Boston Data Project.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "edeb25e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary packages/libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "import statsmodels.api as sm\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8f68cf6",
   "metadata": {},
   "source": [
    "# Task 1 - Data Familiarization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bf8e10f",
   "metadata": {},
   "source": [
    "- **CRIM** - per capita crime rate by town\n",
    "- **ZN** - proportion of residential land zoned for lots over 25,000 sq.ft.\n",
    "- **INDUS** - proportion of non-retail business acres per town.\n",
    "- **CHAS** - Charles River dummy variable (1 if tract bounds river; 0 otherwise)\n",
    "- **NOX** - nitric oxides concentration (parts per 10 million)\n",
    "- **RM** - average number of rooms per dwelling\n",
    "- **AGE** - proportion of owner-occupied units built prior to 1940\n",
    "- **DIS** - weighted distances to five Boston employment centres\n",
    "- **RAD** - index of accessibility to radial highways\n",
    "- **TAX** - full-value property-tax rate per \\\\$10,000\n",
    "- **PTRATIO** - pupil-teacher ratio by town\n",
    "- **LSTAT** - % lower status of the population\n",
    "- **MEDV** - Median value of owner-occupied homes in $1000's"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d99a1c3",
   "metadata": {},
   "source": [
    "# Task 2 - Watson Studio"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5de4be37",
   "metadata": {},
   "source": [
    "##### Taks not needed to complete assignment; skipped"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a38b4a99",
   "metadata": {},
   "source": [
    "# Task 3 - Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f42a7ee3",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
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       "      <th>PTRATIO</th>\n",
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       "      <th>MEDV</th>\n",
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       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
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       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>69.1</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>21.0</td>\n",
       "      <td>9.67</td>\n",
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       "      <td>21.0</td>\n",
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       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1    0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3    0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
       "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
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       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO  LSTAT  MEDV  \n",
       "0       15.3   4.98  24.0  \n",
       "1       17.8   9.14  21.6  \n",
       "2       17.8   4.03  34.7  \n",
       "3       18.7   2.94  33.4  \n",
       "4       18.7   5.33  36.2  \n",
       "..       ...    ...   ...  \n",
       "501     21.0   9.67  22.4  \n",
       "502     21.0   9.08  20.6  \n",
       "503     21.0   5.64  23.9  \n",
       "504     21.0   6.48  22.0  \n",
       "505     21.0   7.88  11.9  \n",
       "\n",
       "[506 rows x 13 columns]"
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the data and reformat into a pandas dataframe\n",
    "boston_url = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ST0151EN-SkillsNetwork/labs/boston_housing.csv'\n",
    "boston_df = pd.read_csv(boston_url)\n",
    "boston_df.drop(columns=(boston_df.columns[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "240f1227",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
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       "      <th>0</th>\n",
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       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
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       "      <th>3</th>\n",
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       "      <td>6.998</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>5.33</td>\n",
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      "text/plain": [
       "   Unnamed: 0     CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  \\\n",
       "0           0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0   \n",
       "1           1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0   \n",
       "2           2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0   \n",
       "3           3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0   \n",
       "4           4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0   \n",
       "\n",
       "     TAX  PTRATIO  LSTAT  MEDV  \n",
       "0  296.0     15.3   4.98  24.0  \n",
       "1  242.0     17.8   9.14  21.6  \n",
       "2  242.0     17.8   4.03  34.7  \n",
       "3  222.0     18.7   2.94  33.4  \n",
       "4  222.0     18.7   5.33  36.2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "202b8230",
   "metadata": {},
   "source": [
    "# Task 4 - Statistics & Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0662bc7f",
   "metadata": {},
   "source": [
    "#### 1. Boxplot of \"Median value of owner-occupied homes\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2184941d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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//766deum8PBwp9ehd+/eki6M8EkXPsMlPUZZzJkzR6mpqcUugwcPdtqu8Dvj4iMdJenmm2/Wtdde6zSqJ0mNGjVS+/btHdfr1q2rhg0bql27dgoPD3e0F77mhe+h3NxcffTRR+rfv78CAwOdXoM+ffooNzdXX3zxheOxv/76a02aNEkbNmxQdnZ2mZ77pcTFxeno0aNKTk7WsmXLVKNGDd19992l3qak19PVbuCSGBejYnFxcfLy8nIanUlMTFTNmjXLPBpX1Vz6FwuVQuEP+ksvvaTc3FxdffXVuu2221xue/ToUZ08ebLEo3sKh4IL56CEhYU59fv4+KhevXqXrGn48OH66KOP9MQTT6hDhw4KDg6WZVnq06dPsd0Ikordp5+fnyS53LaouLg4xcfHKzExUe3bt9eyZct09uxZxy4mSZo2bZpeeeUVzZgxQ127dnXMARk/frxbj+GOo0ePOuacuNKiRYsSb3vixAkZY9SoUaNifYVf7EXnBbmjadOm2rdvn06fPq2aNWu63KZwjkdERIQkKTIyUldddZU2b96sIUOGaNu2bXrooYfUsmVLPfDAA/ruu++0efNmBQQEKCYmxum+AgMD5e/v79Tm5+en3Nxcx3V334OFir4m3bt3d/xgS9Lo0aO1dOlS1a9fX4GBgdq/f38pr8iF5xsYGKi6desqIyND+fn5Je6WKvTzzz9fcpuycvU+Kfy8lfZ/ffToUa1bt06+vr4u+y/+DJf2GO5q0aKFoqOji7UXDX+FNZf0Hi76h0ndunWLbVejRo1i7YXvk8L3UGZmps6fP6+XX35ZL7/8ssuaC1+DmTNnqmbNmnr77bf1+uuvy9vbW126dNGcOXNcPqeyioyMVPfu3bVkyRIdOHBAQ4cOVWBgoH799dcSb1PS61kWha/lxaGvsJakpCTNmzdPOTk5ev/99zV8+PArclRrZUaYsZExY8boySef1Ouvv65nn322xO0KJ9iWtCZB4Zu+MFxkZGSocePGjv7z589f8kc1KytL77//vp566ik9+uijjva8vDwdP37c7efkriZNmqhXr15KSkrSCy+8oMTERLVs2VJdunRxbPP2229r1KhRmj17ttNtf/nll2KHXRbl7+9fbAJr4W0vVr9+fVmWpU8//dQRxi7mqq1QYbg6cuRIsb7CuQL169cvtU5XevbsqY0bN2rdunUaOnRosX5jjNauXau6des6/ZVcOOclJSVFBQUFio2NVVBQkMLDw7Vp0yZt3rxZt912W6nPqSTuvgcLFZ1k/Ze//EU5OTlO9yddGMXq1q2b1q9fr/T0dJfhIz09XV9++aV69+4tb29v1a1bV97e3sUmqBbVoEGDS25TGOKKvldczRGT5HIOVeHk3NL+YKhfv76ioqJK/JwX/sDVq1fP5YT9ipoAXFjzkSNHir32hw8fvqz3ryt16tRxjFhePFH9Ys2bN5d04Y+vadOmadq0aTp58qQ2b96sxx57TL/73e+UlpamwMDA31zP2LFjNXLkSBUUFOi11177zffnjrVr18qyLKfvOOnCHMJNmzZpzZo1Onz4cLE/6qordjPZSOPGjfXII4+oX79+Gj16dInb9e3bV5mZmcrPz1d0dHSxS+vWrSXJsdDesmXLnG7/j3/845KT1SzLkjGm2A/dG2+8UeoCd7/FuHHjdOLECT355JPauXOn4uLinH4ELcsqVs8HH3ygn3766ZL33axZMx07dszpx+fs2bPF1rfp27evjDH66aefXL62bdu2LfExatasqY4dO2rVqlVOI0UFBQV6++231aRJE6ddLe4aP368GjZsqJkzZ+rYsWPF+ufOnav//Oc/mj59utNf+j169NDRo0e1YMECderUyREwunfvrtWrVys1NbXYLiZ3ufseLEnr1q2dtr/4aK6ZM2fKGKNJkyYVe6/l5+dr4sSJMsZo5syZkqSAgAB17dpV77zzTomhQ5J69+6tLVu2lLqrr7COb775xql97dq1LrfPyckp1peUlCQvL69iP1IX69u3r3bv3q2rrrrK5etXGGa6detW4mNUhNtvv12Sik3GT01N1bfffqvu3buXy+MEBgaqW7du+n//7/8pKirK5WvgKgzWrl1bgwYN0uTJk3X8+PFiR51drv79+6t///4aO3asOnXqVC73WZrExEQlJydr2LBhatq0qVPfXXfdpXr16mnJkiVKTEzU1Vdfrc6dO1d4TZUdIzM2U/RwU1eGDh2qZcuWqU+fPpoyZYpuvvlm+fr6Kj09XVu2bNHvf/979e/fX9dee61GjhypBQsWyNfXVz169NDu3bs1b948xzyUkgQHB6tLly56/vnnVb9+fTVr1kwpKSl68803LzkKcrnuvPNO1a9fX88//7y8vb2LBbq+fftq6dKluuaaaxQVFaUvv/xSzz//vFu7DoYMGaInn3xSQ4cO1SOPPKLc3Fy99NJLxX4sb731Vt13332Ki4vTjh071KVLF9WsWVNHjhzRZ599prZt22rixIklPk5CQoJ69uypbt266eGHH1aNGjX06quvavfu3Vq+fPllrbZau3ZtrVq1Sn379lX79u31yCOP6IYbblB2drZWrlypZcuWaciQIXrkkUecbnf77bfLsixt3LjR6eiQHj16OF7byw0z7r4HL8ett96qBQsWaOrUqercubPuv/9+NW3aVIcOHdIrr7yi7du3a8GCBU67x+bPn6/OnTurY8eOevTRR9WyZUsdPXpUa9eu1V/+8hcFBQU5jnLq0qWLHnvsMbVt21YnT57U+vXrNW3aNF1zzTXq0KGDWrdurYcffljnz59XnTp1tHr1an322Wcua61Xr54mTpyoQ4cO6eqrr9aHH36oxYsXa+LEicV+pC42a9Ysbdq0STExMXrggQfUunVr5ebm6sCBA/rwww/1+uuvq0mTJho1apRefPFFjRo1Ss8++6xatWqlDz/8sMRFJn+r1q1b67777tPLL78sLy8v9e7dWwcOHNATTzyhiIgIPfjgg+X2WAsXLlTnzp112223aeLEiWrWrJlycnL0ww8/aN26dfrXv/4lSerXr5+uv/56RUdHq0GDBjp48KAWLFigyMjIUo84Kgt/f/8SV0l2Zd++fY45PRdr0qSJ0/fRmTNnHNudOXNGP/74o9577z29//776tq1q15//fVi9+Hn56cRI0bo5ZdfljHGrd+EasFDE4/hhouPZiqNq0Xzzp07Z+bNm2duuOEG4+/vb2rVqmWuueYaM2HCBLNv3z7Hdnl5eeahhx4yDRs2NP7+/qZTp05m27ZtxRbNc3W0Rnp6uhk4cKCpU6eOCQoKMv/zP/9jdu/eXey2JT0Pd46QKurBBx80kkyfPn2K9Z04ccKMGzfONGzY0AQGBprOnTubTz/91HTt2tXp9SlpQbEPP/zQtGvXzgQEBJgWLVqYRYsWFTuaqdCSJUtMx44dTc2aNU1AQIC56qqrzKhRo5yO3CjJp59+am6//XbHbTt16mTWrVvntE1ZjmYqdOjQITN58mTTokULU6NGDRMSEmK6dOli3n77bcdRUkXdeOONRpL5/PPPHW0//fSTkWTq1atX7HYXH+VxMVevk7vvwcjISHPHHXe4/TwLbdu2zQwaNMiEhoYaHx8f07BhQzNgwACzdetWl9vv3bvX3H333aZevXqmRo0apmnTpmbMmDEmNzfXsU1aWpoZO3asCQsLM76+viY8PNwMHjzYcVSUMcZ8//33plevXiY4ONg0aNDA/PGPfzQffPCBy6OZ2rRpYz7++GMTHR1t/Pz8TKNGjcxjjz1WbLE/FTkqxhhjfv75Z/PAAw+Y5s2bG19fX1O3bl3Tvn178/jjj5tTp045tiv8HNaqVcsEBQWZgQMHmq1bt1bYonn5+flmzpw55uqrrza+vr6mfv36ZuTIkU5H6lz8/Isq6f9bkpk8ebJT2/79+83YsWNN48aNja+vr2nQoIGJiYkxzzzzjGObF154wcTExJj69es7/l/HjRtnDhw4UOpzd/doppJcztFMjz/+uGPbwqO9Ci81a9Y0LVq0MIMGDTLvvPOOyc/PL/Gxv/76ayPJeHt7m8OHD5daZ3U5mskypgqetAUAPCw2Nla//PKLdu/e7elS4MLSpUsVFxenH374QZGRkW4dwWknBQUFKigoUPfu3ZWZmVnl34fMmQEAVFstW7aUr69vqfOp7GjAgAHy9fUtdQmAqoSRGQCoAIzMVG6ZmZlOh/i3a9euSo3O/Pe//3WsXB0QEKA2bdp4uKKKRZgBAAC2xm4mAABga4QZAABga4QZAABga1VntlMJCgoKdPjwYQUFBV3WgmQAAODKM8YoJydH4eHh8vIqfeylyoeZw4cPO06uBwAA7CUtLe2SK7lX+TBTeL6ZtLS0Sy7RDwAAKofs7GxFRES4dUbwKh9mCnctBQcHE2YAALAZd6aIMAEYAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYWpVfNA9A1RUbG+v498cff+yxOgB4FiMzAGwpMTGx1OsAqg+Phpn4+HhZluV0CQsLc/QbYxQfH6/w8HAFBAQoNjZWe/bs8WDFACqLt956q9TrAKoPj4/MtGnTRkeOHHFcdu3a5eibO3eu5s+fr0WLFik1NVVhYWHq2bOncnJyPFgxAE+78847y9QOoGrz+JwZHx8fp9GYQsYYLViwQI8//rgGDBgg6cJfXqGhoUpKStKECRNc3l9eXp7y8vIc17OzsyumcAAecfLkyRI/19nZ2Tp58qRq1659ZYsC4FEeH5nZt2+fwsPD1bx5cw0dOlQ//vijJGn//v3KyMhQr169HNv6+fmpa9eu2rp1a4n3l5CQoJCQEMclIiKiwp8DgCtn2LBhv6kfQNXj0TDTsWNH/e1vf9OGDRu0ePFiZWRkKCYmRpmZmcrIyJAkhYaGOt0mNDTU0efKzJkzlZWV5bikpaVV6HMAcGUtX778N/UDqHo8upupd+/ejn+3bdtWt9xyi6666iq99dZb6tSpkyTJsiyn2xhjirVdzM/PT35+fhVTMACPq127toKDg13uagoJCWEXE1ANeXw308Vq1qyptm3bat++fY55NEVHYY4dO1ZstAZA9bJ27VqX7WvWrLnClQCoDCpVmMnLy9O3336rRo0aqXnz5goLC9OmTZsc/WfPnlVKSopiYmI8WCWAymD06NGlXgdQfXg0zDz88MNKSUnR/v37tX37dg0aNEjZ2dkaPXq0LMvS1KlTNXv2bK1evVq7d+/WmDFjFBgYqOHDh3uybACVQNGJvkz8Baovj86ZSU9P17Bhw/TLL7+oQYMG6tSpk7744gtFRkZKkqZPn64zZ85o0qRJOnHihDp27KiNGzcqKCjIk2UDqASWLVsmy7Ic8+iSkpI0duxYT5cFwAMsY4zxdBEVKTs7WyEhIcrKylJwcLCnywFQDtLT0zV69Gjl5+c72nx8fLR06VI1adLEg5UBKC9l+f2uVHNmAOBSjDFauHBhie1V/O8zAC4QZgDYyqFDh5Samuo0KiNJ+fn5Sk1N1aFDhzxUGQBPIcwAsJWmTZuqbdu2LvuioqLUtGnTK1wRAE8jzACwnYvPv3ax3NzcK1wJgMqAMAPAVg4ePKjvv//eZd/333+vgwcPXuGKAHgaYQYAANgaYQaArURGRpY6Z6ZwnSoA1QdhBoCtWJalGTNmFDvhrJeXl8t2AFUfYQaA7TRp0kRDhw51ahs6dKgaN27soYoAeBJhBoAtjR492rEqaHBwsEaNGuXhigB4CmEGgC35+/vr0UcfVWhoqB599FH5+/t7uiQAHuLRE00CwG8RExOjmJgYT5cBwMMYmQFgW1u3btWQIUO0detWT5cCwIMIMwBsKTc3V/Pnz9fRo0c1f/58Vv8FqjHCDABbWrZsmTIzMyVJmZmZSkpK8nBFADyFMAPAdtLT05WUlCRjjCTJGKOkpCSlp6d7uDIAnkCYAWArxhgtXLiwxPbCgAOg+iDMALCVQ4cOKTU1Vfn5+U7t+fn5Sk1N1aFDhzxUGQBPIcwAsJWmTZuqQ4cO8vJy/vry8vLSzTffrKZNm3qoMgCeQpgBYCuWZWnKlCnFdicZYzRlyhTOzQRUQ4QZAFWCZVnMlwGqKcIMAFspnOhbdDeTZVlMAAaqKcIMAFthAjCAoggzAGylpAnA3t7eTAAGqinCDABbYQIwgKIIMwCqBGMM82WAaoowA8BWCicAFx2BYQIwUH0RZgDYSuEE4IKCAqf2goICJgAD1RRhBoCtFE4AdjUywwRgoHoizACwFcuyNGTIEJcTgIcMGcIEYKAaIswAsBVjjFauXOlyZGbFihXMmQGqIcIMAFspnDPjamSGOTNA9USYAWArhXNmvL29ndpZNA+ovggzAGylpEXzJLFoHlBNEWYA2E6TJk3Upk0bp7Y2bdqocePGHqoIgCcRZgDYTnp6uvbu3evUtmfPHqWnp3uoIgCeRJgBYCuFKwC7WjSPFYCB6okwA8BWOJoJQFGEGQC2EhERoeDgYJd9wcHBioiIuMIVAfA0wgwAW0lLS1N2drbLvuzsbKWlpV3higB4GmEGgK0wMgOgKMIMAFthZAZAUYQZALbStGlTtW3b1mVfVFQUKwAD1RBhBkCVwWHZQPVEmAFgK4cOHdKuXbtc9u3atYtDs4FqiDADwFaYAAygKMIMAFthAjCAoggzAGyladOm6tChg8u+m2++mQnAQDVEmAFgK5ZlaciQIS77hgwZIsuyrnBFADyNMAPAVowxWrlyZbHQYlmWVqxYwRFNQDVEmAFgK5xoEkBRhBkAtlI4Z8bVyAxzZoDqiTADwFYK58y4GplhzgxQPRFmANgKc2YAFEWYAWArzJkBUBRhBoCtcKJJAEURZgBUGexiAqonwgwAW+FEkwCKIswAsJXCQ7O9vJy/vry8vDg0G6imCDMAbMWyLE2ZMqXY0UxeXl4u2wFUfYQZALbTpEkTDR8+3BFcLMvS8OHD1bhxYw9XBsATCDMAbGnEiBGqV6+eJKl+/foaPny4hysC4CmEGQC25O/vr2nTpik0NFQPPvig/P39PV0SAA+pNGEmISFBlmVp6tSpjjZjjOLj4xUeHq6AgADFxsZqz549nisSAABUOpUizKSmpuqvf/2roqKinNrnzp2r+fPna9GiRUpNTVVYWJh69uypnJwcD1UKoLLIzc3V/PnzdfToUc2fP1+5ubmeLgmAh3g8zJw6dUojRozQ4sWLVadOHUe7MUYLFizQ448/rgEDBuj666/XW2+9pV9//VVJSUkerBhAZbBs2TJlZmZKkjIzM/leAKoxj4eZyZMn64477lCPHj2c2vfv36+MjAz16tXL0ebn56euXbtq69atJd5fXl6esrOznS4Aqpb09HQlJSU5Vvw1xigpKUnp6ekergyAJ3g0zKxYsUJfffWVEhISivVlZGRIkkJDQ53aQ0NDHX2uJCQkKCQkxHGJiIgo36IBeJQxRgsXLiyxnVMaANWPx8JMWlqapkyZorfffrvUoxCKLoBljCl1UayZM2cqKyvLcUlLSyu3mgF4XuFZs/Pz853a8/PzOWs2UE15LMx8+eWXOnbsmNq3by8fHx/5+PgoJSVFL730knx8fBwjMkVHYY4dO1ZstOZifn5+Cg4OdroAqDoKT2fg7e3t1O7t7c3pDIBqymNhpnv37tq1a5d27tzpuERHR2vEiBHauXOnWrRoobCwMG3atMlxm7NnzyolJUUxMTGeKhuAhxWezqCkdk5nAFQ/Pp564KCgIF1//fVObTVr1lS9evUc7VOnTtXs2bPVqlUrtWrVSrNnz1ZgYCArfQLVXOHpDN5++23HrmdOZwBUXx4LM+6YPn26zpw5o0mTJunEiRPq2LGjNm7cqKCgIE+XBsDDRowYoTVr1ig7O1tBQUH8kQNUY5UqzHz88cdO1y3LUnx8vOLj4z1SD4DK7eJDswFUXx5fZwYALseyZct06tQpSRcW32TRPKD6IswAsB0WzQNwMcIMAFth0TwARRFmANgKi+YBKIowA8BWWDQPQFGEGQC2Urg4nqvdSSyaB1RPhBkAttOkSRO1adPGqa1NmzYsmgdUU4QZALaTnp6uPXv2OLXt2bOHo5mAaoowA8BWSjpqqaCggKOZgGqKMAPAVgqPZioaWowxHM0EVFOEGQC2EhERoeDgYJd9wcHBioiIuMIVAfA0wgwAW0lLS1N2drbLvuzsbKWlpV3higB4GmEGgK1ERESoVq1aLvtq1arFyAxQDRFmANjKoUOHHCeYLOrUqVPMmQGqIcIMAFu51NFKHM0EVD+EGQC2cqkVflkBGKh+CDMAbCUyMlJt27Z12RcVFaXIyMgrXBEAT/PxdAGAnRhjlJub6+kyqr2pU6dq3LhxxdqnTJnC/4+H+fv7MzqGK44wA5RBbm6uevfu7ekyUAJXAQdXVnJysgICAjxdBqoZdjMBAABbY2QGKAN/f38lJyd7ugzowihZ//79JUmPPfaYbrvtNg9XBOnCZwS40ggzQBlYlsUQeiV022238f8CVGPsZgIAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALb2m8NMdna23nvvPX377bflUQ8AAECZlDnMDB48WIsWLZIknTlzRtHR0Ro8eLCioqL07rvvlnuBAAAApSlzmPnkk0902223SZJWr14tY4xOnjypl156Sc8880y5FwgAAFCaMoeZrKws1a1bV5K0fv16DRw4UIGBgbrjjju0b9++ci8QAACgNGUOMxEREdq2bZtOnz6t9evXq1evXpKkEydOyN/fv9wLBAAAKI1PWW8wdepUjRgxQrVq1VJkZKRiY2MlXdj91LZt2/KuDwAAoFRlDjOTJk3SzTffrLS0NPXs2VNeXhcGd1q0aMGcGQAAcMWVOcxIUnR0tKKjo53a7rjjjnIpCAAAoCzcDjOzZs1yuv7kk0+WezEAAABl5XaY2b9/v+PflmVVSDEAAABl5XaYSUxMrMg6AAAALkuZD83+6KOPSuwrXBkYAADgSilzmBk4cKBSU1OLtS9YsECPPfZYuRQFAADgrjKHmRdffFF9+vTR3r17HW3z5s3TU089pQ8++KBciwMAALiUMh+aHRcXp8zMTPXq1UufffaZVq5cqdmzZys5OVkxMTEVUSMAAECJLmudmYcffliZmZmKjo5Wfn6+Nm7cqI4dO5Z3bQAAAJfkVph56aWXirU1atRIgYGB6tKli7Zv367t27dLkh544IHyrRAAAKAUboWZF1980WW7t7e3Pv/8c33++eeSLqw/Q5gBAABXklth5uIF8wAAACqTMh/NBAAAUJmUW5hJS0vT2LFjy+vuAAAA3FJuYeb48eN66623yuvuAAAA3OL2odlr164ttf/HH3/8zcUAAACUldth5q677pJlWTLGlLgNZ9MGAABXmtu7mRo1aqR3331XBQUFLi9fffVVRdYJAADgktthpn379qUGlkuN2rjy2muvKSoqSsHBwQoODtYtt9yi5ORkR78xRvHx8QoPD1dAQIBiY2O1Z8+eMj0GAACo2twOM4888kip515q2bKltmzZUqYHb9KkiZ577jnt2LFDO3bs0O23367f//73jsAyd+5czZ8/X4sWLVJqaqrCwsLUs2dP5eTklOlxAABA1WWZsg6nVLC6devq+eef19ixYxUeHq6pU6dqxowZkqS8vDyFhoZqzpw5mjBhglv3l52drZCQEGVlZSk4OLgiSwdwBZ05c0a9e/eWJCUnJysgIMDDFQEoT2X5/a40i+bl5+drxYoVOn36tG655Rbt379fGRkZ6tWrl2MbPz8/de3aVVu3bi3xfvLy8pSdne10AQAAVVeZwsyxY8eUnp7uuH7+/Hn96U9/UteuXfXQQw/p119/LXMBu3btUq1ateTn56c//OEPWr16ta677jplZGRIkkJDQ522Dw0NdfS5kpCQoJCQEMclIiKizDUBAAD7KFOYue+++5wWxnv++ee1ePFidejQQWvXrtWDDz5Y5gJat26tnTt36osvvtDEiRM1evRo7d2719Ff9HBvY0yph4DPnDlTWVlZjktaWlqZawIAAPZRpjDz9ddfq1u3bo7rf//73/XSSy9p3rx5WrFihdatW1fmAmrUqKGWLVsqOjpaCQkJuuGGG7Rw4UKFhYVJUrFRmGPHjhUbrbmYn5+f4+iowgsAAKi63Fo0Ly4uTpJ0+PBhzZ8/X4sXL9bZs2f13XffafXq1dqwYYMKCgp07Ngxx/mZlixZclkFGWOUl5en5s2bKywsTJs2bdKNN94oSTp79qxSUlI0Z86cy7pvAABQ9bgVZhITEyVJn3zyicaNG6fevXtr5cqV2rVrl1asWCFJyszM1Nq1a8sUYh577DH17t1bERERysnJ0YoVK/Txxx9r/fr1sixLU6dO1ezZs9WqVSu1atVKs2fPVmBgoIYPH34ZTxUAAFRFbp/OQJLuuOMOjRs3Tv369dN7772n6dOnO/r+/e9/67rrrivTgx89elT33HOPjhw5opCQEEVFRWn9+vXq2bOnJGn69Ok6c+aMJk2apBMnTqhjx47auHGjgoKCyvQ4AACg6irTOjO5ubl69tlntXPnTt16662aPn26vLwuTLuJj49Xy5YtNXLkyAor9nKwzgxQNbHODFC1leX3u0wjM/7+/vrzn//ssi8+Pr4sdwUAAFAuKs2ieQAAAJeDMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGytTEczFfroo4/00Ucf6dixYyooKHDqu9yVfwEAAC5HmcPM008/rVmzZik6OlqNGjUq9aSPAAAAFa3MYeb111/X0qVLdc8991REPQAAAGVS5jkzZ8+eVUxMTEXUAgAAUGZlDjPjx49XUlJSRdQCAABQZmXezZSbm6u//vWv2rx5s6KiouTr6+vUP3/+/HIrDgAA4FLKHGa++eYbtWvXTpK0e/dupz4mAwMAgCutzGFmy5YtFVEHAADAZWHRPAAAYGtujcwMGDBAS5cuVXBwsAYMGFDqtqtWrSqXwgAAANzhVpgJCQlxzIcJCQmp0IIAAADKwq0wk5iY6PLfAAAAnsacGQAAYGuEGQAAYGuEGQAAYGuEGQAAYGtuh5kFCxbo+PHjFVkLAABAmbkdZp5++mmFh4dr8ODB2rhxo4wxFVkXAACAW9wOMxkZGXrzzTd1/Phx9e7dW5GRkXrqqae0f//+iqwPAACgVG6HGT8/P40YMUKbN2/Wf//7X8XFxelvf/ubWrVqpR49emjFihXKy8uryFoBAACKuawJwM2aNdPTTz+t/fv3a/369QoNDdW4ceMUHh5e3vUBAACU6jcfzeTl5SXLsmSMUUFBQXnUBAAA4Da3TmdQ1MGDB7V06VItXbpUaWlp6tKlixYvXqyBAweWd32QZIxRbm6up8sAKpWLPxN8PoDi/P39HedVrOrcDjO5ubl69913tWTJEqWkpKhRo0YaPXq0xo4dqxYtWlRkjdVebm6uevfu7ekygEqrf//+ni4BqHSSk5MVEBDg6TKuCLfDTFhYmHJzc9W3b1+tW7dOv/vd7+TlxZp7AADAs9wOM08++aRGjRql+vXrV2Q9uIRT7YbJeF3W3kGgajFGKjh/4d9ePlI1GU4HSmMVnFetncs9XcYV5/av4rRp0yRJ+/bt05o1a3TgwAFZlqXmzZvrrrvuYlfTFWK8fCRvX0+XAVQSNTxdAFCpVNflbMv0J35CQoKeeOIJGWPUsGFDGWP0888/69FHH9Xs2bP18MMPV1SdAAAALrk96WXLli3605/+pD/96U/65ZdfdOTIEWVkZDjCzKOPPqpPPvmkImsFAAAoxu2Rmddff13jx49XfHy8U3vdunU1a9YsZWRk6LXXXlOXLl3Ku0YAAIASuT0y8+9//1v33HNPif333HOPvvjii3IpCgAAwF1uh5mjR4+qWbNmJfY3b95cGRkZ5VETAACA29wOM7m5uapRo+QjB3x9fXX27NlyKQoAAMBdZTqa6Y033lCtWrVc9uXk5JRLQQAAAGXhdphp2rSpFi9efMltAAAAriS3w8yBAwcqsAwAAIDLw8mVAACArbkdZvr06aOsrCzH9WeffVYnT550XM/MzNR1111XrsUBAABcitthZsOGDcrLy3NcnzNnjo4fP+64fv78eX333XflWx0AAMAluB1mjDGlXgcAAPAE5swAAABbczvMWJYly7KKtQEAAHiS24dmG2M0ZswY+fn5SbqwIvAf/vAH1axZU5Kc5tMAAABcKW6HmdGjRztdHzlyZLFtRo0a9dsrAgAAKAO3w0xiYmJF1gEAAHBZmAAMAABsze2RmbFjx7q13ZIlSy67GAAAgLJyO8wsXbpUkZGRuvHGG1ljBgAAVBpuh5k//OEPWrFihX788UeNHTtWI0eOVN26dSuyNgAAgEtye87Mq6++qiNHjmjGjBlat26dIiIiNHjwYG3YsIGRGgAA4DFuj8xIkp+fn4YNG6Zhw4bp4MGDWrp0qSZNmqRz585p7969qlWrVkXVWa05hcX8c54rBABQuV30G1GdBhrKFGYuVrgisDFGBQUF5VkTirh4QcKgr1d4sBIAgF3k5eUpMDDQ02VcEWU6NDsvL0/Lly9Xz5491bp1a+3atUuLFi3SoUOHGJUBAAAe4fbIzKRJk7RixQo1bdpUcXFxWrFiherVq1eRteF/FZ5CQpJybhgqeft6sBoAQKWVf84xgn/xb0dV53aYef3119W0aVM1b95cKSkpSklJcbndqlWryq04XOB0Qk9vX8IMAOCSqtPJoN0OM6NGjapWLwwAALCHMi2aBwAAUNlwbiYAAGBrHg0zCQkJ6tChg4KCgtSwYUPddddd+u6775y2McYoPj5e4eHhCggIUGxsrPbs2eOhigEAQGXj0TCTkpKiyZMn64svvtCmTZt0/vx59erVS6dPn3ZsM3fuXM2fP1+LFi1SamqqwsLC1LNnT+Xk5HiwcgAAUFlc9qJ55WH9+vVO1xMTE9WwYUN9+eWX6tKli4wxWrBggR5//HENGDBAkvTWW28pNDRUSUlJmjBhQrH7zMvLc1pkLjs7u2KfBAAA8KhKNWcmKytLkhwnsNy/f78yMjLUq1cvxzZ+fn7q2rWrtm7d6vI+EhISFBIS4rhERERUfOEAAMBjKk2YMcZo2rRp6ty5s66//npJUkZGhiQpNDTUadvQ0FBHX1EzZ85UVlaW45KWllaxhQMAAI/y6G6mi91///365ptv9NlnnxXrK7q+jTGmxDVv/Pz8qtWqhwAAVHeVYmTmj3/8o9auXastW7aoSZMmjvawsDBJKjYKc+zYsWKjNQAAoHryaJgxxuj+++/XqlWr9K9//UvNmzd36m/evLnCwsK0adMmR9vZs2eVkpKimJiYK10uAACohDy6m2ny5MlKSkrSmjVrFBQU5BiBCQkJUUBAgCzL0tSpUzV79my1atVKrVq10uzZsxUYGKjhw4d7snQAAFBJeDTMvPbaa5Kk2NhYp/bExESNGTNGkjR9+nSdOXNGkyZN0okTJ9SxY0dt3LhRQUFBV7haAABQGXk0zBhjLrmNZVmKj49XfHx8xRcEAABsp1JMAAYAALhchBkAAGBrhBkAAGBrhBkAAGBrhBkAAGBrhBkAAGBrhBkAAGBrhBkAAGBrhBkAAGBrhBkAAGBrhBkAAGBrhBkAAGBrhBkAAGBrHj1rNsrOKjivS59rHKgGjJEKzl/4t5ePZFmerQeoBKzCz0Q1Q5ixmVo7l3u6BAAAKhV2MwEAAFtjZMYG/P39lZyc7OkygEolNzdX/fv3lyStXr1a/v7+Hq4IqFyq02eCMGMDlmUpICDA02UAlZa/vz+fEaAaYzcTAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNY+GmU8++UT9+vVTeHi4LMvSe++959RvjFF8fLzCw8MVEBCg2NhY7dmzxzPFAgCASsmjYeb06dO64YYbtGjRIpf9c+fO1fz587Vo0SKlpqYqLCxMPXv2VE5OzhWuFAAAVFY+nnzw3r17q3fv3i77jDFasGCBHn/8cQ0YMECS9NZbbyk0NFRJSUmaMGHClSwVAABUUpV2zsz+/fuVkZGhXr16Odr8/PzUtWtXbd26tcTb5eXlKTs72+kCAACqrkobZjIyMiRJoaGhTu2hoaGOPlcSEhIUEhLiuERERFRonQAAwLMqbZgpZFmW03VjTLG2i82cOVNZWVmOS1paWkWXCAAAPMijc2ZKExYWJunCCE2jRo0c7ceOHSs2WnMxPz8/+fn5VXh9AACgcqi0IzPNmzdXWFiYNm3a5Gg7e/asUlJSFBMT48HKAABAZeLRkZlTp07phx9+cFzfv3+/du7cqbp166pp06aaOnWqZs+erVatWqlVq1aaPXu2AgMDNXz4cA9WDQAAKhOPhpkdO3aoW7dujuvTpk2TJI0ePVpLly7V9OnTdebMGU2aNEknTpxQx44dtXHjRgUFBXmqZAAAUMlYxhjj6SIqUnZ2tkJCQpSVlaXg4GBPlwOgnJw5c8axTlVycrICAgI8XBGA8lSW3+9KO2cGAADAHYQZAABga4QZAABga4QZAABga4QZAABga4QZAABga4QZAABga4QZAABga4QZAABga4QZAABga4QZAABga4QZAABga4QZAABga4QZAABga4QZAABga4QZAABga4QZAABga4QZAABga4QZAABga4QZAABga4QZAABga4QZAABga4QZAABga4QZAABgaz6eLgCwE2OMcnNzPV0GJKf/B/5PKg9/f39ZluXpMlDNEGaAMsjNzVXv3r09XQaK6N+/v6dLwP9KTk5WQECAp8tANcNuJgAAYGuMzABl4O/vr+TkZE+XAUkPPfSQ9u7d67jepk0bzZs3z4MVQbrwGQGuNMIMUAaWZTGEXgns2LHDKchI0p49e7Rnzx5FR0d7qCoAnsJuJgC2UlBQoFmzZrnsmzVrlgoKCq5wRQA8jTADwFa2b9+u7Oxsl33Z2dnavn37Fa4IgKcRZgDYSseOHRUcHOyyLyQkRB07drzCFQHwNMIMAFvx8vLSk08+6bLvqaeekpcXX2tAdcOnHoDtREdHq23btk5tUVFRuummmzxUEQBPIswAsKU///nPjlEYLy+vEicFA6j6CDMAbKl27doaMWKEvLy8NGLECNWuXdvTJQHwEMsYYzxdREXKzs5WSEiIsrKySpw0CAAAKpey/H4zMgMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGyNMAMAAGzNx9MFVLTCBY6zs7M9XAkAAHBX4e+2OycqqPJhJicnR5IUERHh4UoAAEBZ5eTkKCQkpNRtqvy5mQoKCnT48GEFBQXJsixPlwOgHGVnZysiIkJpaWmcew2oYowxysnJUXh4uLy8Sp8VU+XDDICqixPJApCYAAwAAGyOMAMAAGyNMAPAtvz8/PTUU0/Jz8/P06UA8CDmzAAAAFtjZAYAANgaYQYAANgaYQYAANgaYQYAANgaYQYAANgaYQYAANgaYQYAANgaYQYAANja/wc0b8XD3Biy9AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create plot\n",
    "plot1 = sns.boxplot(y='MEDV', data=boston_df)\n",
    "plot1 = plot1.set(ylabel =\"MEDV in $1k's\",title='Median Value of Owner-Occupied Homes [MEDV]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb8d806c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    506.000000\n",
       "mean      22.532806\n",
       "std        9.197104\n",
       "min        5.000000\n",
       "25%       17.025000\n",
       "50%       21.200000\n",
       "75%       25.000000\n",
       "max       50.000000\n",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review relevant statistics\n",
    "boston_df['MEDV'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58a1800b",
   "metadata": {},
   "source": [
    "##### <span style=\"color:blue\">Explanation of findings:</span>\n",
    "<span style=\"color:blue\">The box plot and summary statistics show that, for the sample of 506 housing tracts/towns, the median price is \\\\$21.2k, and half of the data is found between \\\\$17.0k and \\\\$25.0k.  The average home price is ~\\\\$22.5k, with a standard deviation of ~\\\\$9.2k. There are numerous outliers on the higher end of the data, with a small number on the lower end.  The range of homes prices spans a \"length\" of \\\\$45k.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30b0f7d1",
   "metadata": {},
   "source": [
    "#### 2. Bar Plot of Charles river variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "58a89578",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number Bordering: 35 ( 6.92 %)\n",
      "Number Not Bordering: 471 ( 93.08 %)\n"
     ]
    }
   ],
   "source": [
    "# Create plot\n",
    "plot2 = sns.catplot(x='CHAS', kind='count', data=boston_df[['CHAS']].replace({1:\"Yes\",0:\"No\"}))\n",
    "plot2 = plot2.set(ylabel =\"Number of Homes\", title='Number of Homes That \\n Do and Do Not Border the Charles River')\n",
    "plt.show()\n",
    "\n",
    "# Focus on relevant statistics\n",
    "nb = boston_df[boston_df['CHAS']==1]['CHAS'].count()\n",
    "nnb = boston_df[boston_df['CHAS']==0]['CHAS'].count()\n",
    "print(\"Number Bordering:\",nb,\"(\",round(nb/(nb+nnb)*100,2),\"%)\")\n",
    "print(\"Number Not Bordering:\",nnb,\"(\",round(nnb/(nb+nnb)*100,2),\"%)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a77686",
   "metadata": {},
   "source": [
    "##### <span style=\"color:blue\">Explanation of findings:</span>\n",
    "<span style=\"color:blue\">The chart and summary count statistics shows that there are very few housing tracts (35, or less than 7%) in the sample that border the Charles River; the vast majority (471, or ~93%) do not.\n",
    "</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7efe968",
   "metadata": {},
   "source": [
    "### 3. Box Plot of MEDV variable vs the (discretized) AGE variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a29a0e58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Discretize the age variable into three groups of 35 years and younger, \n",
    "# between 35 and 70 years and 70 years and older)\n",
    "boston_df.loc[(boston_df['AGE'] <= 35), 'age_group'] = 'Less than 35'\n",
    "boston_df.loc[(boston_df['AGE'] > 35) & (boston_df['AGE'] < 70), 'age_group'] = '35 to 70'\n",
    "boston_df.loc[(boston_df['AGE'] >= 70), 'age_group'] = 'Greater than 70'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "81f063fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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N+rI6R9/kPff09NT9vWzZMnTr1k2vvjZBSb2/FSpUSHfd2stC2v29d+8evLy8YGGR8e/Ue/fuwdLSMs3/+b+V3vo8PDwyfa/v3LkDEYG7u3u6y0uVKgUg48+mjMoy4uzsDB8fnyzrPXz4ECKi915peXl56cWkZcznhvYzAzD887l06dLYt28fJk+ejD59+uDZs2coVaoUvv76a/Tv3z/LfcrrmMyYsa5du2L06NGYP38+fvjhhwzraTvYZnQPE+2XpPaDOzY2FkWKFNEtT0pKyvLL5/Hjx9ixYwfGjBmD4cOH68oTEhLw4MEDg/cpJ7i4uODPP/+EiOh9ud69exdJSUnpJkRvQrueWbNmZTjSK6MP7dR69OiBvXv3YuvWrbh16xYSExPRo0cP3fK1a9fC2toaO3bs0EvctJ1eM6Ot/3rH59cTE+2+jBgxIt1+RQBQvnz5DLeTE8e8YcOGsLKywpYtW9CzZ89062iPQUBAgK6sUaNGGDVqFH7//Xfs378fY8aM0ZWHhYWhZMmSuufGepP3PPXxaNGiBaKiotJ9XUBAABYsWIAtW7bo/U+ltmXLFlhZWenus+Lm5oaDBw8iJSUlw4TGzc0NycnJiI2NTffLWkulUqU5T4C0X+JaqTshpy4rU6ZMhttwdXWFoij4448/dIni6zEA+p9N6W0ju+9dU7BgQVhYWOD27dtplmkHKWTn54Yhn88A8N577+G9995DcnIyjh07hlmzZmHAgAFwd3dH+/btsyUec8XLTGasSJEi+Oabb9CiRQsEBwdnWO+jjz5CXFwckpOT4ePjk+ah/VLSfiCuWrVK7/Xr16/PsvlbURSISJoPpEWLFqXbue5tatSoEZ4+fZrmy17btPwmX2Lp8ff3R4ECBRAdHZ3ucfbx8cnw11dqH3/8MVxcXLBkyRIsXboU5cqVw7vvvqtbrigKrKys9IYev3jxAitWrMhy3doP/dOnT+uVpx7FArxKVMqWLYu//vorw33J7H5GOXHMPTw80L17d+zZsyfdDpiXLl3Cjz/+iMqVK+t1mq1bty6cnJwwc+ZMxMbG6hKdxo0b4+TJk1i/fj0qVaqk+8VtjH/7nru4uKSpr9W6dWtUqlQJkyZNwqVLl9K8dt26dQgLC8Pnn3+ua51o2rQpNBpNph20mzZtCgCYN29epvtWokSJNOfJb7/9hqdPn6Zb//XPjcOHD+PatWuZ3tDuo48+gojgv//9b7rHrmrVqgCAevXqQa1WZ7iN7GZvb4933nkHmzZt0mvpTElJwcqVK1G0aNF0R5i9CUM/n1OztLTEO++8gzlz5gAATpw4kS2xmDO2zJi5SZMmZVmnffv2WLVqFZo1a4b+/fujbt26sLa2xs2bNxEeHo5WrVqhdevWqFixIjp16oSZM2fC2toajRs3xtmzZzF16lS9+9mkx8nJCe+//z6mTJkCV1dXlChRAhEREVi8eDEKFCiQTXv7Zrp06YI5c+YgODgYV69eRdWqVXHw4EFMmDABzZo1S7evxJtwcHDArFmzEBwcjAcPHqBdu3YoXLgw7t27h7/++gv37t3L8gsEePVrNCgoCLNmzYKIpHmPmzdvjunTp6Njx4748ssvERcXh6lTp6b7y/Z1Hh4eaNy4MSZOnIiCBQvC29sb+/fvT/euwj///DOaNm2KwMBAdO3aFUWKFMGDBw9w/vx5nDhxAhs2bMhwOzl1zKdPn46LFy+iU6dO+P3339GiRQuoVCocOXIEU6dOhaOjIzZu3KiX6FlaWqJ+/frYvn07SpYsqetT5e/vD5VKhf379+Prr79+o3iy6z1Pj6WlJTZu3IiAgAD4+vpi8ODB8PX1RUJCArZv344FCxagfv36mDZtmu41HTp0wNKlS9GzZ09cvHgRDRs2REpKCv78809UrFgR7du3x3vvvYfOnTtj/PjxuHPnDj766COoVCqcPHkSdnZ26NevHwCgc+fOGDVqFEaPHo369esjOjoas2fPhrOzc7rxHjt2DJ9//jk++eQT3LhxAyNHjkSRIkXQu3fvDPfR398fX375Jbp164Zjx47h/fffh729PW7fvo2DBw+iatWq6NWrFwoWLIghQ4Zg/PjxetsICQnJ9stlWhMnTkRAQAAaNmyIIUOGwMbGBnPnzsXZs2exZs2aNJdQ35Shn8/z58/Hb7/9hubNm6N48eLQaDS6vnXZ9Rlm1kzV85iMl3o0U2ZeH80kIvLy5UuZOnWqVK9eXdRqtTg4OEiFChXkq6++kr///ltXLyEhQQYPHiyFCxcWtVot9erVk8jIyDQjGNIbFXPz5k1p27atFCxYUBwdHeXDDz+Us2fPZjj64fX9MGSElMj/RoXcu3fP4P2Pi4uTnj17iqenp1hZWYm3t7eMGDEizUgRANKnTx+9Mu1InylTpqQb74YNG/TKIyIipHnz5lKoUCGxtraWIkWKSPPmzdPUy8xff/0lAMTS0lJu3bqVZvmSJUukfPnyolKppFSpUjJx4kRZvHixAJCYmBhdvddHM4mI3L59W9q1ayeFChUSZ2dn6dSpkxw7dizNaA5tHJ9++qkULlxYrK2txcPDQz744AOZP39+lvtg6DE3dDSTVmJiosyZM0feeecdcXBwEJVKJeXLl5ehQ4fK/fv3033NTz/9JADkiy++0CsPCAgQALJt2za98oze2/RGfYkY9p5ndd5m5P79+zJ8+HCpUKGC7n+3bt26Mnv2bElMTExT/8WLFzJ69GgpW7as2NjYiIuLi3zwwQdy+PBhXZ3k5GSZMWOGVKlSRWxsbMTZ2Vl8fX1l+/btujoJCQkydOhQKVasmNja2kr9+vXl1KlTGf4/h4WFSefOnaVAgQJia2srzZo10/tsEUk7mklryZIl8s4774i9vb3Y2tpK6dKlpUuXLnLs2DFdnZSUFJk4caIUK1ZMbGxspFq1arJ9+/Z0z/H0eHt7S/PmzdNdph059Pr7+scff8gHH3ygi6tevXp6xyj1/r/+eZbR+x0cHCz29vZ6ZYZ8PkdGRkrr1q3F29tbVCqVuLi4SP369dOcu/mVIpLFXbaIiIgyEBoaim7duiEqKsqgzrVEOYF9ZoiIiMisMZkhIiIis8bLTERERGTW2DJDREREZo3JDBEZbdu2bVAUBS4uLuneWC03SkxMRM+ePeHp6QlLS0vUqFEjw7pdu3bVu4396xwcHPRmjiYi0+J9ZojIaIsXLwbw6lb9W7ZsSXe29txm3rx5+PnnnzFr1izUrl0702SFiMwLW2aIyCixsbH49ddf8cEHH0CtVusSm9zu7NmzsLW1Rd++feHr66u7uywRmT8mM0RmZsuWLVAUJd2Zp+fNmwdFUXS3ob9y5Qrat28PLy8vqFQquLu7o1GjRjh16tQbb3/ZsmVISkrCwIED0aZNG+zfvz/dW8o/evQIPXr0QKFCheDg4IDmzZvjypUrUBQFISEhenX//vtvdOzYEYULF4ZKpULFihV1t2rPikajwYgRI1CyZEnY2NigSJEi6NOnDx49eqSroygKFi1ahBcvXkBRFCiKkukt/9/E9evX0alTJ719mDZtGlJSUnR1rl69CkVRMGXKFPz4448oUaIEbG1t0aBBA1y6dAkvX77E8OHD4eXlBWdnZ7Ru3TrdWdDXrVsHX19f2Nvbw8HBAYGBgTh58mS27g+RWTHtPfuIyFgvX76UwoULS1BQUJpldevWlVq1aumely9fXsqUKSMrVqyQiIgI2bhxowwePDjLuyxnply5cuLp6SlJSUmyb98+ASAhISF6dZKTk+Xdd98VtVotkyZNkrCwMBk7dqyULVtWAMiYMWN0dc+dOyfOzs5StWpVWb58uYSFhcngwYPFwsIizXpfl5KSIoGBgWJlZSWjRo2SsLAwmTp1qtjb20vNmjV1dxuOjIyUZs2aia2trURGRkpkZKTcvXs3w/Vq79L68uXLdB/29vZ6d8G9e/euFClSRNzc3GT+/Pmye/du6du3rwCQXr166epp7yDs7e0tLVq0kB07dsjKlSvF3d1dypUrJ507d5bu3bvLrl27ZP78+eLg4CAtWrTQi+2HH34QRVGke/fusmPHDtm0aZP4+vqKvb29nDt3Lqu3jyhPYjJDZIYGDRoktra28ujRI11ZdHS0AJBZs2aJyKvb4AOQmTNnZtt2f//9dwEgw4cPF5FXyUTJkiXF29tbUlJSdPV27twpAGTevHl6r584cWKaZCYwMFCKFi0qjx8/1qvbt29fUavV8uDBgwzj2b17twCQyZMn65WvW7dOAMiCBQt0ZendRj4jwcHBAiDTR+pkZvjw4QJA/vzzT7319OrVSxRFkYsXL4rI/5KZ6tWrS3Jysq7ezJkzBYC0bNlS7/UDBgwQALpjc/36dbGyspJ+/frp1Xvy5Il4eHjIp59+atD+EeU1vMxEZIa6d++OFy9e6M0evXTpUqhUKnTs2BEAUKhQIZQuXRpTpkzB9OnTcfLkSb1LHm9C2z+me/fuAF5dvunatSuuXbumd9krIiICAPDpp5/qvb5Dhw56zzUaDfbv34/WrVvDzs4OSUlJukezZs2g0Whw5MiRDOP57bffACDNyKJPPvkE9vb26V6KM5StrS2ioqLSfdja2qaJo1KlSqhbt65eedeuXSEiuji1mjVrBguL/338VqxYEcCrSURT05Zfv34dALBnzx4kJSWhS5cuesdKrVajfv36OHDgwBvvL5E5YzJDZIYqV66MOnXqYOnSpQCA5ORkrFy5Eq1atUKhQoUAQNevJjAwEJMnT0atWrXg5uaGr7/+Gk+ePDF6m0+ePMGGDRtQt25duLm54dGjR3j06BFat24NRVH0OgLHxcXByspKF4uWu7u73vO4uDgkJSVh1qxZsLa21ns0a9YMAHD//v0MY9Jux83NTa9cURR4eHggLi7O6P3UsrCwgI+PT7qP1ImINg5PT8806/Dy8tItT+3142JjY5NpuUajAQDcuXMHAFCnTp00x2vdunWZHiuivIxDs4nMVLdu3dC7d2+cP38eV65cwe3bt9GtWze9Ot7e3rok49KlS1i/fj1CQkKQmJiI+fPnG7W9NWvW4Pnz5zh69CgKFiyYZvnmzZvx8OFDFCxYEC4uLkhKSsKDBw/0vqBjY2P1XlOwYEFYWlqic+fO6NOnT7rbLVmyZIYxabdz7949vYRGRBAbG4s6deoYtY9vysXFBbdv305TfuvWLQCAq6trtmxHu55ffvkF3t7e2bJOoryALTNEZqpDhw5Qq9UIDQ1FaGgoihQpgiZNmmRYv1y5cvjuu+9QtWpVnDhxwujtLV68GI6Ojti/fz/Cw8P1HlOmTEFCQgJWrVoFAKhfvz4A6F0GA4C1a9fqPbezs0PDhg1x8uRJVKtWLd1WEBcXlwxjatSoEQBg5cqVeuUbN27Es2fPdMtzWqNGjRAdHZ3muC5fvhyKoqBhw4bZsp3AwEBYWVnh8uXLGbYaEeVHbJkhMlMFChRA69atERoaikePHmHIkCF6lz9Onz6Nvn374pNPPkHZsmVhY2OD3377DadPn8bw4cN19Xr06IFly5bh8uXLGf7aP3v2LI4ePYpevXrhgw8+SLPc398f06ZNw+LFi9G3b198+OGH8Pf3x+DBgxEfH4/atWsjMjISy5cvBwC9OH/66Se8++67eO+999CrVy+UKFECT548wT///IPt27en6W+SWkBAAAIDAzFs2DDEx8fD398fp0+fxpgxY1CzZk107tzZ6OP6JgYOHIjly5ejefPm+P777+Ht7Y2dO3di7ty56NWrF8qVK5ct2ylRogS+//57jBw5EleuXMGHH36IggUL4s6dOzh69Cjs7e0xduzYbNkWkVkxdQ9kInpzYWFhutE1ly5d0lt2584d6dq1q1SoUEHs7e3FwcFBqlWrJjNmzJCkpCRdPe3InZiYmAy3ox1Vc+rUqQzraEf0HD9+XEREHjx4IN26dZMCBQqInZ2dBAQEyJEjRwSA/PTTT3qvjYmJke7du0uRIkXE2tpa3NzcxM/PT8aPH5/lMXjx4oUMGzZMvL29xdraWjw9PaVXr17y8OFDvXrGjmbKrO7rQ7NFRK5duyYdO3YUFxcXsba2lvLly8uUKVP0Ri1pRzNNmTJF77Xh4eECQDZs2KBXvnTpUgEgUVFReuVbtmyRhg0bipOTk6hUKvH29pZ27drJvn37DNo/oryGs2YT0VuzevVqBAUF4dChQ/Dz8zN1OESURzCZIaIcsWbNGvz3v/9F1apVYWFhgSNHjmDKlCmoWbOmbug2EVF2YJ8ZIsoRjo6OWLt2LcaPH49nz57B09MTXbt2xfjx400dGhHlMWyZISIiIrPGodlERERk1pjMEBERkVljMkNERERmLc93AE5JScGtW7fg6OgIRVFMHQ4REREZQETw5MkTeHl5pZkP7XV5Ppm5desWihUrZuowiIiI6A3cuHEDRYsWzbROnk9mHB0dAbw6GE5OTiaOhoiIiAwRHx+PYsWK6b7HM5PnkxntpSUnJycmM0RERGbGkC4i7ABMREREZo3JDBEREZk1JjNERERk1pjMEBERkVljMkNERERmjckMERERmTUmM0RERGTWmMwQERGRWWMyQ0RERGaNyQwZbNGiRWjQoAEWLVpk6lCIAAANGjTA+++/jwYNGpg6FCIAwJgxY/D+++9jzJgxpg4lXzFpMhMSEgJFUfQeHh4euuUigpCQEHh5ecHW1hYNGjTAuXPnTBhx/vXo0SOsXLkSKSkpWLlyJR49emTqkCif27VrF1JSUgAAKSkp2LVrl4kjovzuzp07CA8PBwCEh4fjzp07Jo4o/zB5y0zlypVx+/Zt3ePMmTO6ZZMnT8b06dMxe/ZsREVFwcPDAwEBAXjy5IkJI86fRo4cqffF8d1335k4IsrvJk6cmOlzoretd+/ees/79OljokjyH5MnM1ZWVvDw8NA93NzcALxqlZk5cyZGjhyJNm3aoEqVKli2bBmeP3+O1atXmzjq/OXYsWN6SSYAnD59GseOHTNRRJTfBQcHG1VOlNN27dqFe/fu6ZXdvXuXLYZvicmTmb///hteXl4oWbIk2rdvjytXrgAAYmJiEBsbiyZNmujqqlQq1K9fH4cPH85wfQkJCYiPj9d70JtLSUlBSEhIustCQkJ0rTVEb8uzZ88QExOT7rKYmBg8e/bsLUdE+V1ycjImT56c7rLJkycjOTn5LUeU/5g0mXnnnXewfPly7NmzBwsXLkRsbCz8/PwQFxeH2NhYAIC7u7vea9zd3XXL0jNx4kQ4OzvrHsWKFcvRfcjrIiMjM0wI4+PjERkZ+ZYjovyuR48e/2o5UXbbtm1bhglLcnIytm3b9pYjyn9Mmsw0bdoUbdu2RdWqVdG4cWPs3LkTALBs2TJdHUVR9F4jImnKUhsxYgQeP36se9y4cSNngs8nfH194eTklO4yZ2dn+Pr6vuWIKL9bvHjxv1pOlN1atmwJS0vLdJdZWVmhZcuWbzmi/Mfkl5lSs7e3R9WqVfH333/rRjW93gpz9+7dNK01qalUKjg5Oek96M1ZWFhkeJlp7NixsLDIVacQ5QP29vYoWbJkusvKlCkDe3v7txwR5XeWlpYYOnRousuGDx+eYaJD2SdXfRMlJCTg/Pnz8PT0RMmSJeHh4YG9e/fqlicmJiIiIgJ+fn4mjDL/8fHxQdWqVfXKqlWrhlq1apkoIsrvUrfeprZkyZK3HAnRK02bNtUNYNEqXLiwXr9PyjkmTWaGDBmCiIgIxMTE4M8//0S7du0QHx+P4OBgKIqCAQMGYMKECdi8eTPOnj2Lrl27ws7ODh07djRl2PnSDz/8oGuFsbCwwPjx400cEeV3I0aMyPQ50ds2d+5cvedz5swxUST5j0mTmZs3b6JDhw4oX7482rRpAxsbGxw5cgTe3t4AgKFDh2LAgAHo3bs3fHx88N///hdhYWFwdHQ0Zdj5UoECBdCpUydYWFigU6dOKFCggKlDonyuYcOGmT4netvc3d1152HDhg0z7RJB2UsRETF1EDkpPj4ezs7OePz4MfvPEOUhixYtwooVK3SDArp06cKRTGRyhw4dwsyZMzFgwAD4+/ubOhyzZsz3d67qM0NEZIibN29i1apV0P4WExGsWrUKN2/eNHFklJ9pNBpMmzYNd+7cwbRp06DRaEwdUr7BZIaIzIqIYMaMGRmW5/HGZsrFVq5cibi4OABAXFwcVq1aZeKI8g8mM0RkVq5du4aoqKg0NylLTk5GVFQUrl27ZqLIKD9ja6FpMZkhIrPi7e2NOnXqpLl5pqIoqFu3rm4AAdHbwtZC02MyQ0RmRVEUdOjQIc0XhIigQ4cOmd4hnCgnsLXQ9JjMEJFZERGsWbMm3ZaZ1atX81cwvXXa1sLX7/RraWnJ1sK3hMkMEZkV7a/g9Fpm+CuYTEFRFAwcODDDcrYW5jwmM0RkVvgrmHKjokWLIigoSJe4KIqCoKAgFClSxMSR5Q9MZojIrPBXMOVWnTp1gouLCwDA1dUVQUFBJo4o/2AyQ0Rmh7+CKTdSq9UYPHgw3N3dMWjQIKjValOHlG9wOgMiMksajQYdO3bE/fv34ebmhlWrVvHLgygP4XQGRJTn8VcwEWmxZYaIiIhyHbbMEBERmcChQ4fwySef4NChQ6YOJV9hMkNERJQNOGu26TCZISIiygacNdt0mMwQERH9S5w127SYzBAREf0LnDXb9JjMkMHYsY2IKC3Omm16TGbIIOzYRkSUPs4XZnpMZsgg7NhGRJQ+zhdmekxmKEvs2EZElDnOF2ZaTGYoU+zYRkRkGM6abTpMZihT7NhGRGQYzhdmOlamDoByN23HtmPHjum1wiiKgjp16rBjGxFRKv7+/vD39zd1GPkOW2YoU4qioEOHDmkuJ4kIOnTowI5tRERkckxmKFMigjVr1qRJWhRFwerVq9lnhoiITI7JDGVK22cmvZYZ9pkhU+ONHIkIYDJDWeDNoCi34o0ciUiLyQxlijeDotyKN3IkIi0mM5Ql3gyKchveyJGIUmMyQwbhzaAot+CNHInodUxmyCC8GRTlFryRIxG9jskMGczf3x8bNmzgDaHIpNgpnXIzjrAzDSYzZDD+k1JuwE7plFtxhJ3pMJkhg/CflHITdkqn3Igj7EyHyQwZZOXKlbh//z4A4P79+/wnJZNjp3TKTTjCzrSYzFCWbt68iZUrV+qVrVy5kv+kZFLslE65BUfYmR6TGcpURv+MKSkp/Cclk2OndMoNOMLO9JjMUKY4NxPlZuyUTrkBR9iZHpMZylTx4sXh5OSU7jInJycUL178LUdE9Ao7pVNuwRF2psdkhjJ1/fp1xMfHp7ssPj4e169ff8sREb3CkSOUm3CEnWkxmaFMaZtPX/9loSgKm0/JZDhyhHIjjrAzHSYzlCltM6mFhf6pYmlpyeZTMgmOHKHciiPsTIfJDGWJzaeUm3DkCOVmHGFnGlamDoDMQ6dOnfDrr7/i/v37bD4lk9Je+jxx4oReQmNpaYnatWvz0mc+JSIm7wQuIkhISAAAqFQqk7dcq9Vqk8fwtjCZIYNom09nzpyJAQMGsPmUTEZ76bNz587plueXD2/Sp9FoEBgYaOowcpU9e/bA1tbW1GG8FbzMRAZj8ynlFrz0SUSpKZLHe8vFx8fD2dkZjx8/zvB+KURkfjQaDTp27Ij79+/Dzc0Nq1atYothPpYbLjNpNBq0atUKALB161aTn4/mfpnJmO9vXmYiIrPES5+UmqIoueqSilqtzlXx5HW8zERERERmjckMEZklTmdARFpMZojILHE6AyLSYjJDRGaH0xkQUWpMZojIrHA6AyJ6HZMZIjIrnM6AiF7HZIaIzIq3tzeqVauW7rJq1apxOgOifIjJDBGZnYwuJfESE1H+xGSGiMzKtWvXcObMmXSXnTlzhpeZiPIhJjNEZFa0s2ZbWOh/fFlYWKBu3bq8zESUDzGZISKzktHs2BYWFpw1myifYjJDRGaHs2YTUWpMZojILHXq1AkuLi4AAFdXVwQFBZk4IiIyFSYzRGSWtLNmu7u7Y9CgQZw1mygfyzXJzMSJE6EoCgYMGKArExGEhITAy8sLtra2aNCgAc6dO2e6IImIiCjXyRXJTFRUFBYsWJDmRliTJ0/G9OnTMXv2bERFRcHDwwMBAQF48uSJiSIlotyCs2YTkZbJk5mnT58iKCgICxcuRMGCBXXlIoKZM2di5MiRaNOmDapUqYJly5bh+fPnWL16tQkjJqLcgLNmE5GWyZOZPn36oHnz5mjcuLFeeUxMDGJjY9GkSRNdmUqlQv369XH48OEM15eQkID4+Hi9BxHlLZw1m4hSM2kys3btWpw4cQITJ05Msyw2NhYA4O7urlfu7u6uW5aeiRMnwtnZWfcoVqxY9gZNRCbFWbOJ6HUmS2Zu3LiB/v37Y+XKlZmOQnj9BlgikulNsUaMGIHHjx/rHjdu3Mi2mInI9DhrNhG9zmTJzPHjx3H37l3Url0bVlZWsLKyQkREBP7zn//AyspK1yLzeivM3bt307TWpKZSqeDk5KT3IKK8QzudgaWlpV65paUlpzMgyqdMlsw0atQIZ86cwalTp3QPHx8fBAUF4dSpUyhVqhQ8PDywd+9e3WsSExMREREBPz8/U4VNRCamnc4go3JOZ0CU/1iZasOOjo6oUqWKXpm9vT1cXFx05QMGDMCECRNQtmxZlC1bFhMmTICdnR06duxoipCJKJfQTmewYsUK3aVnTmdAlH+ZLJkxxNChQ/HixQv07t0bDx8+xDvvvIOwsDA4OjqaOjQiMrF27dph5cqVumSmbdu2pg6JiEzE6GTm6tWr+OOPP3D16lU8f/4cbm5uqFmzJnx9ff/17cQPHDig91xRFISEhCAkJORfrZeI8p5ffvkFKSkpAICUlBRs3LgRPXr0MHFURGQKBiczq1evxn/+8x8cPXoUhQsXRpEiRWBra4sHDx7g8uXLUKvVCAoKwrBhw9gBj4hylPY+M6mtWrUKgYGBKFq0qImiIiJTMagDcK1atTB9+nR06tQJV69eRWxsLI4fP46DBw8iOjoa8fHx2Lp1K1JSUuDj44MNGzbkdNxElE/xPjNE9DqDWmbGjRuH5s2bZ7hcpVKhQYMGaNCgAcaPH4+YmJhsC5CIKDXtfWZel/o+MyVKlHj7gRGRyRjUMpNZIvM6V1dX1KlT540DIiLKDO8zQ0SvM/o+MydOnMCZM2d0z7du3YqPP/4Y3377LRITE7M1OCKi12nvJ5Pe5STeZ4YofzI6mfnqq69w6dIlAMCVK1fQvn172NnZYcOGDRg6dGi2B0hE9LqiRYuicuXKemWVK1fmfWaI8imjk5lLly6hRo0aAIANGzbg/fffx+rVqxEaGoqNGzdmd3xERGncvHkT0dHRemXR0dGcNZsonzI6mRER3b0d9u3bh2bNmgEAihUrhvv372dvdEREr8lo1FJKSgpHMxHlU0YnMz4+Phg/fjxWrFiBiIgIXefgmJiYTCeAJCLKDtrRTNofVVopKSmcNZsonzI6mZk5cyZOnDiBvn37YuTIkShTpgyAV3fj5ASQRJTTvL29UbVq1XSXVatWjaOZiPIho6czqFatmt5oJq0pU6akGSpJRJQTOGKJiFIzOpmZOHEiRowYkabc2toanTp1wpo1a7IlMPofEYFGozF5DAkJCQBe3STR1F8marXa5DGQaVy7dg2nT59Od9np06d50zyifMjoZGbmzJlwcXHBl19+qStLTk5G+/btcfbs2WwNjl7RaDQIDAw0dRi5yp49e2Bra2vqMMgEtDfNO378uF6/GUtLS9SuXZuXmYjyIaP7zPz6668YNmwY1q9fDwB4+fIlPvnkE5w7dw7h4eHZHiARUWram+a93jKXUTkR5X1Gt8zUrl0bmzdvRqtWraBSqbB48WJcvnwZ4eHhHM2UQ9RqNfbs2WPSGDQaDVq1agXg1V2f1Wq1SeMx9fbJtIoWLYqgoCAsX75cVxYUFMSb5hHlU0YnMwDQoEEDrFixAm3btkXFihUREREBV1fX7I6N/p+iKLnqkoparc5V8VD+1K5dO6xcuRIpKSmwsLBA27ZtTR0SEZmIQclMmzZt0i13c3NDgQIF9PrPbNq0KXsiIyLKxC+//KK7QZ6IYOPGjejRo4eJoyIiUzAomXF2dk63nJ1SicgUbt68iVWrVuklM6tWrUJgYCCKFi1q4uiI6G0zKJlZunRpTsdBRGQQ7XQGGZVPnTqVnYCJ8hmjRzMREZmSdjqD5ORkvfLk5GROZ0CUTxmVzJw/fx4nT57UPX/69Ck6deoEb29vtG3bFnfu3Mn2AImIUvP29ka1atXSXcbpDIjyJ6OSmYEDB+L333/XPR83bhyOHj2Kb775Brdu3cKAAQOyOz4iojQymhmbM2YT5U9GJTPR0dGoV6+e7vmGDRswY8YM9O3bF6Ghodi/f3+2B0hElNq1a9fSnR8OAM6cOcPLTET5kEEdgLt16wYAuHPnDqZOnQoHBwc8ffoU169fx7p167Bx40aICB48eIDu3bsDAJYsWZJzURNRvpXRdAYWFhbw8fHhZSaifMio0UyHDx9Gu3bt8Nlnn2HRokW4du2a7g6csbGx2LFjB5MYIspR2mkLOnfurFduYWHB6QyI8imjLjN16NABPXr0QGBgIPr3749evXrplv3xxx+oUaNGdsdHRJSGdjoDbeKiKAqnMyDKx4yaziAkJATFihXDqVOn0K1bN7Rv31637NatWxg0aFC2B0hElJ5OnTrh119/xf379+Hq6oqgoCBTh0REJmL03EwZ3S68f//+/zoYIiJDqdVqDB48GDNnzsSAAQM4+ShRPvZGE01mRUR43ZqIcpy/vz/8/f1NHQYRmZhBfWYqVqyI1atXIzExMdN6f//9N3r16oUff/wxW4IjIiIiyopBLTNz5szBsGHD0KdPHzRp0gQ+Pj7w8vKCWq3Gw4cPER0djYMHDyI6Ohp9+/ZF7969czpuIiIiIgAGJjMffPABoqKicPjwYaxbtw6rV6/G1atX8eLFC7i6uqJmzZro0qULOnXqhAIFCuRwyERERET/Y1SfGT8/P/j5+eVULERERERG46zZREREZNaYzBAREZFZYzJDREREZo3JDBEREZm1HLlpHhHlbSICjUZj8hgSEhIAACqVyuQ36lSr1SaPgSi/eqNk5vLly1i6dCkuX76Mn376CYULF8bu3btRrFgxVK5cObtjJKJcRqPRIDAw0NRh5Cp79uyBra2tqcMgypeMvswUERGBqlWr4s8//8SmTZvw9OlTAMDp06cxZsyYbA+QiIiIKDNGt8wMHz4c48ePx6BBg+Do6Kgrb9iwIX766adsDY6Icie1Wo09e/aYNAaNRoNWrVoBALZu3WryiSZNvX2i/MzoZObMmTNYvXp1mnI3NzfExcVlS1BElLspipKrLqmo1epcFQ8RvV1GX2YqUKAAbt++nab85MmTKFKkSLYERURERGQoo5OZjh07YtiwYYiNjYWiKEhJScGhQ4cwZMgQdOnSJSdiJCIiIsqQ0cnMDz/8gOLFi6NIkSJ4+vQpKlWqhPfffx9+fn747rvvciJGIiIiogwZ3WfG2toaq1atwvfff4+TJ08iJSUFNWvWRNmyZXMiPiIiIqJMvfFN80qXLo3SpUtnZyxERERERjM6menevXumy5csWfLGwRAREREZy+hk5uHDh3rPX758ibNnz+LRo0f44IMPsi0wIiIiIkMYncxs3rw5TVlKSgp69+6NUqVKZUtQRERERIbKllmzLSwsMHDgQMyYMSM7VkdERERksGxJZoBXk08mJSVl1+qIiIiIDGL0ZaZBgwbpPRcR3L59Gzt37kRwcHC2BUZERERkCKOTmZMnT+o9t7CwgJubG6ZNm5blSCciIiKi7GZ0MhMeHp4TcRARERG9kWzrM0NERERkCga1zNSsWROKohi0whMnTvyrgIiIiIiMYVAy8/HHH+dwGEREZK5EBBqNxtRhmFzqY8Dj8YparTa4MeTfMCiZGTNmTE7HQUREZkqj0SAwMNDUYeQqrVq1MnUIucKePXtga2ub49thnxkiIiIya0aPZkpOTsaMGTOwfv16XL9+HYmJiXrLHzx4kG3BERGReUlukfwG3yx5hABI/v+/LQHk/NWV3CkJsNxu+VY3afQpN3bsWCxatAiDBg3CqFGjMHLkSFy9ehVbtmzB6NGjcyJGIiIyF1bIv8kMAFibOoD8yejLTKtWrcLChQsxZMgQWFlZoUOHDli0aBFGjx6NI0eO5ESMRERERBkyOpmJjY1F1apVAQAODg54/PgxAOCjjz7Czp07jVrXvHnzUK1aNTg5OcHJyQm+vr7YtWuXbrmIICQkBF5eXrC1tUWDBg1w7tw5Y0MmIiKiPMzoZKZo0aK4ffs2AKBMmTIICwsDAERFRUGlUhm9rkmTJuHYsWM4duwYPvjgA7Rq1UqXsEyePBnTp0/H7NmzERUVBQ8PDwQEBODJkyfGhk1ERER5lNHJTOvWrbF//34AQP/+/TFq1CiULVsWXbp0MXpuphYtWqBZs2YoV64cypUrhx9++AEODg44cuQIRAQzZ87EyJEj0aZNG1SpUgXLli3D8+fPsXr1amPDJiIiojzK4G5aM2fORJcuXTBp0iRdWbt27VC0aFEcPnwYZcqUQcuWLd84kOTkZGzYsAHPnj2Dr68vYmJiEBsbiyZNmujqqFQq1K9fH4cPH8ZXX32V7noSEhKQkJCgex4fH//GMREREVHuZ3DLzNixY+Hl5YXPPvsMYWFhEBEAQL169TBo0KA3TmTOnDkDBwcHqFQq9OzZE5s3b0alSpUQGxsLAHB3d9er7+7urluWnokTJ8LZ2Vn3KFas2BvFRURERObB4GQmNjYWixcvRlxcHJo2bQpvb2+MGTMGMTEx/yqA8uXL49SpUzhy5Ah69eqF4OBgREdH65a/fhtkEcn01sgjRozA48ePdY8bN278q/iIiIgodzM4mVGpVAgKCsK+fftw+fJldOvWDcuXL0fZsmXRuHFjrFmzRu/yjqFsbGxQpkwZ+Pj4YOLEiahevTp++ukneHh4AECaVpi7d++maa15PU7t6Cjtg4iIiPKuN5rOoESJEhg7dixiYmKwe/duuLu74/PPP4eXl9e/DkhEkJCQgJIlS8LDwwN79+7VLUtMTERERAT8/Pz+9XaIiIgob/jX92m0sLCAoigQEaSkpBj12m+//RZNmzZFsWLF8OTJE6xduxYHDhzA7t27oSgKBgwYgAkTJqBs2bIoW7YsJkyYADs7O3Ts2PHfhk1ERER5xBslM9euXUNoaChCQ0Nx48YNvP/++1i4cCHatm1r1Hru3LmDzp074/bt23B2dka1atWwe/duBAQEAACGDh2KFy9eoHfv3nj48CHeeecdhIWFwdHR8U3CJiIiojzI4GRGo9Fg48aNWLJkCSIiIuDp6Yng4GB0794dpUqVeqONL168ONPliqIgJCQEISEhb7R+IiIiyvsMTmY8PDyg0Wjw0UcfYfv27QgMDISFxRt1uSEiIiLKNgYnM6NHj0aXLl3g6uqak/EQERERGcXgZGbQoEE5GQcRERHRG+F1IiIiIjJrTGaIiIjIrDGZISIiIrP2xslMYmIiLl68iKSkpOyMh4iIiMgoRiczz58/R48ePWBnZ4fKlSvj+vXrAICvv/4akyZNyvYAiYiIiDJjdDIzYsQI/PXXXzhw4ADUarWuvHHjxli3bl22BkdERESUFaOnM9iyZQvWrVuHevXqQVEUXXmlSpVw+fLlbA2OiIiIKCtGJzP37t1D4cKF05Q/e/ZML7nJK0QEGo3G1GGYXOpjwOPxilqtzpPnPBGRuTE6malTpw527tyJfv36AYDuw3zhwoXw9fXN3uhyAY1Gg8DAQFOHkau0atXK1CHkCnv27IGtra2pwyAiyveMTmYmTpyIDz/8ENHR0UhKSsJPP/2Ec+fOITIyEhERETkRIxEREVGGjE5m/Pz8cOjQIUydOhWlS5dGWFgYatWqhcjISFStWjUnYsw1ntUKAiyMPmR5gwiQ8v/D8C2sgPx6eSUlCfYnVpk6CiIiSuWNvpmrVq2KZcuWZXcsuZ+FFWBpbeooTMjG1AEQERGl8cbNDHfv3sXdu3eRkpKiV16tWrV/HRQRERGRoYxOZo4fP47g4GCcP38eIqK3TFEUJCcnZ1twRERERFkxOpnp1q0bypUrh8WLF8Pd3Z1DU4mIiMikjE5mYmJisGnTJpQpUyYn4iEiIiIyitHTGTRq1Ah//fVXTsRCREREZDSjW2YWLVqE4OBgnD17FlWqVIG1tf7onpYtW2ZbcERERERZMTqZOXz4MA4ePIhdu3alWcYOwERERPS2GX2Z6euvv0bnzp1x+/ZtpKSk6D2YyBAREdHbZnQyExcXh4EDB8Ld3T0n4iEiIiIyitHJTJs2bRAeHp4TsRAREREZzeg+M+XKlcOIESNw8OBBVK1aNU0H4K+//jrbgiMiIiLKyhuNZnJwcEBERESaWbIVRWEyQ0RERG/VG900j4iIiCi3MLrPTGoikmZ+JiIiIqK36Y2SmeXLl6Nq1aqwtbWFra0tqlWrhhUrVmR3bERERERZMvoy0/Tp0zFq1Cj07dsX/v7+EBEcOnQIPXv2xP379zFw4MCciJOIiHIpvRb6JNPFQblEqnPgbV29MTqZmTVrFubNm4cuXbroylq1aoXKlSsjJCSEyQwRUT6TkJCg+9tyu6UJI6HcJiEhAXZ2djm+HaMvM92+fRt+fn5pyv38/HD79u1sCYqIiIjIUEa3zJQpUwbr16/Ht99+q1e+bt06lC1bNtsCIyIi86BSqXR/J7dIfoNvFspTkv7XQpf63MhJRp9yY8eOxWeffYbff/8d/v7+UBQFBw8exP79+7F+/fqciJGIiHIxRVH+98QKTGZIR+/cyEFGX2Zq27Yt/vzzT7i6umLLli3YtGkTXF1dcfToUbRu3TonYiQiIiLK0Bvlz7Vr18bKlSuzOxYiIiIioxmczMTHxxtUz8nJ6Y2DISIiIjKWwclMgQIFMr32JSJQFAXJycnZEhgRERGRIQxOZsLDw3V/iwiaNWuGRYsWoUiRIjkSGBEREZEhDE5m6tevr/fc0tIS9erVQ6lSpbI9KCIiIiJD/auJJomIiIhMjckMERERmbV/dWujt3UzHCIiMhP5eaJJAaAdA2MJIL9+RZrgHDA4mWnTpo3ec41Gg549e8Le3l6vfNOmTdkTGRERmR1ONEmmYHAy4+zsrPe8U6dO2R4MERERkbEMTmaWLl2ak3EQEZGZUqvV2LNnj6nDMDmNRoNWrVoBALZu3Qq1Wm3iiEzvbR0DTgeWBRH535Pkl6YLhHKHVOeA3rnxFokINBqNSbadm6Q+Bjwer6jVapP0ZVQUBba2tm99u7mZWq3mMXmLmMxkISEhQfe3/cnVJoyEcpuEhATY2dm99e1qNBoEBga+9e3mZtpfw/ndnj17+AVK+RKHZhMREZFZY8tMFlQqle7vZzU7ApbWJoyGTC75pa6FLvW5YSpz3n8ElaVpLneZmgiQmPLqbxsLIL/eKSIhWUGf3wuYOgwik2IykwW968+W1kxmSCc33GdJZSlQ5+ORsLygAry6uQlR/vZGycylS5dw4MAB3L17FykpKXrLRo8enS2BERERERnC6GRm4cKF6NWrF1xdXeHh4aH361RRFCYzRERE9FYZncyMHz8eP/zwA4YNG5YT8RAREREZxejRTA8fPsQnn3ySE7EQERERGc3oZOaTTz5BWFhYTsRCREREZDSjLzOVKVMGo0aNwpEjR1C1alVYW+uP7vn666+zLTgiIiKirBidzCxYsAAODg6IiIhARESE3jJFUZjMEBER0VtldDITExOTE3EQERERvRFOZ0BERERm7Y1umnfz5k1s27YN169fR2Jiot6y6dOnZ0tguVJKkqkjMB2R/+2/hVX+vXd8fj4HiIhyKaOTmf3796Nly5YoWbIkLl68iCpVquDq1asQEdSqVSsnYsw17E+sMnUIRERE9BqjLzONGDECgwcPxtmzZ6FWq7Fx40bcuHED9evX5/1niIiI6K0zumXm/PnzWLNmzasXW1nhxYsXcHBwwPfff49WrVqhV69eBq9r4sSJ2LRpEy5cuABbW1v4+fnhxx9/RPny5XV1RARjx47FggUL8PDhQ7zzzjuYM2cOKleubGzob0StVmPPnj1vZVu5mUajQatWrQAAW7duhVqtNnFEpsdjQESUOxidzNjb2yMhIQEA4OXlhcuXL+sSi/v37xu1roiICPTp0wd16tRBUlISRo4ciSZNmiA6Ohr29vYAgMmTJ2P69OkIDQ1FuXLlMH78eAQEBODixYtwdHQ0NnyjKYoCW1vOzZuaWq3mMSEiolzD6GSmXr16OHToECpVqoTmzZtj8ODBOHPmDDZt2oR69eoZta7du3frPV+6dCkKFy6M48eP4/3334eIYObMmRg5ciTatGkDAFi2bBnc3d2xevVqfPXVV8aGT0RERHmM0cnM9OnT8fTpUwBASEgInj59inXr1qFMmTKYMWPGvwrm8ePHAIBChQoBeHVPm9jYWDRp0kRXR6VSoX79+jh8+HC6yUxCQoKu5QgA4uPj/1VMRERElLsZncyUKlVK97ednR3mzp2bLYGICAYNGoR3330XVapUAQDExsYCANzd3fXquru749q1a+muZ+LEiRg7dmy2xERERES5X665aV7fvn1x+vRpXefi1JTX7mkiImnKtEaMGIHHjx/rHjdu3MiReImIiCh3MKhlplChQrh06RJcXV1RsGDBDBMJAHjw4IHRQfTr1w/btm3D77//jqJFi+rKPTw8ALxqofH09NSV3717N01rjZZKpYJKpTI6BiIiIjJPBiUzM2bM0I0cmjlzZrZtXETQr18/bN68GQcOHEDJkiX1lpcsWRIeHh7Yu3cvatasCQBITExEREQEfvzxx2yLg4iIiMyXQclMcHBwun//W3369MHq1auxdetWODo66vrIODs7w9bWFoqiYMCAAZgwYQLKli2LsmXLYsKECbCzs0PHjh2zLQ4iIiIyXwYlM8aMCHJycjK47rx58wAADRo00CtfunQpunbtCgAYOnQoXrx4gd69e+tumhcWFvZW7jFDREREuZ9ByUyBAgUy7SeTWnJyssEbF5Es6yiKgpCQEISEhBi8XiIiIso/DEpmwsPDdX9fvXoVw4cPR9euXeHr6wsAiIyMxLJlyzBx4sSciZKIiIgoAwYlM/Xr19f9/f3332P69Ono0KGDrqxly5aoWrUqFixYkK19aoiIiIiyYvR9ZiIjI+Hj45Om3MfHB0ePHs2WoIiIiIgMZXQyU6xYMcyfPz9N+c8//4xixYplS1BEREREhjJ6OoMZM2agbdu22LNnj25iySNHjuDy5cvYuHFjtgdIRPpSd5xPMLy/PeVRqc8BQwZVEOVFRiczzZo1w6VLlzBv3jxcuHABIoJWrVqhZ8+ebJkhegtST6Ta5/eCJoyEcpuEhATY2dmZOgyit87oZAZ4dalpwoQJ2R0LERERkdHeKJn5448/8PPPP+PKlSvYsGEDihQpghUrVqBkyZJ49913sztGIkol9dxjc95/CJWlCYMhk0tI/l8LHeelo/zK6GRm48aN6Ny5M4KCgnDixAldk/eTJ08wYcIE/Prrr9keJBH9T+obWKosATWTGfp/ht7clCivMXo00/jx4zF//nwsXLgQ1tbWunI/Pz+cOHEiW4MjIiIiyorRyczFixfx/vvvpyl3cnLCo0ePsiMmIiIiIoMZncx4enrin3/+SVN+8OBBlCpVKluCIiIiIjKU0cnMV199hf79++PPP/+Eoii4desWVq1ahSFDhqB37945ESMRERFRhozuADx06FA8fvwYDRs2hEajwfvvvw+VSoUhQ4agb9++OREjERFRpkQEGo3GpDGk3r6pYwEAtVqdbzqFv9HQ7B9++AEjR45EdHQ0UlJSUKlSJTg4OGR3bERERAbRaDQIDAw0dRg6rVq1MnUI2LNnD2xtbU0dxlvxRskMANjZ2aU74SQRERHR22RwMtO9e3eD6i1ZsuSNgyEiInoTarUae/bsMWkMIqK795pKpTL5JR61Wm3S7b9NBiczoaGh8Pb2Rs2aNTmZGRER5SqKouSKSyqcG8s0DE5mevbsibVr1+LKlSvo3r07OnXqhEKFCuVkbERERERZMnho9ty5c3H79m0MGzYM27dvR7FixfDpp59iz549bKkhIiIikzGqA7BKpUKHDh3QoUMHXLt2DaGhoejduzdevnyJ6OhojmjKIRxymFZ+GnJIRESZe+PRTIqiQFEUiAhSUlKyMyZ6DYccppWfhhwSEVHmjLoDcEJCAtasWYOAgACUL18eZ86cwezZs3H9+nW2yhAREZFJGNwy07t3b6xduxbFixdHt27dsHbtWri4uORkbPT/OOQwrfw05JCIiDJncDIzf/58FC9eHCVLlkRERAQiIiLSrbdp06ZsC45e4ZBDIiKijBmczHTp0sXkv8aJiIiIXmfUTfOIiIiIchujOgATERER5TZMZoiIiMisMZkhIiIis8ZkhoiIiMwakxkiIiIya0xmiIiIyKwxmSEiIiKzxmSGiIiIzBqTGSIiIjJrTGaIiIjIrBk8nQER5T4JyQoAMXUYJiECJKa8+tvGAsivU8e9OgeI8jcmM0RmrM/vBUwdAhGRyfEyExEREZk1tswQmRm1Wo09e/aYOgyT02g0aNWqFQBg69atUKvVJo7I9HgMKL9iMkNkZhRFga2tranDyFXUajWPCVE+xstMREREZNaYzBAREZFZYzJDREREZo3JDBEREZk1JjNERERk1pjMEBERkVljMkNERERmjckMERERmTUmM0RERGTWmMwQERGRWWMyQ0RERGaNyQwRERGZNSYzREREZNaYzBAREZFZYzJDREREZo3JDBEREZk1JjNERERk1pjMEBERkVljMkNERERmjckMGezQoUP45JNPcOjQIVOHQkREpMNkhgyi0Wgwbdo03LlzB9OmTYNGozF1SERERACYzJCBVq5cibi4OABAXFwcVq1aZeKIiIiIXjFpMvP777+jRYsW8PLygqIo2LJli95yEUFISAi8vLxga2uLBg0a4Ny5c6YJNh+7efMmVq1aBREB8Op9WbVqFW7evGniyIiIiEyczDx79gzVq1fH7Nmz010+efJkTJ8+HbNnz0ZUVBQ8PDwQEBCAJ0+evOVI8y8RwYwZMzIs1yY4REREpmJlyo03bdoUTZs2TXeZiGDmzJkYOXIk2rRpAwBYtmwZ3N3dsXr1anz11VdvM9R869q1a4iKikpTnpycjKioKFy7dg0lSpR4+4ERERH9v1zbZyYmJgaxsbFo0qSJrkylUqF+/fo4fPhwhq9LSEhAfHy83oPenLe3N+rUqQNLS0u9cktLS9StWxfe3t4mioyIiOiVXJvMxMbGAgDc3d31yt3d3XXL0jNx4kQ4OzvrHsWKFcvROPM6RVEwcODADMsVRTFBVERERP+Ta5MZrde/LEUk0y/QESNG4PHjx7rHjRs3cjrEPK9o0aIICgrSHXdFURAUFIQiRYqYODIiIqJcnMx4eHgAQJpWmLt376ZprUlNpVLByclJ70H/XqdOneDi4gIAcHV1RVBQkIkjIiIieiXXJjMlS5aEh4cH9u7dqytLTExEREQE/Pz8TBhZ/qRWqzF48GC4u7tj0KBBUKvVpg6JiIgIgIlHMz19+hT//POP7nlMTAxOnTqFQoUKoXjx4hgwYAAmTJiAsmXLomzZspgwYQLs7OzQsWNHE0adf/n7+8Pf39/UYRAREekxaTJz7NgxNGzYUPd80KBBAIDg4GCEhoZi6NChePHiBXr37o2HDx/inXfeQVhYGBwdHU0VMhEREeUyiuTxu57Fx8fD2dkZjx8/Zv8ZojzkxYsXCAwMBADs2bMHtra2Jo6IiLKTMd/fubbPDBEREZEhmMwQERGRWWMyQ0RERGaNyQwRERGZNSYzREREZNaYzBAREZFZYzJDREREZo3JDBEREZk1JjNERERk1pjMEBERkVljMkNERERmjckMERERmTUmM0RERGTWmMwQERGRWWMyQ0RERGaNyQwRERGZNSYzREREZNaYzBAREZFZYzJDREREZo3JDBEREZk1JjNERERk1pjMEBERkVljMkNERERmjckMERERmTUmM0RERGTWmMwQERGRWWMyQ0RERGaNyQwRERGZNStTB0BE5kdEoNFoTBpD6u2bOhYAUKvVUBTF1GEQ5UtMZojIaBqNBoGBgaYOQ6dVq1amDgF79uyBra2tqcMgypd4mYmIiIjMGltmiMhoarUae/bsMWkMIoKEhAQAgEqlMvklHrVabdLtE+VnTGaIyGiKouSKSyp2dnamDoGIcgFeZiIiIiKzxmSGiIiIzBqTGSIiIjJrTGaIiIjIrDGZISIiIrPGZIaIiIjMGpMZIiIiMmtMZoiIiMisMZkhIiIis8ZkhoiIiMwakxkiIiIya0xmiIiIyKwxmSEiIiKzludnzRYRAEB8fLyJIyEiIiJDab+3td/jmcnzycyTJ08AAMWKFTNxJERERGSsJ0+ewNnZOdM6ihiS8pixlJQU3Lp1C46OjlAUxdThmLX4+HgUK1YMN27cgJOTk6nDIeI5SbkOz8nsIyJ48uQJvLy8YGGRea+YPN8yY2FhgaJFi5o6jDzFycmJ/6SUq/CcpNyG52T2yKpFRosdgImIiMisMZkhIiIis8ZkhgymUqkwZswYqFQqU4dCBIDnJOU+PCdNI893ACYiIqK8jS0zREREZNaYzBAREZFZYzJDREREZo3JDBHlaQ0aNMCAAQNMHQblcV27dsXHH39s6jDyLSYzZm7evHmoVq2a7gZNvr6+2LVrl16drl27QlEUvUe9evUyXW92/WOGhoam2bb2cffuXV29M2fOoH79+rC1tUWRIkXw/fffGzQfBxkuNjYW/fv3R5kyZaBWq+Hu7o53330X8+fPx/Pnz3N8+6GhoShQoECOrf/AgQNQFAWPHj3KsW0Yg+d+9sotycLVq1ehKApOnTpl6lB0WrZsieLFi0OtVsPT0xOdO3fGrVu39Oqkdx7Onz/fRBFnvzx/B+C8rmjRopg0aRLKlCkDAFi2bBlatWqFkydPonLlyrp6H374IZYuXap7bmNj81bi++yzz/Dhhx/qlXXt2hUajQaFCxcG8Or23wEBAWjYsCGioqJw6dIldO3aFfb29hg8ePBbiTOvu3LlCvz9/VGgQAFMmDABVatWRVJSEi5duoQlS5bAy8sLLVu2TPe1L1++hLW19VuOOGPJyclQFCXL25ubGs99elsaNmyIb7/9Fp6envjvf/+LIUOGoF27djh8+LBevaVLl+qdk4beXdcsCOU5BQsWlEWLFumeBwcHS6tWrQx+/ZgxYwSA3iM8PFxERE6fPi0NGzYUtVothQoVki+++EKePHli8Lrv3r0r1tbWsnz5cl3Z3LlzxdnZWTQaja5s4sSJ4uXlJSkpKQavmzIWGBgoRYsWladPn6a7PPVxBiDz5s2Tli1bip2dnYwePVpERLZt2ya1atUSlUolJUuWlJCQEHn58qXuddOmTZMqVaqInZ2dFC1aVHr16qU7N8LDw9OcU2PGjBERkYSEBPnmm2/Ey8tL7OzspG7durrzTURk6dKl4uzsLNu3b5eKFSuKpaWlXLlyRS/+mJiYNOsPDg4WEZH69etLv3795JtvvpGCBQuKu7u7btuGxJ46ht27d0uFChXE3t5eAgMD5datWwa/Bzz3/52sPsfOnTsnTZs2FXt7eylcuLB06tRJ7t27p1u+YcMGqVKliu6zq1GjRrr/h/DwcKlTp47Y2dmJs7Oz+Pn5ydWrV9PdzuvnWf369fXimzJlinh4eEihQoWkd+/ekpiYqHvtihUrpHbt2uLg4CDu7u7SoUMHuXPnjm659v9k3759Urt2bbG1tRVfX1+5cOGCUcdq69atoiiK3rYByObNm41ajzlhMpOHJCUlyZo1a8TGxkbOnTunKw8ODhZnZ2dxc3OTsmXLyueff673D/S6J0+eyKeffioffvih3L59W27fvi0JCQny7Nkz8fLykjZt2siZM2dk//79UrJkSd2XhiGmTp0qzs7O8vz5c11Z586dpWXLlnr1Tpw4IQDSfGmR8e7fvy+KosjEiRMNqg9AChcuLIsXL5bLly/L1atXZffu3eLk5CShoaFy+fJlCQsLkxIlSkhISIjudTNmzJDffvtNrly5Ivv375fy5ctLr169RORVwjJz5kxxcnLSnVPaZKFjx47i5+cnv//+u/zzzz8yZcoUUalUcunSJRF5lUhYW1uLn5+fHDp0SC5cuJAmKUtKSpKNGzcKALl48aLcvn1bHj16JCKvkhknJycJCQmRS5cuybJly0RRFAkLCzMo9tQxNG7cWKKiouT48eNSsWJF6dixo8HvA8/9fyezZObWrVvi6uoqI0aMkPPnz8uJEyckICBAGjZsqFtuZWUl06dPl5iYGDl9+rTMmTNHnjx5Ii9fvhRnZ2cZMmSI/PPPPxIdHS2hoaFy7dq1dLd19OhRXcJx+/ZtiYuL08Xn5OQkPXv2lPPnz8v27dvFzs5OFixYoHvt4sWL5ddff5XLly9LZGSk1KtXT5o2bapbrk1m3nnnHTlw4ICcO3dO3nvvPfHz8zP4OMXFxcmnn34q/v7+euUApEiRIuLi4iI+Pj4yb948SU5ONni9uR2TmTzg9OnTYm9vL5aWluLs7Cw7d+7UW7527VrZsWOHnDlzRrZt2ybVq1eXypUr6/0afF16HxwLFiyQggUL6n2R7Ny5UywsLCQ2NtagWCtVqqT3JSEiEhAQIF988YVe2X//+18BIIcPHzZovZSxI0eOCADZtGmTXrmLi4vY29uLvb29DB06VFcOQAYMGKBX97333pMJEybola1YsUI8PT0z3O769evFxcVF91zbupHaP//8I4qiyH//+1+98kaNGsmIESN0rwMgp06dynQ/tV8EDx8+1CuvX7++vPvuu3plderUkWHDhhkVOwD5559/dGVz5swRd3f3TGNKjef+v5NZMjNq1Chp0qSJXtmNGzd0ye3x48cFQLqtLXFxcQJADhw4YFAc2lbAkydPponP29tbkpKSdGWffPKJfPbZZxmuS5sYvd6CuW/fPl2dnTt3CgB58eJFpnENHTpU7OzsBIDUq1dP7t+/r7d83LhxcvjwYTl58qRMnTpV7OzsZNy4cQbtszlgn5k8oHz58jh16hQePXqEjRs3Ijg4GBEREahUqRKAV9futapUqQIfHx94e3tj586daNOmjcHbOX/+PKpXrw57e3tdmb+/P1JSUnDx4kW4u7tn+vrIyEhER0dj+fLlaZYpiqL3XP6/A+Tr5fTmXj+WR48eRUpKCoKCgpCQkKC3zMfHR+/58ePHERUVhR9++EFXlpycDI1Gg+fPn8POzg7h4eGYMGECoqOjER8fj6SkJGg0Gjx79kzvnEntxIkTEBGUK1dOrzwhIQEuLi665zY2NqhWrdob7TeANK/19PTU64RrSOx2dnYoXbp0huvIDM/9nHX8+HGEh4fDwcEhzbLLly+jSZMmaNSoEapWrYrAwEA0adIE7dq1Q8GCBVGoUCF07doVgYGBCAgIQOPGjfHpp5/C09PT6DgqV64MS0tL3XNPT0+cOXNG9/zkyZMICQnBqVOn8ODBA6SkpAAArl+/rvu8BvTPV20cd+/eRfHixTPc9jfffIMePXrg2rVrGDt2LLp06YIdO3bozqPvvvtOV7dGjRoAgO+//16v3JwxmckDbGxsdB2AfXx8EBUVhZ9++gk///xzuvU9PT3h7e2Nv//+26jtiEiGH7CGfPAuWrQINWrUQO3atfXKPTw8EBsbq1em/ZLIKkGirJUpUwaKouDChQt65aVKlQIA2NrapnnN68lHSkoKxo4dm27yq1arce3aNTRr1gw9e/bEuHHjUKhQIRw8eBA9evTAy5cvM4wtJSUFlpaWOH78uN6XAAC9LyZbW9t/9eX+egdmRVF0XySGxp7eOsTAUUc893NWSkoKWrRogR9//DHNMk9PT1haWmLv3r04fPgwwsLCMGvWLIwcORJ//vknSpYsiaVLl+Lrr7/G7t27sW7dOnz33XfYu3dvlqM+X5fZefbs2TM0adIETZo0wcqVK+Hm5obr168jMDAQiYmJGa5He95r15MRV1dXuLq6oly5cqhYsSKKFSuGI0eOwNfXN9369erVQ3x8PO7cuZMnzrXcPRyA3oiIpPmlnVpcXBxu3LiR6S8PGxsbJCcn65VVqlQJp06dwrNnz3Rlhw4dgoWFRZpf1q97+vQp1q9fjx49eqRZ5uvri99//13vHzosLAxeXl4oUaJEpuulrLm4uCAgIACzZ8/We++MUatWLVy8eBFlypRJ87CwsMCxY8eQlJSEadOmoV69eihXrlyaoaHpnVM1a9ZEcnIy7t69m2a9Hh4eRsWoHaH3+jayYkjs/wbP/ZxXq1YtnDt3DiVKlEhzHmkTc0VR4O/vj7Fjx+LkyZOwsbHB5s2bdeuoWbMmRowYgcOHD6NKlSpYvXp1utt60/PswoULuH//PiZNmoT33nsPFSpUMLhlz1jaJDuz74GTJ09CrVbn6O0S3iYmM2bu22+/xR9//IGrV6/izJkzGDlyJA4cOICgoCAArz5IhwwZgsjISFy9ehUHDhxAixYt4OrqitatW2e43hIlSuD06dO4ePEi7t+/j5cvXyIoKAhqtRrBwcE4e/YswsPD0a9fP3Tu3DnLzH7dunVISkrSxZVax44doVKp0LVrV5w9exabN2/GhAkTMGjQIDa1Z5O5c+ciKSkJPj4+WLduHc6fP4+LFy9i5cqVuHDhQppWkdeNHj0ay5cvR0hICM6dO4fz58/rfsECQOnSpZGUlIRZs2bhypUrWLFiRZp7WJQoUQJPnz7F/v37cf/+fTx//hzlypVDUFAQunTpgk2bNiEmJgZRUVH48ccf8euvvxq1j97e3lAUBTt27MC9e/fw9OlTg15nSOz/Bs/97PP48WOcOnVK73H9+nX06dMHDx48QIcOHXD06FFcuXIFYWFh6N69O5KTk/Hnn39iwoQJOHbsGK5fv45Nmzbh3r17qFixImJiYjBixAhERkbi2rVrCAsLw6VLl1CxYsV0YyhcuDBsbW2xe/du3LlzB48fPzYo9uLFi8PGxkZ3nm3btg3jxo3718fk6NGjmD17Nk6dOoVr164hPDwcHTt2ROnSpXWtMtu3b8fChQtx9uxZXL58GYsWLcLIkSPx5Zdf5p3ZvU3YX4eyQffu3cXb21tsbGzEzc1NGjVqpDdK4/nz59KkSRNxc3MTa2trKV68uAQHB8v169czXe/du3clICBAHBwcsmVotq+vb6YjP06fPi3vvfeeqFQq8fDwkJCQEA5NzWa3bt2Svn37SsmSJcXa2locHBykbt26MmXKFHn27JmuHjIYwrl7927x8/MTW1tbcXJykrp16+qN1Jg+fbp4enqKra2tBAYGyvLly9N0yO3Zs6e4uLjoDc1OTEyU0aNHS4kSJcTa2lo8PDykdevWcvr0aRFJv+NwRr7//nvx8PAQRVH0hmb3799fr16rVq30RuFlFXt6MWzevFkM+QjluZ89goOD0wyLRqoh+JcuXZLWrVtLgQIFxNbWVipUqCADBgyQlJQUiY6OlsDAQHFzcxOVSiXlypWTWbNmiYhIbGysfPzxx+Lp6Sk2Njbi7e0to0ePznSkz8KFC6VYsWJiYWGRZmh2av3799ctFxFZvXq1lChRQlQqlfj6+sq2bdv0OhOn14n95MmTAkBiYmLSjUX7mVyoUCFRqVRSokQJ6dmzp9y8eVNXZ9euXVKjRg1xcHAQOzs7qVKlisycOVPv1grmThHhrSaJiIjIfPEyExEREZk1JjNERERk1pjMEBERkVljMkNERERmjckMERERmTUmM0RERGTWmMwQERGRWWMyQ0RERGaNyQwRZYvY2FgEBATA3t4+z8z3klrnzp0xYcIEU4eRoR07dqBmzZpZTkhIlBcxmSEyM4cPH4alpSU+/PBDU4eiZ8aMGbh9+zZOnTqFS5cupVsnJCQENWrUSFN+9epVKIqCU6dO5WyQb+j06dPYuXMn+vXrh4SEBFSuXBlffvllmnpDhw6Ft7c34uPj33qMH330ERRFyXCCRKK8jMkMkZlZsmQJ+vXrh4MHD+L69eumDkfn8uXLqF27NsqWLYvChQubOpxsNXv2bHzyySdwdHSESqXC8uXLERoait27d+vqHDlyBDNmzEBoaCicnJyydfsigqSkpCzrdevWDbNmzcrWbROZAyYzRGbk2bNnWL9+PXr16oWPPvoIoaGhaeps27YNZcuWha2tLRo2bIhly5ZBURQ8evRIV+fw4cN4//33YWtri2LFiuHrr7/Gs2fPMt32vHnzULp0adjY2KB8+fJYsWKFblmJEiWwceNGLF++HIqioGvXrv96XyMiIlC3bl2oVCp4enpi+PDhel/oDRo0QL9+/TBgwAAULFgQ7u7uWLBgAZ49e4Zu3brB0dERpUuXxq5du/TWGx0djWbNmsHBwQHu7u7o3Lkz7t+/n2EcKSkp2LBhA1q2bKkrq127NkaOHInPP/8cjx49gkajQbdu3dCnTx80bNgwy+O7cuVK+Pj4wNHRER4eHujYsSPu3r2rW37gwAEoioI9e/bAx8cHKpUKf/zxB/766y80bNgQjo6OcHJyQu3atXHs2DHd61q2bKmbNZooXzHxRJdEZITFixeLj4+PiIhs375dSpQooTfDckxMjFhbW8uQIUPkwoULsmbNGilSpIjeTLynT58WBwcHmTFjhly6dEkOHTokNWvWlK5du2a43U2bNom1tbXMmTNHLl68KNOmTRNLS0v57bffROTVLOsffvihfPrpp3L79m159OhRuusZM2aMVK9ePU15TEyM3uzBN2/eFDs7O+ndu7ecP39eNm/eLK6urrqZtkVezYbt6Ogo48aNk0uXLsm4cePEwsJCmjZtKgsWLJBLly5Jr169xMXFRTcr+K1bt8TV1VVGjBgh58+flxMnTkhAQIA0bNgww33XzlocGxurV/7y5Uvx8fGRzp07y+DBg6VChQry/Plzg47v4sWL5ddff5XLly9LZGSk1KtXT5o2bapbrp09uVq1ahIWFib//POP3L9/XypXriydOnWS8+fPy6VLl2T9+vVy6tQpvbgKFy4soaGhGe4PUV7EZIbIjPj5+cnMmTNF5NWXqaurq+zdu1e3fNiwYVKlShW914wcOVIvmencubN8+eWXenX++OMPsbCwkBcvXmS43S+++EKv7JNPPpFmzZrpnrdq1UqCg4MzjX/MmDFiYWEh9vb2eg87Ozu9ZObbb7+V8uXL6yVqc+bMEQcHB0lOThaRV8nMu+++q1uelJQk9vb20rlzZ13Z7du3BYBERkaKiMioUaOkSZMmejHduHFDAMjFixfTjXnz5s1iaWmpF4tWdHS0qNVqsbGxkaNHj4rImx3fo0ePCgB58uSJiPwvmdmyZYtePUdHxywTlZo1a0pISEimdYjyGl5mIjITFy9exNGjR9G+fXsAgJWVFT777DMsWbJEr06dOnX0Xle3bl2958ePH0doaCgcHBx0j8DAQKSkpCAmJibdbZ8/fx7+/v56Zf7+/jh//rzR+1G+fHmcOnVK7/Hrr7+m2Z6vry8URdHb3tOnT3Hz5k1dWbVq1XR/W1pawsXFBVWrVtWVubu7A4DuEs7x48cRHh6ut+8VKlQA8KrPT3pevHgBlUqlF4tWxYoV0bZtWwQEBOiOuyHH9+TJk2jVqhW8vb3h6OiIBg0aAECaPlA+Pj56zwcNGoTPP/8cjRs3xqRJk9KN2dbWFs+fP093X4jyKitTB0BEhlm8eDGSkpJQpEgRXZmIwNraGg8fPkTBggUhImm+dEVE73lKSgq++uorfP3112m2Ubx48Qy3n9560/uCz4qNjQ3KlCmjV2Zlpf9RlNl+pC63trZOE2PqMm1d7XDllJQUtGjRAj/++GOauDw9PdON19XVFc+fP0diYiJsbGzSLLeystKLP6vj++zZMzRp0gRNmjTBypUr4ebmhuvXryMwMBCJiYl69e3t7fWeh4SEoGPHjti5cyd27dqFMWPGYO3atWjdurWuzoMHD+Dm5pbuvhDlVUxmiMxAUlISli9fjmnTpqFJkyZ6y9q2bYtVq1ahb9++qFChQppWjtQdRAGgVq1aOHfuXJqEIjMVK1bEwYMH0aVLF13Z4cOHUbFixTfYm6xVqlQJGzdu1EtqDh8+DEdHR71kzli1atXCxo0bUaJEiTQJVEa0Q8mjo6PTHVae3jYyO75nzpzB/fv3MWnSJBQrVgxA2vcoM+XKlUO5cuUwcOBAdOjQAUuXLtUlMxqNBpcvX0bNmjUNXh9RXsDLTERmYMeOHXj48CF69OiBKlWq6D3atWuHxYsXAwC++uorXLhwAcOGDcOlS5ewfv163YgnbVIwbNgwREZGok+fPjh16hT+/vtvbNu2Df369ctw+9988w1CQ0Mxf/58/P3335g+fTo2bdqEIUOG5Mj+9u7dGzdu3EC/fv1w4cIFbN26FWPGjMGgQYNgYfHmH1t9+vTBgwcP0KFDB92on7CwMHTv3h3JycnpvsbNzQ21atXCwYMHDdpGVse3ePHisLGxwaxZs3DlyhVs27YN48aNy3K9L168QN++fXHgwAFcu3YNhw4dQlRUlF5CeeTIEahUKvj6+hoUK1FewWSGyAwsXrwYjRs3hrOzc5plbdu2xalTp3DixAmULFkSv/zyCzZt2oRq1aph3rx5GDlyJABApVIBeNXPJCIiAn///Tfee+891KxZE6NGjcrwMgsAfPzxx/jpp58wZcoUVK5cGT///DOWLl2q6+uR3YoUKYJff/0VR48eRfXq1dGzZ0/06NED33333b9ar5eXFw4dOoTk5GQEBgaiSpUq6N+/P5ydnTNNkr788kusWrXKoG1kdXzd3NwQGhqKDRs2oFKlSpg0aRKmTp2a5XotLS0RFxeHLl26oFy5cvj000/RtGlTjB07VldnzZo1CAoKgp2dnUGxEuUVirx+QZ2I8pQffvgB8+fPx40bN0wditnSaDQoX7481q5dm2tbPe7du4cKFSrg2LFjKFmypKnDIXqr2GeGKI+ZO3cu6tSpAxcXFxw6dAhTpkxB3759TR2WWVOr1Vi+fHmmN9cztZiYGMydO5eJDOVLbJkhymMGDhyIdevW4cGDByhevDg6d+6MESNGGNzhlYjI3DCZISIiIrPGDsBERERk1pjMEBERkVljMkNERERmjckMERERmTUmM0RERGTWmMwQERGRWWMyQ0RERGaNyQwRERGZtf8D1uhGayu+py8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create plot\n",
    "plot3 = sns.boxplot(x=\"age_group\", y=\"MEDV\", data=boston_df)\n",
    "plot3.set(xlabel=\"Age of Home (Years)\", ylabel=\"Median Home Value ($ks)\",\n",
    "          title='Median Home Value of Owner-Occupied Homes \\nvs. Age of Home')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0578a109",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35 to 70</th>\n",
       "      <td>128.0</td>\n",
       "      <td>24.947656</td>\n",
       "      <td>6.969370</td>\n",
       "      <td>10.2</td>\n",
       "      <td>20.675</td>\n",
       "      <td>22.6</td>\n",
       "      <td>27.425</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greater than 70</th>\n",
       "      <td>287.0</td>\n",
       "      <td>19.793380</td>\n",
       "      <td>9.515375</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.800</td>\n",
       "      <td>18.2</td>\n",
       "      <td>22.550</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Less than 35</th>\n",
       "      <td>91.0</td>\n",
       "      <td>27.775824</td>\n",
       "      <td>7.638198</td>\n",
       "      <td>17.1</td>\n",
       "      <td>23.050</td>\n",
       "      <td>24.8</td>\n",
       "      <td>31.150</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count       mean       std   min     25%   50%     75%   max\n",
       "age_group                                                                    \n",
       "35 to 70         128.0  24.947656  6.969370  10.2  20.675  22.6  27.425  50.0\n",
       "Greater than 70  287.0  19.793380  9.515375   5.0  13.800  18.2  22.550  50.0\n",
       "Less than 35      91.0  27.775824  7.638198  17.1  23.050  24.8  31.150  50.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View relevant statistics\n",
    "age_groups = list(boston_df['age_group'].unique())\n",
    "boston_df.groupby('age_group').agg('MEDV').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0efbad3",
   "metadata": {},
   "source": [
    "##### <span style=\"color:blue\">Explanation of findings:</span>\n",
    "<span style=\"color:blue\">The age variable does appear to have an effect on the median home value in a given tract.  We can see that as the housing tract gets older, its average of the median home values decreases, as does the median (of the median home values).  It is no surprise that older homes/housing tracts are not worth quite as much.  The quarter ranges start and end higher in an absolute sense the younger the age group, but the variance and IQR are tighter for the middle age group compared to the youngest and oldest groups.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "285dcfff",
   "metadata": {},
   "source": [
    "### 4. Scatter plot - Proportion of non-retail business acres per town vs. Nitric Oxide Concentrations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f6b6d253",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create plot\n",
    "plot4 = sns.scatterplot(x='NOX', y='INDUS', data=boston_df)\n",
    "plot4.set(ylabel=\"PoNRBApT\", xlabel=\"NOX Concentrations (pp10m)\",\n",
    "          title='Proportion of Non-Retail Business Acres per Town (PoNRBApT) \\nvs. Nitric Oxide Concentrations')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18f1d19d",
   "metadata": {},
   "source": [
    "##### <span style=\"color:blue\">Explanation of findings:</span>\n",
    "<span style=\"color:blue\">The Proportion of Non-Retail Business Acres per Town (PoNRBApT) does appears to be positively (and roughly linearly) correlated with NOX concentrations.  That is, the higher the NOX concentartion, the higher the proportion of the town that is non-retail business acres. If any causation exists, it is likely not captured by this graph.  I would suspect that NOX conentrations are in fact a product of increases non-retail business activities; in other words, they are an indicator and not a culprit.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee03710f",
   "metadata": {},
   "source": [
    "### 5. Histogram of Pupil-to-teacher Ratio variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "82ac87ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create plot\n",
    "plot5 = sns.displot(boston_df['PTRATIO'], kde = False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4e7121d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    506.000000\n",
       "mean      18.455534\n",
       "std        2.164946\n",
       "min       12.600000\n",
       "25%       17.400000\n",
       "50%       19.050000\n",
       "75%       20.200000\n",
       "max       22.000000\n",
       "Name: PTRATIO, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review relevant statistics\n",
    "boston_df['PTRATIO'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b55c6966",
   "metadata": {},
   "source": [
    "##### <span style=\"color:blue\">Explanation of findings:</span>\n",
    "<span style=\"color:blue\">The histogram shows a distribution that is slightly skewed left, with a median at 19 (19:1, pupils to teacher).  The mean is closer to 18.  The mode is clearly at about; that is the most common ratio.  Relatively few tracts had a ratio above the median, with the 75% mark landing at roughly 20.  </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbf39cac",
   "metadata": {},
   "source": [
    "# Task 5 - Hypothesis Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "638b927b",
   "metadata": {},
   "source": [
    "**QUESTION \\#1**: Is there a significant difference in median value of houses bounded by the Charles river or not? (T-test for independent samples)\n",
    "\n",
    "**HYPOTHESIS**: $H\\_0: μ\\_bounded = μ\\_unbounded$ (The average of the median home value for homes bounded by the Charles River is not different than the average median home value for homes not bounded by the Charles River)\n",
    "\n",
    "**ALTERNATIVE HYPOTHESIS**: $H\\_A: μ\\_bounded$ is not equal to $μ\\_unbounded$ (The average of the median home value for homes bounded by the Charles River is different than the average median home value for homes not bounded by the Charles River)\n",
    "\n",
    "**SIGNIFICANCE LEVEL**: α = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a9bab317",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeveneResult(statistic=8.75190489604598, pvalue=0.003238119367639829)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check significance of variance differences\n",
    "scipy.stats.levene(boston_df[boston_df['CHAS'] == 1]['MEDV'],\n",
    "                   boston_df[boston_df['CHAS'] == 0]['MEDV'], center='mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f05262a",
   "metadata": {},
   "source": [
    "<span style=\"color:blue\">Since the p-value is less than 0.05, the variance are not equal, and we will factor this into our t-test</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f9fa4152",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=3.113291312794837, pvalue=0.003567170098137517)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform T Test\n",
    "scipy.stats.ttest_ind(boston_df[boston_df['CHAS'] == 1]['MEDV'],\n",
    "                      boston_df[boston_df['CHAS'] == 0]['MEDV'], equal_var=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f1d8d80",
   "metadata": {},
   "source": [
    "#### <span style=\"color:blue\">Conclusion to Question 1</span>\n",
    "<span style=\"color:blue\">Since the p-value is less than alpha value 0.05, we reject the null hypothesis as there is enough evidence that there is a statistical difference in median home values depending on whether or not the housing tract borders the Charles River.  The answer to the basic question posed above is **YES, THERE IS REASON TO SUSPECT A SIGNIFICANT DIFFERENCE IN MEDIAN HOME PRICE WHEN BOUNDED OR NOT BY THE CHARLES RIVER**</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba11d01b",
   "metadata": {},
   "source": [
    "**QUESTION \\#2**: Is there a difference in Median values of houses (MEDV) for each proportion of owner occupied units built prior to 1940 (AGE)? (ANOVA)\n",
    "\n",
    "**HYPOTHESIS**: $H\\_0: The averages of the median home values, when grouped by AGE, are not signficantly different from one enother.\n",
    "\n",
    "**ALTERNATIVE HYPOTHESIS**: $H\\_A: The averages of the median home values, when grouped by AGE, are signficantly different from one enother.\n",
    "\n",
    "**SIGNIFICANCE LEVEL**: α = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "74eeb264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeveneResult(statistic=2.7806200293748304, pvalue=0.06295337343259205)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check significance of variance differences\n",
    "scipy.stats.levene(boston_df[boston_df['age_group'] == 'Less than 35']['MEDV'],\n",
    "                   boston_df[boston_df['age_group'] == '35 to 70']['MEDV'], \n",
    "                   boston_df[boston_df['age_group'] == 'Greater than 70']['MEDV'], \n",
    "                   center='mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80438e5b",
   "metadata": {},
   "source": [
    "<span style=\"color:blue\">Since the p-value is greater than 0.05 we can assume equality of variance</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9ff87dad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new dataframes focused on age group and median home value\n",
    "thirtyfive_lower = boston_df[boston_df['age_group'] == 'Less than 35']['MEDV']\n",
    "thrirtyfive_seventy = boston_df[boston_df['age_group'] == '35 to 70']['MEDV']\n",
    "seventy_older = boston_df[boston_df['age_group'] == 'Greater than 70']['MEDV']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a87d264e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F_Statistic: 36.40764999196599, P-Value: 1.7105011022702984e-15\n"
     ]
    }
   ],
   "source": [
    "#Perform ANOVA tests\n",
    "f_statistic, p_value = scipy.stats.f_oneway(thirtyfive_lower, thrirtyfive_seventy, seventy_older)\n",
    "print(\"F_Statistic: {0}, P-Value: {1}\".format(f_statistic,p_value))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d04a1d49",
   "metadata": {},
   "source": [
    "#### <span style=\"color:blue\">Conclusion to Question 2</span>\n",
    "<span style=\"color:blue\">Since the p-value is less than 0.05, we will reject the null hypothesis as there is significant evidence that at least one of the means for the median home values when grouped by their age differ.   The answer to the question is **YES, THERE IS REASON TO SUSPECT THAT THE 'AGE' CATEGORY GROUPS HAVE SIGNIFICANTLY DIFFERENT MEDIAN HOME VALUES.**</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dd7ea67",
   "metadata": {},
   "source": [
    "**QUESTION \\#3**: Can we conclude that there is no relationship between Nitric oxide concentrations and proportion of non-retail business acres per town? (Pearson Correlation)\n",
    "\n",
    "**HYPOTHESIS**: $H\\_0:$ Nitric Oxide Concentrations are not correlated with proportion of non-retail business acres per town\n",
    "\n",
    "**ALTERNATIVE HYPOTHESIS**:$H\\_A:$ Nitric Oxide Concentrations are correlated with proportion of non-retail business acres per town\n",
    "\n",
    "**SIGNIFICANCE LEVEL**: α = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5f6ae0bd",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'INDUs'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3801\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'INDUs'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[19], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Run Pearson Test\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m scipy\u001b[38;5;241m.\u001b[39mstats\u001b[38;5;241m.\u001b[39mpearsonr(boston_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNOX\u001b[39m\u001b[38;5;124m'\u001b[39m], boston_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mINDUs\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3805\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   3806\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3807\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[0;32m   3808\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   3809\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3804\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3805\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3806\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3808\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3809\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'INDUs'"
     ]
    }
   ],
   "source": [
    "# Run Pearson Test\n",
    "scipy.stats.pearsonr(boston_df['NOX'], boston_df['INDUs'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4061ead",
   "metadata": {},
   "source": [
    "#### <span style=\"color:blue\">Conclusion to Question 3</span>\n",
    "<span style=\"color:blue\">The p-value is extremely small; this gives us reason to reject the null hypothesis that these two values are not correlated.  The answer to the core question is **NO, THERE IS REASON TO SUSPECT THAT THE NITROUS OXIDE CONCENTRATIONS ARE CORRELATED WITH THE PROPORTION OF NON-RETAIL BUSINESS ACRES PER TOWN**.</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44c3547d",
   "metadata": {},
   "source": [
    "**QUESTION \\#4**: What is the impact of an additional weighted distance to the five Boston employment centres on the median value of owner occupied homes? (Regression analysis)\n",
    "\n",
    "**HYPOTHESIS**: $H\\_0: β1$ = 0 (The weighted distance to the five Boston employment centres has no impact on the median value of owner occupied homes).\n",
    "\n",
    "**ALTERNATIVE HYPOTHESIS**: $H\\_A: β1$ is not equal to 0 (The weighted distance to the five Boston employment centres has a significant impact on the median value of owner occupied homes).\n",
    "\n",
    "**SIGNIFICANCE LEVEL**: α = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6bf4b5a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>MEDV</td>       <th>  R-squared:         </th> <td>   0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.061</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   33.58</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 06 Dec 2023</td> <th>  Prob (F-statistic):</th> <td>1.21e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:06:47</td>     <th>  Log-Likelihood:    </th> <td> -1823.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3652.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   504</td>      <th>  BIC:               </th> <td>   3660.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   18.3901</td> <td>    0.817</td> <td>   22.499</td> <td> 0.000</td> <td>   16.784</td> <td>   19.996</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DIS</th>   <td>    1.0916</td> <td>    0.188</td> <td>    5.795</td> <td> 0.000</td> <td>    0.722</td> <td>    1.462</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>139.779</td> <th>  Durbin-Watson:     </th> <td>   0.570</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 305.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.466</td>  <th>  Prob(JB):          </th> <td>5.59e-67</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.424</td>  <th>  Cond. No.          </th> <td>    9.32</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &       MEDV       & \\textbf{  R-squared:         } &     0.062   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.061   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     33.58   \\\\\n",
       "\\textbf{Date:}             & Wed, 06 Dec 2023 & \\textbf{  Prob (F-statistic):} &  1.21e-08   \\\\\n",
       "\\textbf{Time:}             &     15:06:47     & \\textbf{  Log-Likelihood:    } &   -1823.9   \\\\\n",
       "\\textbf{No. Observations:} &         506      & \\textbf{  AIC:               } &     3652.   \\\\\n",
       "\\textbf{Df Residuals:}     &         504      & \\textbf{  BIC:               } &     3660.   \\\\\n",
       "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const} &      18.3901  &        0.817     &    22.499  &         0.000        &       16.784    &       19.996     \\\\\n",
       "\\textbf{DIS}   &       1.0916  &        0.188     &     5.795  &         0.000        &        0.722    &        1.462     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 139.779 & \\textbf{  Durbin-Watson:     } &    0.570  \\\\\n",
       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } &  305.104  \\\\\n",
       "\\textbf{Skew:}          &   1.466 & \\textbf{  Prob(JB):          } & 5.59e-67  \\\\\n",
       "\\textbf{Kurtosis:}      &   5.424 & \\textbf{  Cond. No.          } &     9.32  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   MEDV   R-squared:                       0.062\n",
       "Model:                            OLS   Adj. R-squared:                  0.061\n",
       "Method:                 Least Squares   F-statistic:                     33.58\n",
       "Date:                Wed, 06 Dec 2023   Prob (F-statistic):           1.21e-08\n",
       "Time:                        15:06:47   Log-Likelihood:                -1823.9\n",
       "No. Observations:                 506   AIC:                             3652.\n",
       "Df Residuals:                     504   BIC:                             3660.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         18.3901      0.817     22.499      0.000      16.784      19.996\n",
       "DIS            1.0916      0.188      5.795      0.000       0.722       1.462\n",
       "==============================================================================\n",
       "Omnibus:                      139.779   Durbin-Watson:                   0.570\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              305.104\n",
       "Skew:                           1.466   Prob(JB):                     5.59e-67\n",
       "Kurtosis:                       5.424   Cond. No.                         9.32\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup and run Regression Analysis\n",
    "X = boston_df['DIS']\n",
    "y = boston_df['MEDV']\n",
    "X = sm.add_constant(X) \n",
    "model = sm.OLS(y, X).fit()\n",
    "predictions = model.predict(X)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f27cf96",
   "metadata": {},
   "source": [
    "#### <span style=\"color:blue\">Conclusion to Question 4</span>\n",
    "<span style=\"color:blue\">The coefficient related to the DIS independent variable is 1.0916.  The p-value associated with this value is very small, so there is reason to reject the null hypothesis that the weighted distance to the five main Boston employment centers has no impact on the median home value.  It also indicates-- and this answers the question-- that **FOR EVERY UNIT INCREASE IN DIS, THE MEDIAN HOME VALUE INCREASES BY NEARLY \\$1,000**.  The correlation coefficient of roughly 0.24 also indicates that these two values are correlated.</span>"
   ]
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