9frEQgEYhJUKoq0KGvgB54aseNngCio1XrnT1N3Hz7urzWIR2Hov5xZiXOm5cMbCGHZs5vh8ibvzz9vIIiXt/bPTknBax9BkiQsXRDeWfdiHK9/Grr60OMJwKTXYXJ+6i1IjdaoK8tkWcYtt9wCs9k87O97vd6YBZWKphfZIElAW48XrT0eFNhYBR5LTFQGVOZZsWFvm+ZPVNZsaYQsA6dUxGepnk4n4fdfn4dFj3yI2jYXfvLSl/j91+cmZWHkezWtcPT5UWRPw+lT8pQOR1FXzS/Fb9/cjS8OduFguysuRcXiBemUggyYDBxndiKj/gzdfPPNKCgoQGZm5rBvBQUF+OY3vxnPWDXNYjJE/hHlgsLYEwPOJqdwIa0giom1PPQtUYWhOVYT/nB9uF5lzZZG/F+CZnDEmvhcXVVdCn2KzE4ZSVFmGs6YGp6psjpOk2ojo/N57TMqoz5Reeqpp+IZByF8/VPb5kJNszMyfIhiY+BEhcesIiHW8onK5sPdqGt3Id2oxyVxXqr3lYocLP/aSfjtm3vw05e/xLzyLJxUaIvrc8ZSW48XH+xNzdkpI1lSXYoNe9uwanMj7r7gpJgPvuPo/OjwzElF2PkTH4FgCIc6OOxNEInKoQ6XZgcMihOCS2YVJWT54r+dPRlnTs2Dxx/Csr9vhtuXPPUqL28NDzibV56FKQVM5IFwQbHNbEBjdx8+q+uM+eOLqx+2Jo8OExUV4Sj9+Gjs7oM/KMNs0KHYztqfksx0mAw6+IMyGrrcSocTc0OW6iWoMFTUq+TbzNjX2ov7X96ZkOcdr8FXZEtSuIj2aGlGPS6bGz6Ji3VRraPPj4auPgA8URktJioqMrP/m7a2rRcePzdRx0rtoELaVNldcjw6nYSKXO3Wqbyt0FK9fJsZj1w3DzoJ+L9NDXGrb4ilnU1O7G7pgUmvw+UpOjtlJOIabN2XzTHt6Nrd/0K0NCsdmZbUbAOPFhMVFcm3mZFrNSEkA3taWFAbK2KGCjt+BlQMmlCrNS8quFTvtMl5uPP8qQCAn7z0ZWTpnFqJ2SlfqypElsWkcDTqsmBiNiblWuD2BbHuy5aYPS43JkePiYqKSJLE6584iOz4YX1KhFZ3/gxeqne1QoWh3z1vKk6bnAu3L4g7nt2s2tNRXyAUmZ0y1q3SWiZJUuRUJZYj9Tk6P3pMVFSGBbWxV9seflXLjp8BWu38GbxUT6kTNL1OwsPXzUNehgm7W3rwwCu7FInjRD7Y04pOlw95GWacNZVdhsO5ekEZJAn4Z20H6jtjU8/F0fnRY6KiMiLL1sJIbrXg1c+xtHiiIsty5JWv0oWhBbY0PHztfEgS8Nznh7F2W5Oi8QxHXPtcNb8EBj3/KRhOaVY6Tu2vc1qzpXHcj+cPhrDvSPiF00xe/YwavztVZvDOn5BGW0cTqc8XRJPDA2BgczANnC41dvehz6fOq4lobW9wYF//Ur1LVVAYesbUPNxx7hQAwL2rtqsqKex0+fDe7lYAyid1aic6x1Ztbhj3puwDbb3wBUOwmQ2RnVt0YkxUVKYyzwqTQQeXL4jDMTpqTGUH++enZFmMyLayWFDIthiRmR7uOBCfo2SnxqV6d50/FV+ZlAOXL4hlf1dPvcrarY3wB2XMKrVjehFf2R/PxbOKYDXpcajDjY2Hxrd4d3B9SjKuWlAKExWVMeh1mF4UnmrJgtrx446f4UmSpKk6FW8gGLleUdMJgUGvwx+un48cqwm7mp34xWs1SocEAHixP6lbykm0J2QxGbCof7rxeItquTF5bJioqNCMItapxEqk44eFtMfQUp3KezWt6Hb7UWg34wyVLdUrykzD778+FwDw108P4fUdzYrGs6elB182OmHUS7hiHrt9RkMkv69ubx7XVSkLaceGiYoKRVqU2fkzbgfawoVrbE0+lqjZqdXALJWBwtAyVS7VO2daAW4/ezIA4AcvbsfhDuWudcXn6txpBcjhdeiofGVSDsqy09HrDeCtXWObqSLLMluTx4iJigpxlkrs8OpnZKKgtq5d3UPJTqStx4v394RnpyxV8TyQ/7jwJCyYmI0ebwB3PLcZ3kDi61UCwRBWbw53ryRqvYAW6HQDM1XGOlK/xelBl9sPvU7C1EKe8EaDiYoKiRqVZocHXS6fwtEkNyYqIxOfk2Qfoy+W6s0tz8KUAvVuLTbqdfjj9fORZTFie4MDv1q3O+ExfLivHe29XuRYTThnWkHCnz+ZiUTlo/3taHb0Rf3nxVX+lPwMpBn1MY1N65ioqJAtzYgJORYArFMZjy6XD91uPwBgUi4TlaNNygt/j3W7/UmdEItXuMlwQlCSlY6HrgnXqzz18UG8uTN2o9lHQ3yurpxXApOBP/6jMSHXgq9U5ECWxzZTZeDaR73JtFrxO1WlIhNqmaiMmTgpKM1KR7qJr2COZjEZUJIZ3iadrKcqO5scSbdU7/wZhbjtzAoAwD3/ty1mE09PxOH24+1dRwAMnA5QdJYOuv6JdqYKd/yMHRMVlWJB7fjx2ufEKpK882fVpvAr2wuqCpJqqd49F03H3PIsOD0BfPe5LfAFQnF/zrXbm+ALhjC9yMapqGO0aE4x0o161La5sLW+O6o/W9McXjRbVZwZh8i0jYmKSs3gicq41baJHT9MVEYyMEsl+Qpq/cEQXt6anIWhJoMOj14/H/Y0A7bWd+N3b+2J+3OuGnRFxmFjY5NhNuDiWUUAoiuq7fUGIoMVefUTPSYqKiVOVPa39irSHaAFPFE5sYHOn+Q7UflgTxs6knipXnmOBb/tr1d5bEMt3tt9JG7Ptb+1F1vru6HXSbiSs1PGRVybvbKtadSThve0OCHLQKHdjNwMczzD0yQmKipVkpmGzHQjAiE5ssSKohNJVDhDZUTJPEvlxU31AJJ7qd5FM4twy2mTAADL/7ENTd3Rd5OMhpidcs5J+ci38R/K8Th1ci5KMtPg9ATwTs3okktxhc9Bb2OTnH+7U4AkSZEjQnb+RC8UkgdNpWWiMpLBY/STaQmmlpbq3btoOmaXZqLb7cedz22BPxjbepVgSMaa/tkpyf65UgO9TsJV1eFTqdGO1N8l6lNYGzQmTFRUTBRdsU4les1OD7yBEIx6CWXZFqXDUa2y7HQY9RK8gRCanR6lwxk1LS3VMxv0+NMN1bCZDdh4qAu/f3tvTB//4/3taHF6kJluxPkzODslFsT1z4Z97Wgdxd8b8TOcE2nHhomKirHzZ+zq+q8yJuZaVTlSXS0Mel1kZk9dEl3/rBInBBpps52Qa8GvlswBAKz84AA+2NMas8cW1z5XzC2B2cA2/ViozM9A9YQsBEMyXtp6/JkqwZCMPS28+hkPJioqNniWSrQ9+6mutp0dP6OVbKP097T0YEejAwadhCvmligdTsxcOqcY3/jqBADhepUWx/hPuJweP974MjxULtk6o9Ru6YJyAOEW+eP9fK5rd8HjD8Fi0mMiB0+OCRMVFZtSkAGjXkKPJ4DGOBXZaZUoDmV9yomJhY3JMvRNnBCcN71Acx0UP7m0ClXFdnS6fLjz+S0IjLNe5fXtzfAGQphSkIE5ZZzfEUuXzimGyaDDniPhbdQjEdc+04psPN0dIyYqKmYy6CK7S3j9Ex22Jo9eRRJ1/gSCocj4ci0WhqYZ9fjTjdWwmvT4vK4Tf3h337geTyR1S6o5OyXWMtONuLCqEMDA53k47PgZPyYqKsdR+mPDRGX0Bnf+qN2H+9rR1hNeqneuRpfqVeRZ8curZwMA/vj+fny0r31Mj3Ow3YUvDnZBJwFXzefslHgQ12kvb20ccbowR+ePHxMVlWOLcvS8gSAausL7UyrzuU79RMTVT0OXW/XDBV8cVBiq5aV6V84rxfVfKYcsA3e/sBWtPdHXq6zu/1ydMTUfRf07nSi2zpyajwKbGV1uf6Rd/mjiZzdPVMZOu3/TNSLS+cNEZdTqO90IyYDNbEBeRvLsf1FKfoYZGWYDQjIStiBvLBxuP97eGR6wlQqFofdfPhPTi2xo7/Xi7ue3IhjFnJtQSI50RqXC50opQ2aqDHP909rjQVuPF5IUrlGhsWGionIiC6/v7IOjz69wNMnhQNvARFrey5+YJElJUafySoot1Usz6vHoDdWwmPT45EAHHn1v/6j/7Kd1HWjs7oMtzRCpo6D4EBuV39/dio5e75DfE4sIK/KssJgMCY9NK5ioqFyWxYTSrHQAwG6eqowK61Oilwx1KmIJXCoVhk4pyMCDi2cBAB55dy/+eaBjVH9OfK4um1OCNCNnp8TT1EIb5pZlIhCS8fLWpiG/x2uf2GCikgRYpxIdMbiMicroqf1E5UDboKV687UzO2U0rq4uw9IFZQjJwF3Pb0H7Ua/aj+byBgbNTmERbSKIDrSjNyqLjh9OpB0fJipJgJ0/0eGJSvREQa1aT1TETpWzT8pHgS31CkN/duVMTC3IQGuPF997Yetx9zKt+7IFbl8QFXlWVE/ITmCUqevyOSUw6iXsanYOGSXBjp/YYKKSBFhQGx0xuGwyO35GrbJ/Oq0ah74FQzJWp3hhqMVkwJ9urEaaUYcP97Vj5foDI36s2Cq9pLo0Za7IlJZtNeGCGUNnqnj8QdS2hac9z+SJyrgwUUkC4thw75HemG9W1Rqnxx85Gp/EE5VRm5QX3vfT3uuF06Ouou1PDnCpHgCcVGjDz64I16v8/u29+OJg5zEfU9/pxqe1nZAk4CqN7EFKFmLv1MtbG+EPhrCnpQchGcjLMCHfpq0JyonGRCUJlGdbkGE2wBcIqbaGQC1EfUqBLdxyS6NjSzNGfpgeVNmpirj351I94JqFZbhqfimCIRnffXYLOl2+Ib8vTp5Om5wbKcKnxDh7Wj7yMkxo7/Vhw962IRuTebI1PkxUkoBOJ0UKanc1OxSORt1YnzJ2aiyodXr8eHNnuDBUiyPzoyVJEh5cPAuV+Va0OD34j38M1KvIsjxkZD4lllGvw5XzwsXLL25q4Oj8GGKikiQiBbXc+XNcosZCFIfS6IkFjmqqU3l9ezM8/vBSvblcqgcAsJoN+NMN1TAZdHh/Txse/7AWAPDFwS4c7nTDatLj4llFCkeZmkQN1bs1rfi0NtxKzkLa8VM0UVmxYgVOPvlk2Gw2FBQUYPHixdizZ4+SIamWqFMRA4RoeDxRGTs1zlLhUr3hzSi24/7LqwAAv31zDzYd6op0Ri2aXczhYgqZUWxHVbEdvmAI+1p7I++j8VE0UVm/fj2WLVuGTz/9FG+//TYCgQAuvPBCuFzq+UGpFoM7f2R59KO0U01de/iHg+hiodETe5HE51BpXKp3fDd8ZQIum1OMQEjGnc9twWs7mgHwikxpgz//JoMuclJJY6do2v3GG28M+fVTTz2FgoICbNq0CWeddZZCUanTSYU26HUSOl0+HHF6uWRsGLIsDwx749VP1CInKm0uyLKs+AkGl+odnyRJWHH1bHzZ6MDBjvCOpvKcdHxlUo7CkaW2K+eVYMXrNQiEZEwvssGgZ4XFeKnqM+hwhAtFc3KG/4vm9XrhdDqHvKWKNKM+kplzQu3wWnu8cPmC0OsklGdblA4n6UzIsUAnAS5fEG09x59+Gm+Dl+otqeZpykhsaUY8ekM1TP3/GF49vww6Ha/IlJSXYcY508Jt9CykjQ3VJCqyLGP58uU444wzMGvWrGE/ZsWKFcjMzIy8lZeXJzhKZXHw2/GJbpXy7HSYDKr51k4aJoMO5TnhBO+Awp0/kaV6ZgMumsnC0OOZVZqJP1w/D5fOKcYtp01SOhwCcO+i6bh0TjG+c2al0qFogmp+mt9xxx3Yvn07nnvuuRE/5t5774XD4Yi81dfXJzBC5bHz5/hYSDt+aimoXbUpfJpy2dxiLtUbhYtnFeNPN1Qj22pSOhRCeCr2n26oxpQC1srFgipKw7/73e9i7dq12LBhA8rKRi4EM5vNMJtTd8LfDO78OS5RBFrBQtoxq8iz4oM9bYoW1Lq8Aaz7MlwYmqoj84logKInKrIs44477sDq1avx3nvvoaKiQslwVE8kKgc7XHB5AwpHoz51nKEybgOdP8qdqHCpHhENpmiismzZMvztb3/Ds88+C5vNhpaWFrS0tKCvr0/JsFQr32ZGgc0MWQZ2t3CeytFEjQrbAcdODUPfxDyQq+dzqR4RKZyorFy5Eg6HA+eccw6Ki4sjby+88IKSYakaC2qH5w+GcLgz3KLJ1uSxEzUqhzvcCCiwALO+041/1nZAkoCree1DRFC4RoWDy6I3o9iOD/a0sUX5KA1dfQiEZKQb9Si0cebGWBXZ05Bm1MHjD6G+qy/hhclrtoSLaE+t5FI9IgpTTdcPjQ47f4Ynij8n5Vk5R2IcdDoJk3JF509iC2oHL9VjES0RCUxUkoy4+tnd4kQwxBMpIVKfwmufcZvcX1Cb6C3KGw914VAHl+oR0VBMVJLMpFxr5Ghe6VkXahLp+GEh7bgpNUvlxY1cqkdEx2KikmT0OgnTi8QmZV7/COLVP4e9jZ8SiUqfL8ilekQ0LCYqSYidP8fiVNrYEV1TiUxU3tzZgl5vAGXZXKpHREMxUUlCLKgdyuUNoMXpAcBEJRbE9VmzwwO3LzGDBUUR7ZJqLtUjoqGYqCQhMaGWVz9hBzvCr/xzrCZkWbjrZLyyLCZkW4wAEnOq0uzow0f72wGEExUiosGYqCSh6UU2SBLQ2uNFW49X6XAUx0La2EvkKP3Vmxshy8BXKnIwIdcS9+cjouTCRCUJWc0GVPTPuuCpClDHQtqYixTUxrlFecjsFJ6mENEwmKgkKW5SHiD20nB0fuwkqvNnS303attcSDfqsWhOcVyfi4iSExOVJCU6f3iiMpCo8OondhK1nFAsILx4VhEyzJydQkTHYqKSpNj5EybLMurawqPeK/IyFI5GO8TpVG1bb9x2cnn8QbyyrQkAi2iJaGRMVJKUOFE50NYLjz+ocDTK6XT54PQEIEnARBZixsykXCskCXB6Auh0+eLyHO/UHIHTE0BJZhpOnZwbl+cgouTHRCVJFdjMyLGaEJKBvUd6lA5HMaKGojQrHWlGvcLRaEeaUY+SzPD24njVqYhrn6uqS6Hn7BQiGgETlSQlSRKvf8DR+fEkFjzGo06l1enB+r1tAHjtQ0THx0QliXGUPgtp4ymenT8vbW1ESAaqJ2RFZrYQEQ2HiUoSm1FsA5DaJyp17aKQlolKrMVrloosy3ix/9pn6YLymD42EWkPE5UkVlWcCQDY3dKDUCg+nRlqF1lGyFflMVcRaVHujenjftnoxN4jvTAZdLiUs1OI6ASYqCSxynwrTAYder0B1He5lQ4n4YIhGQc7wv/dvPqJvcr+du+DHW4EY5gIv7ipHgBw0cwiZKYbY/a4RKRNTFSSmFGvw7TC1L3+aerugy8QgsmgQ0lWutLhaE5pdjpMeh18gRCauvti8pi+QAhrI7NTSmPymESkbUxUkpyoU0nFCbWikHZSroXtrXGg10mR2TSxKqh9b3crutx+FNjMOHNqfkwek4i0jYlKkqtK4Z0/AxNpee0TL7Hu/HmRs1OIKEpMVJJcVUm4oDYVr34ihbQcnR83YpR+LBKV9l4vPtjTCoCbkolo9JioJLnp/Vc/TQ4Put3xGXWuVpyhEn/ic3ugbfydPy9vbUIgJGNuWSam9tdWERGdCBOVJGdPM6I8J1xImmrXPwOtyUxU4kWcVsXiREWMzF+ygKcpRDR6TFQ0IBVH6Xv8QTT2d6LwRCV+xBj9xu6+cS2/3NXkxK5mJ0x6HS6fUxKr8IgoBTBR0QAx+C2VTlQOdbghy4A9zYAcq0npcDQr12qCLc0AWQYOd459Vs+qzeHTlPNnFCCbXy8iigITFQ0YaFFOnS3KkdH5+RmQJHaPxIskSZETq9oxjtL3B0N4aUsjAC4gJKLoMVHRALGccH9rD3yBkMLRJAYLaRNnvKP01+9pQ4fLh7wME86extkpRBQdJioaUJqVDnuaAf6gjH2tqXGqIhblcYZK/EUKasd4oiKufa6cVwqjnj9yiCg6/KmhAZIkYUaKFdSKLpRKdvzEXeU4Zql0uXx4p+YIAGApu32IaAyYqGiEuP5JlTqVgWFvTFTibTzTaV/Z3gR/UEZVsT2STBMRRYOJikYMjNJ3KBxJ/DncfnS4wsPtJuUyUYk3kah0uHxwuP1R/VkxMp+nKUQ0VkxUNEKcqOxqckKWZYWjiS9R1FlkT4PVbFA4Gu2zmg0otJsBAHUdoz9V2XukB9sbHDDoJFw5j7NTiGhsmKhoxJSCDBh0EpyeAJocHqXDiSte+yRepPMnilH6YhLtOdMKkJthjktcRKR9TFQ0wmzQY0pBuDtD6wW1HJ2feNGO0g8EQ1jTPzuF1z5ENB5MVDRk8PWPlnGGSuJNzhezVEaXqHy0vx2tPV5kW4w4b3pBPEMjIo1joqIhqVJQK+Z5sDU5cSKdP6OcpSKKaK+cVwqTgT9miGjs+BNEQ0SiouUW5VBIHlSjkqFwNKljcIvyiYq1HX1+vLUrPDuFI/OJaLyYqGiImFNxuNMNpye6NtJkcaTHgz5/EAadhLLsdKXDSRnlORbodRL6/EEccXqP+7Gvbm+CLxDCtEIbZpVydgoRjQ8TFQ3JtppQkpkGANit0VMVcfUwIcfCcewJZNTrMCHHAuDEnT+i22fJglIujCSiceNPeo2ZEbn+0WZBbS1bkxUzsJxw5DqVA2292Hy4GzoJWDyvNFGhEZGGMVHRGK13/nDHj3IqRzFKf3X/AsKzT8pHgT0tIXERkbYxUdGYgc4fbSYq4tqBhbSJV3GC5YTBkIzVm8OzU5ZwdgoRxQgTFY0RVz97jvQgEAwpHE3scSqtck60nPCfBzrQ7PDAnmbABTMKExkaEWkYExWNmZBjgdWkhy8QGvVwrmThC4RQ39UHgFc/SqjsP8U63OmGf5gkeFX/tc/lc0uQZtQnNDYi0i4mKhqj00mRUxWt1anUd7kRDMmwmPQosHF3TKIV2s1IN+oRDMk43Oke8ns9Hj/WfdkMgCPziSi2mKhoUKSgVmN1KqI1uSLPyrZXBUiSNOKE2nU7WuDxh1CZb8W88iwFoiMirWKiokFabVEe6PhhIa1SKkcoqBUj85cuKGMSSUQxxURFg6oGXf2caNx5MqltFx0/rE9RSuUws1QOdbjw+cFOSBJw1XzOTiGi2GKiokHTimzQSUCHy4fWnuOPO08mtW3cmqy0gRblgem0q/pbks+YkofiTK41IKLYYqKiQWlGfeR6REsFtWxNVp6YXyO+FqGQHBnyxiJaIooHJioapbXBb73eQOR0aBITFcVU5IY/90ecXvR6A/j8YCcauvqQYTbgwqoihaMjIi1ioqJRWuv8Odj/Cj4vw4TMdKPC0aSuTIsRuVYTgPDXRBTRXjanGOkmzk4hothjoqJR4kSlRiNXP6J4s5Kj8xUnOn++bHTg9R3h2SkcmU9E8cJERaNEi3JdhwtuX0DhaMZvYMcPr32UJr4Gj22ohdsXxMRcCxZOzFY4KiLSKkUTlQ0bNuDyyy9HSUkJJEnCSy+9pGQ4mpJvMyPfZoYsA7tbepQOZ9wihbQcna84UVArTrmWVHN2ChHFj6KJisvlwty5c/Hoo48qGYZmVWlolD47ftTj6K8BZ6cQUTwZlHzySy65BJdccomSIWjajGI71u9tw+ZDXThnWr7S4YxLHWeoqMbghZCnVuaiPMeiYDREpHWKJirR8nq98HoHBpg5ncl/UhBPovNn9ZZGrN7SqHA046eTgAm5/EdRaRNyLJAkQJZZREtE8ZdUicqKFSvwwAMPKB1G0jhzSh4m51vR0NWndCgxceW8EpgNbIFVWppRjxtPmYD9rb1YNJuzU4goviRZJctgJEnCmjVrsHjx4hE/ZrgTlfLycjgcDtjt9gRESUREROPldDqRmZk5qn+/k+pExWw2w2w2Kx0GERERJQjnqBAREZFqKXqi0tvbi/3790d+XVdXh61btyInJwcTJkxQMDIiIiJSA0UTlY0bN+Lcc8+N/Hr58uUAgJtvvhlPP/20QlERERGRWiiaqJxzzjlQSS0vERERqRBrVIiIiEi1mKgQERGRajFRISIiItViokJERESqxUSFiIiIVIuJChEREakWExUiIiJSLSYqREREpFpMVIiIiEi1kmp78tHEVFun06lwJERERDRa4t/t0UynT+pEpaenBwBQXl6ucCREREQUrZ6eHmRmZh73YyQ5iZfthEIhNDU1wWazQZKkmD620+lEeXk56uvrYbfbY/rYFD1+PdSFXw914ddDffg1OT5ZltHT04OSkhLodMevQknqExWdToeysrK4Pofdbuc3mYrw66Eu/HqoC78e6sOvychOdJIisJiWiIiIVIuJChEREakWE5URmM1m3H///TCbzUqHQuDXQ2349VAXfj3Uh1+T2EnqYloiIiLSNp6oEBERkWoxUSEiIiLVYqJCREREqsVEhYiIiFSLicow/ud//gcVFRVIS0vDggUL8OGHHyodUkpasWIFTj75ZNhsNhQUFGDx4sXYs2eP0mFRvxUrVkCSJNx9991Kh5LSGhsb8Y1vfAO5ubmwWCyYN28eNm3apHRYKSkQCOAnP/kJKioqkJ6ejsrKSvzsZz9DKBRSOrSkxkTlKC+88ALuvvtu/PjHP8aWLVtw5pln4pJLLsHhw4eVDi3lrF+/HsuWLcOnn36Kt99+G4FAABdeeCFcLpfSoaW8L774Ao899hjmzJmjdCgpraurC6effjqMRiPWrVuHXbt24aGHHkJWVpbSoaWkX//61/jf//1fPProo6ipqcFvfvMb/Pa3v8Uf//hHpUNLamxPPsopp5yC6upqrFy5MvK+GTNmYPHixVixYoWCkVFbWxsKCgqwfv16nHXWWUqHk7J6e3tRXV2N//mf/8GDDz6IefPm4eGHH1Y6rJT0wx/+EB9//DFPfVXisssuQ2FhIZ544onI+5YsWQKLxYK//vWvCkaW3HiiMojP58OmTZtw4YUXDnn/hRdeiE8++UShqEhwOBwAgJycHIUjSW3Lli3DpZdeigsuuEDpUFLe2rVrsXDhQlxzzTUoKCjA/Pnz8fjjjysdVso644wz8O6772Lv3r0AgG3btuGjjz7CokWLFI4suSX1UsJYa29vRzAYRGFh4ZD3FxYWoqWlRaGoCAhv2ly+fDnOOOMMzJo1S+lwUtbzzz+PzZs344svvlA6FAJQW1uLlStXYvny5fjRj36Ezz//HHfeeSfMZjO++c1vKh1eyvnBD34Ah8OB6dOnQ6/XIxgM4he/+AWuv/56pUNLakxUhiFJ0pBfy7J8zPsose644w5s374dH330kdKhpKz6+nrcddddeOutt5CWlqZ0OAQgFAph4cKF+OUvfwkAmD9/Pnbu3ImVK1cyUVHACy+8gL/97W949tlnMXPmTGzduhV33303SkpKcPPNNysdXtJiojJIXl4e9Hr9Macnra2tx5yyUOJ897vfxdq1a7FhwwaUlZUpHU7K2rRpE1pbW7FgwYLI+4LBIDZs2IBHH30UXq8Xer1ewQhTT3FxMaqqqoa8b8aMGVi1apVCEaW2e+65Bz/84Q9x3XXXAQBmz56NQ4cOYcWKFUxUxoE1KoOYTCYsWLAAb7/99pD3v/322zjttNMUiip1ybKMO+64A6tXr8Z7772HiooKpUNKaeeffz527NiBrVu3Rt4WLlyIG2+8EVu3bmWSooDTTz/9mJb9vXv3YuLEiQpFlNrcbjd0uqH/rOr1erYnjxNPVI6yfPly3HTTTVi4cCFOPfVUPPbYYzh8+DBuv/12pUNLOcuWLcOzzz6Ll19+GTabLXLSlZmZifT0dIWjSz02m+2Y+iCr1Yrc3FzWDSnke9/7Hk477TT88pe/xNe//nV8/vnneOyxx/DYY48pHVpKuvzyy/GLX/wCEyZMwMyZM7Flyxb8/ve/x6233qp0aMlNpmP86U9/kidOnCibTCa5urpaXr9+vdIhpSQAw7499dRTSodG/c4++2z5rrvuUjqMlPbKK6/Is2bNks1mszx9+nT5scceUzqklOV0OuW77rpLnjBhgpyWliZXVlbKP/7xj2Wv16t0aEmNc1SIiIhItVijQkRERKrFRIWIiIhUi4kKERERqRYTFSIiIlItJipERESkWkxUiIiISLWYqBAREZFqMVEhIiIi1WKiQkRERKrFRIWIjnHLLbdAkqRj3vbv3690aESUYriUkIiGdfHFF+Opp54a8r78/Pwhv/b5fDCZTIkMi4hSDE9UiGhYZrMZRUVFQ97OP/983HHHHVi+fDny8vLwta99DQCwa9cuLFq0CBkZGSgsLMRNN92E9vb2yGO5XC5885vfREZGBoqLi/HQQw/hnHPOwd133x35GEmS8NJLLw2JISsrC08//XTk142Njbj22muRnZ2N3NxcXHnllTh48GDk92+55RYsXrwYv/vd71BcXIzc3FwsW7YMfr8/8jFerxff//73UV5eDrPZjKlTp+KJJ56ALMuYMmUKfve73w2J4csvv4ROp8OBAwfG/0kloqgxUSGiqPzlL3+BwWDAxx9/jP/3//4fmpubcfbZZ2PevHnYuHEj3njjDRw5cgRf//rXI3/mnnvuwfvvv481a9bgrbfewgcffIBNmzZF9bxutxvnnnsuMjIysGHDBnz00UfIyMjAxRdfDJ/PF/m4999/HwcOHMD777+Pv/zlL3j66aeHJDvf/OY38fzzz+MPf/gDampq8L//+7/IyMiAJEm49dZbjzlFevLJJ3HmmWdi8uTJY/uEEdH4KLy9mYhU6Oabb5b1er1stVojb0uXLpXPPvtsed68eUM+9r777pMvvPDCIe+rr6+XAch79uyRe3p6ZJPJJD///POR3+/o6JDT09Plu+66K/I+APKaNWuGPE5mZqb81FNPybIsy0888YQ8bdo0ORQKRX7f6/XK6enp8ptvvhmJe+LEiXIgEIh8zDXXXCNfe+21sizL8p49e2QA8ttvvz3sf3dTU5Os1+vlzz77TJZlWfb5fHJ+fr789NNPj+KzRkTxwBoVIhrWueeei5UrV0Z+bbVacf3112PhwoVDPm7Tpk14//33kZGRccxjHDhwAH19ffD5fDj11FMj78/JycG0adOiimfTpk3Yv38/bDbbkPd7PJ4h1zIzZ86EXq+P/Lq4uBg7duwAAGzduhV6vR5nn332sM9RXFyMSy+9FE8++SS+8pWv4NVXX4XH48E111wTVaxEFDtMVIhoWFarFVOmTBn2/YOFQiFcfvnl+PWvf33MxxYXF2Pfvn2jej5JkiDL8pD3Da4tCYVCWLBgAf7+978f82cHF/kajcZjHjcUCgEA0tPTTxjHd77zHdx000347//+bzz11FO49tprYbFYRvXfQESxx0SFiMaluroaq1atwqRJk2AwHPsjZcqUKTAajfj0008xYcIEAEBXVxf27t075GQjPz8fzc3NkV/v27cPbrd7yPO88MILKCgogN1uH1Oss2fPRigUwvr163HBBRcM+zGLFi2C1WrFypUrsW7dOmzYsGFMz0VEscFiWiIal2XLlqGzsxPXX389Pv/8c9TW1uKtt97CrbfeimAwiIyMDHz729/GPffcg3fffRdffvklbrnlFuh0Q3/8nHfeeXj00UexefNmbNy4EbfffvuQ05Ebb7wReXl5uPLKK/Hhhx+irq4O69evx1133YWGhoZRxTpp0iTcfPPNuPXWW/HSSy+hrq4OH3zwAf7xj39EPkav1+OWW27BvffeiylTpgy5siKixGOiQkTjUlJSgo8//hjBYBAXXXQRZs2ahbvuuguZmZmRZOS3v/0tzjrrLFxxxRW44IILcMYZZ2DBggVDHuehhx5CeXk5zjrrLNxwww34z//8zyFXLhaLBRs2bMCECRNw9dVXY8aMGbj11lvR19cX1QnLypUrsXTpUvz7v/87pk+fjttuuw0ul2vIx3z729+Gz+fDrbfeOo7PDBHFgiQffSlMRJQA55xzDubNm4eHH35Y6VCO8fHHH+Occ85BQ0MDCgsLlQ6HKKWxRoWIqJ/X60V9fT3uu+8+fP3rX2eSQqQCvPohIur33HPPYdq0aXA4HPjNb36jdDhEBF79EBERkYrxRIWIiIhUi4kKERERqRYTFSIiIlItJipERESkWkxUiIiISLWYqBAREZFqMVEhIiIi1WKiQkRERKr1/wFeUdILTiLCJgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "plt.plot(df[\"Result\"].values)\n",
+    "plt.xlabel(\"Frequency\")\n",
+    "plt.ylabel(\"Dice roll number\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -74,9 +326,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "\"\"\"The difference between the two plots relies on the sorted func.\n",
+    "The first plot illustrates the ordered sequence of dice numbers, while the second plot shows the actual individual \n",
+    "observations in their original random way.\"\"\""
    ]
   },
   {
@@ -91,11 +343,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 77,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "def manual_mean():\n",
+    "  total_sum = 0\n",
+    "  total_count = 0\n",
+    "  for value in df[\"Result\"]:\n",
+    "    total_sum += value\n",
+    "    total_count += 1\n",
+    "\n",
+    "  mean = total_sum/total_count\n",
+    "  return mean\n",
+    "\n",
+    "round(manual_mean())"
    ]
   },
   {
@@ -107,11 +381,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 80,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "freq = {}\n",
+    "for value in df[\"Result\"]:\n",
+    "    if value in freq:\n",
+    "        freq[value] += 1\n",
+    "    else:\n",
+    "        freq[value] = 1\n",
+    "#print(\"Frequency is: \", freq)\n",
+    "\n",
+    "sum_values = 0\n",
+    "total_count = 0\n",
+    "\n",
+    "for value, count in freq.items():\n",
+    "  sum_values += value * count\n",
+    "  total_count += count\n",
+    "mean = sum_values/total_count\n",
+    "round(mean)"
    ]
   },
   {
@@ -124,11 +425,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 84,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "def manual_median(sorted_df):\n",
+    "  total_count = 0\n",
+    "  for value in sorted_df:\n",
+    "    total_count += 1\n",
+    "    if total_count % 2 == 0:\n",
+    "      median = (sorted_df[total_count // 2 - 1] + sorted_df[total_count // 2]) / 2\n",
+    "    else:\n",
+    "      median = (sorted_df[total_count // 2])\n",
+    "\n",
+    "  return median\n",
+    "\n",
+    "print((manual_median(sorted_df[\"Result\"])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Manual median is:  [7.5 2.5]\n",
+      "Method median is: 4.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "sorted_df_array = sorted_df.values\n",
+    "def manual_median(sorted_df_array):\n",
+    "  median = (sorted_df_array[4] + sorted_df_array[5])/2\n",
+    "  return median\n",
+    "manual_median(sorted_df_array)\n",
+    "\n",
+    "print(\"Manual median is: \", (sorted_df_array[4] + sorted_df_array[5])/2)\n",
+    "print(\"Method median is:\", np.median(sorted_df))"
    ]
   },
   {
@@ -140,11 +486,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 90,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([7.5, 2.5]), array([7.5, 2.5]))"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "def manual_quartiles(sorted_df_array):\n",
+    "    q2 = manual_median(sorted_df_array)\n",
+    "    for_q1 = sorted_df_array[:6]\n",
+    "    for_q3 = sorted_df_array[:6:]\n",
+    "    q1 = manual_median(for_q1)\n",
+    "    q3 = manual_median(for_q3)\n",
+    "    return q1, q3\n",
+    "\n",
+    "manual_quartiles(sorted_df_array)"
    ]
   },
   {
@@ -158,11 +523,120 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 91,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>roll</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0  roll  value\n",
+       "0           0     0      1\n",
+       "1           1     1      2\n",
+       "2           2     2      6\n",
+       "3           3     3      1\n",
+       "4           4     4      6"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "data = pd.read_csv(\"roll_the_dice_hundred.csv\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x12d3050fa50>"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data = data.sort_values(by = \"value\")\n",
+    "data_plot = plt.scatter( x = sorted_data[\"roll\"], y = sorted_data[\"value\"])\n",
+    "data_plot"
    ]
   },
   {
@@ -171,9 +645,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "\"\"\"\"\"\""
    ]
   },
   {
@@ -185,11 +657,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 93,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "def manual_mean():\n",
+    "  total_sum = 0\n",
+    "  total_count = 0\n",
+    "  for value in data[\"value\"]:\n",
+    "    total_sum += value\n",
+    "    total_count += 1\n",
+    "\n",
+    "  mean = total_sum/total_count\n",
+    "  return mean\n",
+    "\n",
+    "round(manual_mean())"
    ]
   },
   {
@@ -201,11 +695,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 94,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Frequency is:  {1: 12, 2: 17, 6: 23, 5: 12, 4: 22, 3: 14}\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "freq = {}\n",
+    "for value in data[\"value\"]:\n",
+    "    if value in freq:\n",
+    "        freq[value] += 1\n",
+    "    else:\n",
+    "        freq[value] = 1\n",
+    "print(\"Frequency is: \", freq)"
    ]
   },
   {
@@ -217,11 +726,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 95,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "plt.hist(data[\"value\"])\n",
+    "plt.xlabel(\"value\")\n",
+    "plt.ylabel(\"roll\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -230,9 +754,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "\"\"\" Because the mean will be the most observed event,\n",
+    "it makes sense that the bar corresponding to 4 will be the one with > frequency.\"\"\""
    ]
   },
   {
@@ -244,22 +767,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 96,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "data_2 = pd.read_csv(\"roll_the_dice_thousand.csv\")\n",
+    "\n",
+    "plt.hist(data_2[\"value\"])\n",
+    "plt.xlabel(\"value\")\n",
+    "plt.ylabel(\"roll\")\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 97,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A think because now we have a larger dataset, the frequency distribution will behave more evenly and more consistently. Also, this provides more~\\nconfidence in the \"random\" of the dice, instead of looking like some bias in some numbers.'"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "\"\"\"Because now we have a larger dataset, the frequency distribution will behave more evenly and more consistently. Also, this provides more~\n",
+    "confidence in the \"random\" of the dice, instead of looking like some bias in some numbers.\"\"\""
    ]
   },
   {
@@ -274,11 +824,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 98,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "ages_pop = pd.read_csv(\"ages_population.csv\")\n",
+    "plt.hist(ages_pop[\"observation\"], bins = 30)\n",
+    "plt.xlabel(\"age\")\n",
+    "plt.ylabel(\"freq\")\n",
+    "plt.show()\n",
+    "# mean around 40 years old\n",
+    "# std around 14"
    ]
   },
   {
@@ -290,11 +858,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 99,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean is:  36.56\n",
+      "Std is:  12.816499625976762\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "print(\"Mean is: \", ages_pop[\"observation\"].mean())\n",
+    "print(\"Std is: \",ages_pop[\"observation\"].std())"
    ]
   },
   {
@@ -317,11 +896,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 101,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "ages_pop_2 = pd.read_csv(\"ages_population2.csv\")\n",
+    "plt.hist(ages_pop_2[\"observation\"], bins = 30)\n",
+    "plt.xlabel(\"age\")\n",
+    "plt.ylabel(\"freq\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -333,13 +928,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 102,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Very different, even according to the range of ages (suggesting less variability). Now we will have a lower mean age,\\n so the central tendency of this dataset is lower than the one before. '"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "\"\"\"Very different, even according to the range of ages (suggesting less variability). Now we will have a lower mean age,\n",
+    " so the central tendency of this dataset is lower than the one before. \"\"\""
    ]
   },
   {
@@ -351,22 +956,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 103,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean is:  27.155\n",
+      "Std is:  2.969813932689186\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "print(\"Mean is: \", ages_pop_2[\"observation\"].mean())\n",
+    "print(\"Std is: \",ages_pop_2[\"observation\"].std())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 105,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' The mean age of  27.155 indicates that the central tendency of the dataset is lower than that of the previous dataset.\\nAlso suggests a younger population compared to the first dataset.\\n\\nA really lower std of around 2.97 indicates that the ages in this dataset are more tightly clustered around the mean.\\nThe previous set had a bigger range -> where the values were more spread out.'"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "\n",
+    "\"\"\" The mean age of  27.155 indicates that the central tendency of the dataset is lower than that of the previous dataset.\n",
+    "Also suggests a younger population compared to the first dataset.\n",
+    "\n",
+    "A really lower std of around 2.97 indicates that the ages in this dataset are more tightly clustered around the mean.\n",
+    "The previous set had a bigger range -> where the values were more spread out.\"\"\"\n"
    ]
   },
   {
@@ -381,11 +1011,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 106,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "ages_pop_3 = pd.read_csv(\"ages_population3.csv\")\n",
+    "plt.hist(ages_pop_3[\"observation\"], bins = 30)\n",
+    "plt.xlabel(\"age\")\n",
+    "plt.ylabel(\"freq\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -397,11 +1043,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 107,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean is:  41.989\n",
+      "Std is:  16.144705959865934\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "print(\"Mean is: \", ages_pop_3[\"observation\"].mean())\n",
+    "print(\"Std is: \",ages_pop_3[\"observation\"].std())"
    ]
   },
   {
@@ -410,9 +1067,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "\"\"\" Now with a mean age of 41.989, this indicates indicates that the central tendency of this dataset is more similar with the one in Step 1.\n",
+    "And also sugests a similar population as in Step 1.\n",
+    "\n",
+    "With a std of  16.144, this surelly puts us in a population with the age range really similar to step 1, a dataset not so tightly clustered around the mean.\n",
+    "Now we have values more spread out.\"\"\""
    ]
   },
   {
@@ -424,11 +1083,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 108,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the first quartile is:  30.0\n",
+      "the second quartile is:  40.0\n",
+      "the third quartile is:  53.0\n",
+      "the fourth quartile is:  77.0\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "q1 = np.quantile(ages_pop_3, 0.25)\n",
+    "q2 = np.quantile(ages_pop_3, 0.50)\n",
+    "q3 = np.quantile(ages_pop_3, 0.75)\n",
+    "q4 = np.quantile(ages_pop_3, 1)\n",
+    "\n",
+    "print(\"the first quartile is: \", q1)\n",
+    "print(\"the second quartile is: \", q2)\n",
+    "print(\"the third quartile is: \", q3)\n",
+    "print(\"the fourth quartile is: \", q4)"
    ]
   },
   {
@@ -437,9 +1116,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "\"\"\"the median and mean are really similar, not equal, probably because of some outliers/skewness - lot of observations between q3 and q4\"\"\""
    ]
   },
   {
@@ -451,11 +1128,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 109,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The percentile 10 is:  22.0\n",
+      "The percentile 15 is:  25.0\n",
+      "The percentile 85 is:  64.0\n",
+      "The percentile 90 is:  67.0\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "p10 = np.quantile(ages_pop_3, 0.10)\n",
+    "p15 = np.quantile(ages_pop_3, 0.15)\n",
+    "p85 = np.quantile(ages_pop_3, 0.85)\n",
+    "p90 = np.quantile(ages_pop_3, 0.90)\n",
+    "\n",
+    "\n",
+    "print(\"The percentile 10 is: \", p10)\n",
+    "print(\"The percentile 15 is: \", p15)\n",
+    "print(\"The percentile 85 is: \", p85)\n",
+    "print(\"The percentile 90 is: \", p90)"
    ]
   },
   {
@@ -464,9 +1162,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "\"\"\"maybe if we had younger ages in p10 and p15, we could get a sample with a younger mean.\n",
+    "p10 is 22 years away from the mean, as for p90 is 27 years away, pushing the mean value a litlle up\"\"\""
    ]
   },
   {
@@ -500,9 +1197,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ironhack-3.7",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "ironhack-3.7"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -514,7 +1211,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/your-code/roll_the_dice_hundred.csv b/your-code/roll_the_dice_hundred.csv
new file mode 100644
index 0000000..50975a2
--- /dev/null
+++ b/your-code/roll_the_dice_hundred.csv
@@ -0,0 +1,101 @@
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diff --git a/your-code/roll_the_dice_thousand.csv b/your-code/roll_the_dice_thousand.csv
new file mode 100644
index 0000000..f820dbb
--- /dev/null
+++ b/your-code/roll_the_dice_thousand.csv
@@ -0,0 +1,1001 @@
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