-Optional--Peer-Graded-Assignment-jupyterlite-v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\">\n",
    "</center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import the required libraries we need for the lab.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as pyplot\n",
    "import scipy.stats\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read the dataset in the csv file from the URL\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston_df=pd.read_csv('boston_housing_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add your code below following the instructions given in the course to complete the peer graded assignment\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1: Become familiar with the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following describes the dataset variables:\r\n",
    "\r\n",
    "- CRIM: per capita crime rate by town\r\n",
    "\r\n",
    "- ZN: proportion of residential land zoned for lots over 25,000 sq.ft.\r\n",
    "\r\n",
    "- INDUS: proportion of non-retail business acres per town.\r\n",
    "\r\n",
    "- CHAS: Charles River dummy variable (1 if tract bounds river; 0 otherwise)\r\n",
    "\r\n",
    "- NOX: nitric oxides concentration (parts per 10 million)\r\n",
    "\r\n",
    "- RM: average number of rooms per dwelling\r\n",
    "\r\n",
    "- AGE: proportion of owner-occupied units built prior to 1940\r\n",
    "\r\n",
    "- DIS: weighted distances to five Boston employment centres\r\n",
    "\r\n",
    "- RAD: index of accessibility to radial highways\r\n",
    "\r\n",
    "- TAX: full-value property-tax rate per $10,000\r\n",
    "\r\n",
    "- PTRATIO: pupil-teacher ratio by town\r\n",
    "\r\n",
    "- LSTAT: % lower status of the population\r\n",
    "\r\n",
    "- MEDV: Median value of owner-occupied homes in $1000's\r\n",
    "occupied homes in $1000's\r\n",
    "-occupied homes in $1000's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <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",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <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>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        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",
       "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
       "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  MEDV  \n",
       "0       15.3  396.90   4.98  24.0  \n",
       "1       17.8  396.90   9.14  21.6  \n",
       "2       17.8  392.83   4.03  34.7  \n",
       "3       18.7  394.63   2.94  33.4  \n",
       "4       18.7  396.90   5.33  36.2  \n",
       "..       ...     ...    ...   ...  \n",
       "501     21.0  391.99   9.67  22.4  \n",
       "502     21.0  396.90   9.08  20.6  \n",
       "503     21.0  396.90   5.64  23.9  \n",
       "504     21.0  393.45   6.48  22.0  \n",
       "505     21.0  396.90   7.88  11.9  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
       "       'PTRATIO', 'B', 'LSTAT', 'MEDV'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 14)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677083</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT        MEDV  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- For the \"Median value of owner-occupied homes\" provide a boxplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2: Generate Descriptive Statistics and Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'\\n    EXPLANATION:\\n\\n    \\n    This boxplot show 50% of the data, which is the height of the box.\\n    The line inside the box is the median value of home price\\n    The whiskers (T shaped lines) represent the minimum and maximum values\\n    Points outside the whiskers are outliers\\n\\n'"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax = sns.boxplot(y='MEDV', data=boston_df)\n",
    "pyplot.title('Boxplot of Median value of owner-occupied homes')\n",
    "pyplot.ylabel('Median value of owner-occupied homes in $1000\\'s')\n",
    "pyplot.show()\n",
    "\n",
    "\"\"\"\n",
    "    EXPLANATION:\n",
    "\n",
    "    \n",
    "    This boxplot show 50% of the data, which is the height of the box.\n",
    "    The line inside the box is the median value of home price\n",
    "    The whiskers (T shaped lines) represent the minimum and maximum values\n",
    "    Points outside the whiskers are outliers\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Provide a  bar plot for the Charles river variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "chas_count = boston_df['CHAS'].value_counts().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\"\\n    EXPLANATION:\\n\\n    The bar plot shows the number of homes that are near the Charles river (CHAS = 1) and those that are not (CHAS=0)\\n    Each variable was shown seperately\\n    The heights of the bars indicates the count of homes\\n\\n    As we can see here, most towns aren't near the Charles river as the bar height for value 0 is much higher compared to value 1\\n\\n\""
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax = sns.barplot(x='CHAS', y='count', data=chas_count)\n",
    "pyplot.title('Bar plot of the Charles river variable')\n",
    "pyplot.ylabel('Charles River dummy variable')\n",
    "pyplot.show()\n",
    "\n",
    "\"\"\"\n",
    "    EXPLANATION:\n",
    "\n",
    "    The bar plot shows the number of homes that are near the Charles river (CHAS = 1) and those that are not (CHAS=0)\n",
    "    Each variable was shown seperately\n",
    "    The heights of the bars indicates the count of homes\n",
    "\n",
    "    As we can see here, most towns aren't near the Charles river as the bar height for value 0 is much higher compared to value 1\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Provide a boxplot for the MEDV variable vs the AGE variable. (Discretize the age variable into three groups of 35 years and younger, between 35 and 70 years and 70 years and older)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'\\n    EXPLANATION:\\n    \\n    This boxplot shows the median value of owner-occupied homes (MEDV) in $1000s, \\ncategorized by three age groups: “35 and younger,” “35 to 70,” and “70 and older.\\n\\n    This plot helps compare home values across different age groups, showing trends and variability.\\n    \\n    For instance, it appears that the median home value tends to decrease with increasing age group,\\nand there are more high-value outliers among younger homeowners.\\n\\n'"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Discretize the 'AGE' column\n",
    "\n",
    "boston_df['Age_Group'] = pd.cut(boston_df['AGE'], bins=[0, 35, 70, float('inf')], labels=['35 and younger', '35 to 70', '70 and older'])\n",
    "\n",
    "ax = sns.boxplot(x='Age_Group', y='MEDV', data=boston_df)\n",
    "pyplot.ylabel(\"Median value of owner-occupied homes in $1000\\'s\")\n",
    "pyplot.title('Boxplot of MEDV by AGE Group')\n",
    "pyplot.show()\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "    EXPLANATION:\n",
    "    \n",
    "    This boxplot shows the median value of owner-occupied homes (MEDV) in $1000s, \n",
    "categorized by three age groups: “35 and younger,” “35 to 70,” and “70 and older.\n",
    "\n",
    "    This plot helps compare home values across different age groups, showing trends and variability.\n",
    "    \n",
    "    For instance, it appears that the median home value tends to decrease with increasing age group,\n",
    "and there are more high-value outliers among younger homeowners.\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Provide a scatter plot to show the relationship between Nitric oxide concentrations and the proportion of non-retail business acres per town. What can you say about the relationship?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'\\n    EXPLANATION:\\n    \\n    This scatter plot illustrates the relationship between nitric oxide concentrations\\nand the proportion of non-retail business acres per town\\n\\n    This plot helps visualize how environmental factors like nitric oxide levels\\nmight be associated with urban development patterns.\\n    \\n'"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax = sns.scatterplot(x='NOX', y='INDUS', data=boston_df)\n",
    "pyplot.title('Relationship between Nitric oxide concentrations and the proportion of non-retail business acres per town')\n",
    "pyplot.xlabel('Nitric oxide concentrations')\n",
    "pyplot.ylabel('proportion of non-retail business acres per town')\n",
    "pyplot.show()\n",
    "\n",
    "\"\"\"\n",
    "    EXPLANATION:\n",
    "    \n",
    "    This scatter plot illustrates the relationship between nitric oxide concentrations\n",
    "and the proportion of non-retail business acres per town\n",
    "\n",
    "    This plot helps visualize how environmental factors like nitric oxide levels\n",
    "might be associated with urban development patterns.\n",
    "    \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "      Looking at the picture, we can see that these values has a linear relationship as the concentration of Nitric oxide increases, the proportion of non-retail business acres also tends to increase. This means that towns with higher industrial activity, indicated by more non-retail business acres, tend to have higher levels of Nitric oxide pollution. The data points are more densely packed at lower values, indicating that most towns have lower levels of both NOX and INDUS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Create a histogram for the pupil to teacher ratio variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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//vWvNWDAAI0aNUolJSV68cUX9eOPP+qFF16o1nrLfyF54cKFCggIkK+vr2JiYq7oH6WkpCStWLFCd955p8aNG6cOHTrI4XDo2LFjWrt2rSZMmKBu3bpddB0hISF67LHHdOzYMd1444366KOP9MYbb+ixxx5TVFSUpJ9OQd19993O97Fly5Zav369Vq5cWek6fXx89NJLL6moqEhdu3ZVenq6nnvuOfXr1++yrie7lJ/97Gfy8/PTX//6V8XFxalhw4aKiIhQRETEVb1XF1vvn//8Z/Xr1099+/bViBEj1Lx5c/3www/KyMjQ7t279fe//13S5X9WH374YS1atEi///3vdfDgQfXs2VMOh0Pbt29XXFycHnrooSsak7lz56pHjx6644479Nhjjyk6OlqFhYU6fPiw/vGPf+iTTz658oG+hAEDBmjlypVKTEzUAw88oKysLP3xj39Us2bN9PXXX7v0bd++vTZu3Kh//OMfatasmQICApxHw85XW9831DD3Xh+N60X53VgX3oVT7p577rnk3VgvvfSS6d69uwkNDXXeKjxy5Ehz5MgRl9dNnjzZREREmHr16lW4a2LWrFnmxhtvNN7e3iY0NNQMHTrUeatuOYfDYZ577jnTokUL4+PjYzp06GA+/PBD07FjR5e7Uy52J1NJSYmZOHGiad68ufH19TU33XSTWbVqlRk+fLjLfpbfOfLiiy+6vL6qdV9qHM+3atUq061bN+Pr62saNGhgevXqZf7v//7vsrZTldTUVBMTE2Pq16/vchdQfHy8adu2bYX+F+6vMcYUFRWZZ5991rRq1cr4+Pg4bz8eN26cycnJuej2y7ezceNG06VLF2O3202zZs3MlClTKtx1lJ2dbR544AETHBxsgoKCzNChQ83OnTsrvRurQYMGZu/evSYhIcH4+fmZ4OBg89hjj5mioiKXdVb3bixjjHnnnXdM69atjbe3t5Fkpk2b5lx2Oe9Vddb7xRdfmMGDB5umTZsab29vEx4ebu666y6zYMECZ5/L/awaY0xxcbH5wx/+YGJjY42Pj48JCQkxd911l0lPT3f20QU/A1DuwrEz5qfxe/TRR03z5s2Nt7e3adKkienevbt57rnnnH2u9DNa1Xeq3AsvvGCio6ON3W43cXFx5o033jDTpk0zF/5zuGfPHnP77bcbf39/lzsxq7rT72reQ9Q+mzHnHXcDUKnMzEy1bt1a06ZN05QpU9xdznUrISFBeXl52rdvX42tc8SIEXrvvfdUVFRUY+sE4Fk4jQVc4IsvvtA777yj7t27KzAwUAcPHtTs2bMVGBiokSNHurs8AMAVIuwAF2jQoIF27typN998Uz/++KOCgoKUkJCg559/vsrbzwEAnovTWAAAwNK49RwAAFgaYQcAAFgaYQcAAFgaFyhLcjgcOn78uAICAq74J8kBAIB7GGNUWFh4yee2EXb00/NLIiMj3V0GAACohqysrArPPTwfYUdyPiMmKytLgYGBbq4GAABcjoKCAkVGRjr/Ha8KYUf/eTpuYGAgYQcAgGvMpS5B4QJlAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaV7uLgAAgNp27Ngx5eXluW37oaGhioqKctv2r3eEHQCApR07dkytW8epuPi022rw8/PXgQMZBB43IewAACwtLy9PxcWn1e3RaQpsFl3n2y/IPqLtf5muvLw8wo6bEHYAANeFwGbRCo5q5e4y4AZcoAwAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACzNrWHn008/1cCBAxURESGbzaZVq1ZV2XfUqFGy2WxKTU11aS8pKdHYsWMVGhqqBg0aaNCgQfr2229rt3AAAHDNcGvYOXXqlDp27Kj58+dftN+qVau0fft2RUREVFiWlJSk999/X++++662bNmioqIiDRgwQGVlZbVVNgAAuIa49Xd2+vXrp379+l20z3fffacxY8bo448/1j333OOyLD8/X2+++abefvtt3X333ZKkpUuXKjIyUuvWrVPfvn1rrXYAAHBt8OhrdhwOh4YNG6ZJkyapbdu2FZbv2rVLpaWl6tOnj7MtIiJC7dq1U3p6epXrLSkpUUFBgcsEAACsyaPDzqxZs+Tl5aXHH3+80uU5OTny8fFR48aNXdrDwsKUk5NT5XpnzpypoKAg5xQZGVmjdQMAAM/hsWFn165dmjt3rhYvXiybzXZFrzXGXPQ1kydPVn5+vnPKysq62nIBAICH8tiws3nzZuXm5ioqKkpeXl7y8vLS0aNHNWHCBEVHR0uSwsPDdfbsWZ08edLltbm5uQoLC6ty3Xa7XYGBgS4TAACwJo8NO8OGDdPevXu1Z88e5xQREaFJkybp448/liTdfPPN8vb2VlpamvN12dnZ2rdvn7p37+6u0gEAgAdx691YRUVFOnz4sHM+MzNTe/bsUXBwsKKiohQSEuLS39vbW+Hh4WrV6qen1gYFBWnkyJGaMGGCQkJCFBwcrIkTJ6p9+/bOu7MAAMD1za1hZ+fOnerZs6dzfvz48ZKk4cOHa/HixZe1jpdfflleXl4aPHiwiouL1atXLy1evFj169evjZIBAMA1xq1hJyEhQcaYy+5/5MiRCm2+vr6aN2+e5s2bV4OVAQAAq/DYa3YAAABqAmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYmlvDzqeffqqBAwcqIiJCNptNq1atci4rLS3VU089pfbt26tBgwaKiIjQI488ouPHj7uso6SkRGPHjlVoaKgaNGigQYMG6dtvv63jPQEAAJ7KrWHn1KlT6tixo+bPn19h2enTp7V7925NnTpVu3fv1sqVK3Xo0CENGjTIpV9SUpLef/99vfvuu9qyZYuKioo0YMAAlZWV1dVuAAAAD+blzo3369dP/fr1q3RZUFCQ0tLSXNrmzZunW265RceOHVNUVJTy8/P15ptv6u2339bdd98tSVq6dKkiIyO1bt069e3bt9b3AQAAeLZr6pqd/Px82Ww2NWrUSJK0a9culZaWqk+fPs4+ERERateundLT06tcT0lJiQoKClwmAABgTddM2Dlz5oyefvppDRkyRIGBgZKknJwc+fj4qHHjxi59w8LClJOTU+W6Zs6cqaCgIOcUGRlZq7UDAAD3uSbCTmlpqR566CE5HA699tprl+xvjJHNZqty+eTJk5Wfn++csrKyarJcAADgQTw+7JSWlmrw4MHKzMxUWlqa86iOJIWHh+vs2bM6efKky2tyc3MVFhZW5TrtdrsCAwNdJgAAYE0eHXbKg87XX3+tdevWKSQkxGX5zTffLG9vb5cLmbOzs7Vv3z517969rssFAAAeyK13YxUVFenw4cPO+czMTO3Zs0fBwcGKiIjQAw88oN27d+vDDz9UWVmZ8zqc4OBg+fj4KCgoSCNHjtSECRMUEhKi4OBgTZw4Ue3bt3fenQUAAK5vbg07O3fuVM+ePZ3z48ePlyQNHz5cycnJWr16tSSpU6dOLq/bsGGDEhISJEkvv/yyvLy8NHjwYBUXF6tXr15avHix6tevXyf7AAAAPJtbw05CQoKMMVUuv9iycr6+vpo3b57mzZtXk6UBAACL8OhrdgAAAK4WYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFiaW8POp59+qoEDByoiIkI2m02rVq1yWW6MUXJysiIiIuTn56eEhATt37/fpU9JSYnGjh2r0NBQNWjQQIMGDdK3335bh3sBAAA8mVvDzqlTp9SxY0fNnz+/0uWzZ8/WnDlzNH/+fO3YsUPh4eHq3bu3CgsLnX2SkpL0/vvv691339WWLVtUVFSkAQMGqKysrK52AwAAeDAvd268X79+6tevX6XLjDFKTU3VM888o/vvv1+StGTJEoWFhWnZsmUaNWqU8vPz9eabb+rtt9/W3XffLUlaunSpIiMjtW7dOvXt27fO9gUAAHgmj71mJzMzUzk5OerTp4+zzW63Kz4+Xunp6ZKkXbt2qbS01KVPRESE2rVr5+xTmZKSEhUUFLhMAADAmjw27OTk5EiSwsLCXNrDwsKcy3JycuTj46PGjRtX2acyM2fOVFBQkHOKjIys4eoBAICn8NiwU85ms7nMG2MqtF3oUn0mT56s/Px855SVlVUjtQIAAM/jsWEnPDxckiococnNzXUe7QkPD9fZs2d18uTJKvtUxm63KzAw0GUCAADW5LFhJyYmRuHh4UpLS3O2nT17Vps2bVL37t0lSTfffLO8vb1d+mRnZ2vfvn3OPgAA4Prm1ruxioqKdPjwYed8Zmam9uzZo+DgYEVFRSkpKUkpKSmKjY1VbGysUlJS5O/vryFDhkiSgoKCNHLkSE2YMEEhISEKDg7WxIkT1b59e+fdWQAA4Prm1rCzc+dO9ezZ0zk/fvx4SdLw4cO1ePFiPfnkkyouLlZiYqJOnjypbt26ae3atQoICHC+5uWXX5aXl5cGDx6s4uJi9erVS4sXL1b9+vXrfH8AAIDncWvYSUhIkDGmyuU2m03JyclKTk6uso+vr6/mzZunefPm1UKFAADgWuex1+wAAADUBMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwtGqFnRtuuEEnTpyo0P7jjz/qhhtuuOqiAAAAakq1ws6RI0dUVlZWob2kpETffffdVRcFAABQU7yupPPq1aud//3xxx8rKCjIOV9WVqb169crOjq6xooDAAC4WlcUdn75y19Kkmw2m4YPH+6yzNvbW9HR0XrppZdqrDgAAICrdUWnsRwOhxwOh6KiopSbm+ucdzgcKikp0cGDBzVgwIAaK+7cuXN69tlnFRMTIz8/P91www2aMWOGHA6Hs48xRsnJyYqIiJCfn58SEhK0f//+GqsBAABc26p1zU5mZqZCQ0NrupYKZs2apQULFmj+/PnKyMjQ7Nmz9eKLL2revHnOPrNnz9acOXM0f/587dixQ+Hh4erdu7cKCwtrvT4AAOD5rug01vnWr1+v9evXO4/wnO8vf/nLVRcmSVu3btW9996re+65R5IUHR2td955Rzt37pT001Gd1NRUPfPMM7r//vslSUuWLFFYWJiWLVumUaNG1UgdAADg2lWtIzvTp09Xnz59tH79euXl5enkyZMuU03p0aOH1q9fr0OHDkmSvvjiC23ZskX9+/eX9NMRppycHPXp08f5Grvdrvj4eKWnp9dYHQAA4NpVrSM7CxYs0OLFizVs2LCarsfFU089pfz8fLVu3Vr169dXWVmZnn/+eT388MOSpJycHElSWFiYy+vCwsJ09OjRKtdbUlKikpIS53xBQUEtVA8AADxBtY7snD17Vt27d6/pWipYvny5li5dqmXLlmn37t1asmSJ/vSnP2nJkiUu/Ww2m8u8MaZC2/lmzpypoKAg5xQZGVkr9QMAAPerVtj5zW9+o2XLltV0LRVMmjRJTz/9tB566CG1b99ew4YN07hx4zRz5kxJUnh4uKT/HOEpl5ubW+Foz/kmT56s/Px855SVlVV7OwEAANyqWqexzpw5o4ULF2rdunXq0KGDvL29XZbPmTOnRoo7ffq06tVzzWP169d3XhAdExOj8PBwpaWlqXPnzpJ+Ouq0adMmzZo1q8r12u122e32GqkRAAB4tmqFnb1796pTp06SpH379rksu9jpoys1cOBAPf/884qKilLbtm31+eefa86cOXr00Ued20pKSlJKSopiY2MVGxurlJQU+fv7a8iQITVWBwAAuHZVK+xs2LChpuuo1Lx58zR16lQlJiYqNzdXERERGjVqlP7whz84+zz55JMqLi5WYmKiTp48qW7dumnt2rUKCAiokxoBAIBnq/bv7NSFgIAApaamKjU1tco+NptNycnJSk5OrrO6AADAtaNaYadnz54XPV31ySefVLsgAACAmlStsFN+vU650tJS7dmzR/v27avwgFAAAAB3qlbYefnllyttT05OVlFR0VUVBAAAUJOq9Ts7VRk6dGiNPRcLAACgJtRo2Nm6dat8fX1rcpUAAABXpVqnscqfMF7OGKPs7Gzt3LlTU6dOrZHCAAAAakK1wk5QUJDLfL169dSqVSvNmDHD5QnkAAAA7latsLNo0aKargMAAKBWXNWPCu7atUsZGRmy2Wxq06aN8/lUAAAAnqJaYSc3N1cPPfSQNm7cqEaNGskYo/z8fPXs2VPvvvuumjRpUtN1AgAAVEu17sYaO3asCgoKtH//fv3www86efKk9u3bp4KCAj3++OM1XSMAAEC1VevIzpo1a7Ru3TrFxcU529q0aaNXX32VC5QBAIBHqdaRHYfDIW9v7wrt3t7ecjgcV10UAABATalW2Lnrrrv0xBNP6Pjx48627777TuPGjVOvXr1qrDgAAICrVa2wM3/+fBUWFio6Olo/+9nP9POf/1wxMTEqLCzUvHnzarpGAACAaqvWNTuRkZHavXu30tLSdODAARlj1KZNG9199901XR8AAMBVuaIjO5988onatGmjgoICSVLv3r01duxYPf744+ratavatm2rzZs310qhAAAA1XFFYSc1NVW//e1vFRgYWGFZUFCQRo0apTlz5tRYcQAAAFfrisLOF198oV/84hdVLu/Tp4927dp11UUBAADUlCsKO//+978rveW8nJeXl77//vurLgoAAKCmXFHYad68ub788ssql+/du1fNmjW76qIAAABqyhWFnf79++sPf/iDzpw5U2FZcXGxpk2bpgEDBtRYcQAAAFfrim49f/bZZ7Vy5UrdeOONGjNmjFq1aiWbzaaMjAy9+uqrKisr0zPPPFNbtQIAAFyxKwo7YWFhSk9P12OPPabJkyfLGCNJstls6tu3r1577TWFhYXVSqEAAADVccU/KtiyZUt99NFHOnnypA4fPixjjGJjY9W4cePaqA8AAOCqVOsXlCWpcePG6tq1a03WAgAAUOOq9WwsAACAawVhBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWJrHh53vvvtOQ4cOVUhIiPz9/dWpUyft2rXLudwYo+TkZEVERMjPz08JCQnav3+/GysGAACexKPDzsmTJ3X77bfL29tb//znP/XVV1/ppZdeUqNGjZx9Zs+erTlz5mj+/PnasWOHwsPD1bt3bxUWFrqvcAAA4DG83F3AxcyaNUuRkZFatGiRsy06Otr538YYpaam6plnntH9998vSVqyZInCwsK0bNkyjRo1qq5LBgAAHsajw87q1avVt29f/dd//Zc2bdqk5s2bKzExUb/97W8lSZmZmcrJyVGfPn2cr7Hb7YqPj1d6enqVYaekpEQlJSXO+YKCgtrdEQBudezYMeXl5blt+6GhoYqKinLb9oHrnUeHnX/96196/fXXNX78eE2ZMkWfffaZHn/8cdntdj3yyCPKycmRJIWFhbm8LiwsTEePHq1yvTNnztT06dNrtXYAnuHYsWNq3TpOxcWn3VaDn5+/DhzIIPAAbuLRYcfhcKhLly5KSUmRJHXu3Fn79+/X66+/rkceecTZz2azubzOGFOh7XyTJ0/W+PHjnfMFBQWKjIys4eoBeIK8vDwVF59Wt0enKbBZdJ1vvyD7iLb/Zbry8vIIO4CbeHTYadasmdq0aePSFhcXpxUrVkiSwsPDJUk5OTlq1qyZs09ubm6Foz3ns9vtstvttVAxAE8V2CxawVGt3F0GADfw6Luxbr/9dh08eNCl7dChQ2rZsqUkKSYmRuHh4UpLS3MuP3v2rDZt2qTu3bvXaa0AAMAzefSRnXHjxql79+5KSUnR4MGD9dlnn2nhwoVauHChpJ9OXyUlJSklJUWxsbGKjY1VSkqK/P39NWTIEDdXDwAAPIFHh52uXbvq/fff1+TJkzVjxgzFxMQoNTVVv/71r519nnzySRUXFysxMVEnT55Ut27dtHbtWgUEBLixcgAA4Ck8OuxI0oABAzRgwIAql9tsNiUnJys5ObnuigIAANcMj75mBwAA4GoRdgAAgKURdgAAgKURdgAAgKV5/AXKwNXiuUgAcH0j7MDSeC4SAICwA0vjuUgAAMIOrgs8FwnXM3efypU4nStJGRkZbtv29T7+hB0AsDBPOJUrXd+nc4vzT0iyaejQoW6r4Xoef4mwAwCW5u5TuRKnc0tPF0oy6jTkKTWJaV3n27/ex18i7ADAdYFTue7XsGkU74Gb8Ds7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0rgbCwDqgLt+UM6dP2QHeArCDgDUIk/4QTlJKi0569btA+5E2AGAWuTuH5TL/nKr9q1eqHPnztX5tgFPQdgBgDrgrh+UK8g+UufbBDwNFygDAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLu6bCzsyZM2Wz2ZSUlORsM8YoOTlZERER8vPzU0JCgvbv3+++IgEAgEe5ZsLOjh07tHDhQnXo0MGlffbs2ZozZ47mz5+vHTt2KDw8XL1791ZhYaGbKgUAAJ7kmgg7RUVF+vWvf6033nhDjRs3drYbY5SamqpnnnlG999/v9q1a6clS5bo9OnTWrZsmRsrBgAAnuKaCDujR4/WPffco7vvvtulPTMzUzk5OerTp4+zzW63Kz4+Xunp6XVdJgAA8EBe7i7gUt59913t3r1bO3bsqLAsJydHkhQWFubSHhYWpqNHj1a5zpKSEpWUlDjnCwoKaqhaAADgaTz6yE5WVpaeeOIJLV26VL6+vlX2s9lsLvPGmApt55s5c6aCgoKcU2RkZI3VDAAAPItHh51du3YpNzdXN998s7y8vOTl5aVNmzbplVdekZeXl/OITvkRnnK5ubkVjvacb/LkycrPz3dOWVlZtbofAADAfTz6NFavXr305ZdfurT993//t1q3bq2nnnpKN9xwg8LDw5WWlqbOnTtLks6ePatNmzZp1qxZVa7XbrfLbrfXau0AAMAzeHTYCQgIULt27VzaGjRooJCQEGd7UlKSUlJSFBsbq9jYWKWkpMjf319DhgxxR8kAAMDDeHTYuRxPPvmkiouLlZiYqJMnT6pbt25au3atAgIC3F0aAADwANdc2Nm4caPLvM1mU3JyspKTk91SDwAA8GwefYEyAADA1SLsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAAS7vmHgQK4MocO3ZMeXl5bq0hNDRUUVFRbq0BwPWLsANY2LFjx9S6dZyKi0+7tQ4/P38dOJBB4AHgFoQdwMLy8vJUXHxa3R6dpsBm0W6poSD7iLb/Zbry8vIIOwDcgrADXAcCm0UrOKqVu8sAALfgAmUAAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBpHh12Zs6cqa5duyogIEBNmzbVL3/5Sx08eNCljzFGycnJioiIkJ+fnxISErR//343VQwAADyNR4edTZs2afTo0dq2bZvS0tJ07tw59enTR6dOnXL2mT17tubMmaP58+drx44dCg8PV+/evVVYWOjGygEAgKfwcncBF7NmzRqX+UWLFqlp06batWuX7rzzThljlJqaqmeeeUb333+/JGnJkiUKCwvTsmXLNGrUKHeUDQAAPIhHH9m5UH5+viQpODhYkpSZmamcnBz16dPH2cdutys+Pl7p6elVrqekpEQFBQUuEwAAsKZrJuwYYzR+/Hj16NFD7dq1kyTl5ORIksLCwlz6hoWFOZdVZubMmQoKCnJOkZGRtVc4AABwq2sm7IwZM0Z79+7VO++8U2GZzWZzmTfGVGg73+TJk5Wfn++csrKyarxeAADgGTz6mp1yY8eO1erVq/Xpp5+qRYsWzvbw8HBJPx3hadasmbM9Nze3wtGe89ntdtnt9torGAAAeAyPPrJjjNGYMWO0cuVKffLJJ4qJiXFZHhMTo/DwcKWlpTnbzp49q02bNql79+51XS4AAPBAHn1kZ/To0Vq2bJk++OADBQQEOK/DCQoKkp+fn2w2m5KSkpSSkqLY2FjFxsYqJSVF/v7+GjJkiJurBwAAnsCjw87rr78uSUpISHBpX7RokUaMGCFJevLJJ1VcXKzExESdPHlS3bp109q1axUQEFDH1QIAAE/k0WHHGHPJPjabTcnJyUpOTq79ggAAwDXHo6/ZAQAAuFqEHQAAYGkefRoLAGAdGRkZ19V24TkIOwCAWlWcf0KSTUOHDnVrHaUlZ926fbgPYaeWHTt2THl5eW6tITQ0VFFRUW6tAcD1q/R0oSSjTkOeUpOY1nW+/ewvt2rf6oU6d+5cnW8bnoGwU4uOHTum1q3jVFx82q11+Pn568CBDAIPALdq2DRKwVGt6ny7BdlH6nyb8CyEnVqUl5en4uLT6vboNAU2i3ZLDQXZR7T9L9OVl5dH2AEAXJcIO3UgsFm0W/7fDAAA4NZzAABgcRzZAVAnuO0YgLsQdgDUKm47BuBuhB0AtYrbjgG4G2EHQJ3gtmMA7sIFygAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNL4UUGgDvBcKABwH8IOUIt4LhQAuB9hB6hFPBcKANyPsAPUAZ4LBQDuwwXKAADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0ng2FgAA14GMjAy3bTs0NFRRUVFu275lws5rr72mF198UdnZ2Wrbtq1SU1N1xx13uLssAADcqjj/hCSbhg4d6rYa/Pz8deBAhtsCjyXCzvLly5WUlKTXXntNt99+u/785z+rX79++uqrr9yaJD2JOxN9SUmJ7Ha7W7btzv0GAE9QerpQklGnIU+pSUzrOt9+QfYRbf/LdOXl5RF2rsacOXM0cuRI/eY3v5Ekpaam6uOPP9brr7+umTNnurk69/KERC+bTTLGfduXVFpy1q3bBwB3a9g0SsFRrdxdhltc82Hn7Nmz2rVrl55++mmX9j59+ig9Pd1NVXkOdyf67C+3at/qhW7f/rlz5+p82wAAz3DNh528vDyVlZUpLCzMpT0sLEw5OTmVvqakpEQlJSXO+fz8fElSQUFBjdZWVFQkSfrh6EGdKymu0XVfroLso5KkstISt9RQVnrWI7af/93X8vay1fn2y8f/et2+J9TA9vkMsH03bz/nmKSf/k2s6X9ny9dnLnX2wFzjvvvuOyPJpKenu7Q/99xzplWrVpW+Ztq0aUYSExMTExMTkwWmrKysi2aFa/7ITmhoqOrXr1/hKE5ubm6Foz3lJk+erPHjxzvnHQ6HfvjhB4WEhMhmc8//87keFBQUKDIyUllZWQoMDHR3Odcdxt99GHv3YvzdqzbH3xijwsJCRUREXLTfNR92fHx8dPPNNystLU333Xefsz0tLU333ntvpa+x2+0V7g5q1KhRbZaJ8wQGBvIHx40Yf/dh7N2L8Xev2hr/oKCgS/a55sOOJI0fP17Dhg1Tly5ddNttt2nhwoU6duyYfv/737u7NAAA4GaWCDsPPvigTpw4oRkzZig7O1vt2rXTRx99pJYtW7q7NAAA4GaWCDuSlJiYqMTERHeXgYuw2+2aNm2a235g8HrH+LsPY+9ejL97ecL424xx86+9AQAA1CKeeg4AACyNsAMAACyNsAMAACyNsAMAACyNsIMa9emnn2rgwIGKiIiQzWbTqlWrquw7atQo2Ww2paam1ll9Vnc545+RkaFBgwYpKChIAQEBuvXWW3Xs2LG6L9aCLjX+RUVFGjNmjFq0aCE/Pz/FxcXp9ddfd0+xFjRz5kx17dpVAQEBatq0qX75y1/q4MGDLn2MMUpOTlZERIT8/PyUkJCg/fv3u6li67jU2JeWluqpp55S+/bt1aBBA0VEROiRRx7R8ePH66Q+wg5q1KlTp9SxY0fNnz//ov1WrVql7du3X/InvnFlLjX+33zzjXr06KHWrVtr48aN+uKLLzR16lT5+vrWcaXWdKnxHzdunNasWaOlS5cqIyND48aN09ixY/XBBx/UcaXWtGnTJo0ePVrbtm1TWlqazp07pz59+ujUqVPOPrNnz9acOXM0f/587dixQ+Hh4erdu7cKCwvdWPm171Jjf/r0ae3evVtTp07V7t27tXLlSh06dEiDBg2qmwJr5GmcQCUkmffff79C+7fffmuaN29u9u3bZ1q2bGlefvnlOq/telDZ+D/44INm6NCh7inoOlPZ+Ldt29bMmDHDpe2mm24yzz77bB1Wdv3Izc01ksymTZuMMcY4HA4THh5uXnjhBWefM2fOmKCgILNgwQJ3lWlJF459ZT777DMjyRw9erTW6+HIDuqUw+HQsGHDNGnSJLVt29bd5VxXHA6H/vd//1c33nij+vbtq6ZNm6pbt24XPdWImtWjRw+tXr1a3333nYwx2rBhgw4dOqS+ffu6uzRLys/PlyQFBwdLkjIzM5WTk6M+ffo4+9jtdsXHxys9Pd0tNVrVhWNfVR+bzVYnz6Yk7KBOzZo1S15eXnr88cfdXcp1Jzc3V0VFRXrhhRf0i1/8QmvXrtV9992n+++/X5s2bXJ3edeFV155RW3atFGLFi3k4+OjX/ziF3rttdfUo0cPd5dmOcYYjR8/Xj169FC7du0kSTk5OZKksLAwl75hYWHOZbh6lY39hc6cOaOnn35aQ4YMqZOHs1rmcRHwfLt27dLcuXO1e/du2Ww2d5dz3XE4HJKke++9V+PGjZMkderUSenp6VqwYIHi4+PdWd514ZVXXtG2bdu0evVqtWzZUp9++qkSExPVrFkz3X333e4uz1LGjBmjvXv3asuWLRWWXfj3xxjD36QadLGxl366WPmhhx6Sw+HQa6+9Vic1cWQHdWbz5s3Kzc1VVFSUvLy85OXlpaNHj2rChAmKjo52d3mWFxoaKi8vL7Vp08alPS4ujrux6kBxcbGmTJmiOXPmaODAgerQoYPGjBmjBx98UH/605/cXZ6ljB07VqtXr9aGDRvUokULZ3t4eLgkVTiKk5ubW+FoD6qnqrEvV1paqsGDByszM1NpaWl1clRHIuygDg0bNkx79+7Vnj17nFNERIQmTZqkjz/+2N3lWZ6Pj4+6du1a4VbcQ4cOqWXLlm6q6vpRWlqq0tJS1avn+me3fv36zqNuuDrGGI0ZM0YrV67UJ598opiYGJflMTExCg8PV1pamrPt7Nmz2rRpk7p3717X5VrKpcZe+k/Q+frrr7Vu3TqFhITUWX2cxkKNKioq0uHDh53zmZmZ2rNnj4KDgxUVFVXhw+3t7a3w8HC1atWqrku1pEuN/6RJk/Tggw/qzjvvVM+ePbVmzRr94x//0MaNG91XtIVcavzj4+M1adIk+fn5qWXLltq0aZPeeustzZkzx41VW8fo0aO1bNkyffDBBwoICHAewQkKCpKfn59sNpuSkpKUkpKi2NhYxcbGKiUlRf7+/hoyZIibq7+2XWrsz507pwceeEC7d+/Whx9+qLKyMmef4OBg+fj41G6BtX6/F64rGzZsMJIqTMOHD6+0P7ee16zLGf8333zT/PznPze+vr6mY8eOZtWqVe4r2GIuNf7Z2dlmxIgRJiIiwvj6+ppWrVqZl156yTgcDvcWbhGVjb0ks2jRImcfh8Nhpk2bZsLDw43dbjd33nmn+fLLL91XtEVcauwzMzOr7LNhw4Zar8/2/4sEAACwJK7ZAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAXBZNm7cKJvNph9//FGStHjxYjVq1MitNdW25ORkderUyd1lVMuF7xdwPSPsALgs3bt3V3Z2toKCgi6r/5EjR2Sz2bRnz56r3nZCQoKSkpKuej1WVdn4XOn7BVgZYQfAZfHx8VF4eLhsNpu7S7mmGWN07ty5y+pbWlpa7e3wfgH/QdgBrnEJCQkaM2aMxowZo0aNGikkJETPPvuszn8SjM1m06pVq1xe16hRIy1evFjSf47CvPvuu+revbt8fX3Vtm1blweEXulpkfKnHnfu3Fk2m00JCQmSJIfDoRkzZqhFixay2+3q1KmT1qxZU+V6RowYoU2bNmnu3Lmy2Wyy2Ww6cuSIJOmrr75S//791bBhQ4WFhWnYsGHKy8tzvnbNmjXq0aOHc1wGDBigb775xmX93377rR566CEFBwerQYMG6tKli7Zv3+7S5+2331Z0dLSCgoL00EMPqbCw0LnMGKPZs2frhhtukJ+fnzp27Kj33nuvwrh9/PHH6tKli+x2uzZv3lxhP8vfg7/97W9KSEiQr6+vli5dqhMnTujhhx9WixYt5O/vr/bt2+udd9655PhU9n6tWLFCbdu2ld1uV3R0tF566aUqxx2wlFp/+haAWhUfH28aNmxonnjiCXPgwAGzdOlS4+/vbxYuXOjsI8m8//77Lq8LCgqq8JC+Fi1amPfee8989dVX5je/+Y0JCAgweXl5xpj/POTy5MmTxhhjFi1aZIKCgqqs67PPPjOSzLp160x2drY5ceKEMcaYOXPmmMDAQPPOO++YAwcOmCeffNJ4e3ubQ4cOVbqeH3/80dx2223mt7/9rcnOzjbZ2dnm3Llz5vjx4yY0NNRMnjzZZGRkmN27d5vevXubnj17Ol/73nvvmRUrVphDhw6Zzz//3AwcONC0b9/elJWVGWOMKSwsNDfccIO54447zObNm83XX39tli9fbtLT040xxkybNs00bNjQ3H///ebLL780n376qQkPDzdTpkxxbmPKlCmmdevWZs2aNeabb74xixYtMna73WzcuNFl3Dp06GDWrl1rDh8+7BzT85W/B9HR0WbFihXmX//6l/nuu+/Mt99+a1588UXz+eefm2+++ca88sorpn79+mbbtm0XHZ8L36+dO3eaevXqmRkzZpiDBw+aRYsWGT8/P5eHZAJWRdgBrnHx8fEmLi7O5cnZTz31lImLi3POX27YeeGFF5zLS0tLTYsWLcysWbOMMVcedsrX+fnnn7u0R0REmOeff96lrWvXriYxMfGi+/jEE0+4tE2dOtX06dPHpS0rK8tIMgcPHqx0Pbm5uUaS8ynXf/7zn01AQIAziF1o2rRpxt/f3xQUFDjbJk2aZLp162aMMaaoqMj4+vo6w1G5kSNHmocfftgY859xu9TT5cvHKzU19aL9jDGmf//+ZsKECc75ysbnwvdryJAhpnfv3i59Jk2aZNq0aXPJ7QHXOk5jARZw6623ulybcdttt+nrr79WWVnZFa3ntttuc/63l5eXunTpooyMjBqrs6CgQMePH9ftt9/u0n777bdf8XZ27dqlDRs2qGHDhs6pdevWkuQ8VfXNN99oyJAhuuGGGxQYGOg8tXbs2DFJ0p49e9S5c2cFBwdXuZ3o6GgFBAQ455s1a6bc3FxJP51GO3PmjHr37u1Sx1tvvVXhdFmXLl0ua78u7FdWVqbnn39eHTp0UEhIiBo2bKi1a9c69+FyZWRkVDru1fmcANcaL3cXAKD22Ww2l2t4pMu/+LU2LnC9cJ3GmCvejsPh0MCBAzVr1qwKy5o1ayZJGjhwoCIjI/XGG28oIiJCDodD7dq109mzZyVJfn5+l9yOt7d3hdodDoezBkn63//9XzVv3tyln91ud5lv0KDBZe3Xhf1eeuklvfzyy0pNTVX79u3VoEEDJSUlOffhclU2xhd+JgCrIuwAFrBt27YK87Gxsapfv74kqUmTJsrOznYu//rrr3X69OlK13PnnXdKks6dO6ddu3ZpzJgx1arJx8dHklyOGgQGBioiIkJbtmxxbkeS0tPTdcstt1x0XRcefbjpppu0YsUKRUdHy8ur4p+yEydOKCMjQ3/+8591xx13SJK2bNni0qdDhw76n//5H/3www8XPbpTlTZt2shut+vYsWOKj4+/4tdfjs2bN+vee+/V0KFDJf0UsL7++mvFxcU5+1Q2PpXVeuH+p6en68Ybb3R+TgCr4jQWYAFZWVkaP368Dh48qHfeeUfz5s3TE0884Vx+1113af78+dq9e7d27typ3//+9xWOWEjSq6++qvfff18HDhzQ6NGjdfLkST366KPVqqlp06by8/PTmjVr9O9//1v5+fmSpEmTJmnWrFlavny5Dh48qKefflp79uxxqfdC0dHR2r59u44cOaK8vDw5HA6NHj1aP/zwgx5++GF99tln+te//qW1a9fq0UcfVVlZmRo3bqyQkBAtXLhQhw8f1ieffKLx48e7rPfhhx9WeHi4fvnLX+r//u//9K9//UsrVqzQ1q1bL2sfAwICNHHiRI0bN05LlizRN998o88//1yvvvqqlixZUq1xu9DPf/5zpaWlKT09XRkZGRo1apRycnIuOT4XmjBhgtavX68//vGPOnTokJYsWaL58+dr4sSJNVIn4NHce8kQgKsVHx9vEhMTze9//3sTGBhoGjdubJ5++mmXC5a/++4706dPH9OgQQMTGxtrPvroo0ovUF62bJnp1q2b8fHxMXFxcWb9+vXOdVzpBcrGGPPGG2+YyMhIU69ePRMfH2+MMaasrMxMnz7dNG/e3Hh7e5uOHTuaf/7znxddz8GDB82tt95q/Pz8jCSTmZlpjDHm0KFD5r777jONGjUyfn5+pnXr1iYpKcm572lpaSYuLs7Y7XbToUMHs3HjxgoXax85csT86le/MoGBgcbf39906dLFbN++3Rjz0wXKHTt2dKnl5ZdfNi1btnTOOxwOM3fuXNOqVSvj7e1tmjRpYvr27Ws2bdpU6bhVpaoLuk+cOGHuvfde07BhQ9O0aVPz7LPPmkceecTce++9Fx2fyrb73nvvmTZt2hhvb28TFRVlXnzxxYvWBFiFzRhO2gLXsoSEBHXq1EmpqanVXseRI0cUExOjzz///Jp9PAIAVIXTWAAAwNIIOwAAwNI4jQUAACyNIzsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDS/h99fu5ZGaYnPwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'\\n    EXPLANATION:\\n    \\n    This histogram shows the distribution of the pupil-to-teacher ratio.\\n    \\n    The peak around the value 20 suggests that this ratio is the most common in the dataset,\\nwith over 140 occurrences.\\n\\n    This visualization helps understand how pupil-to-teacher ratios are spread across different schools or districts.\\n\\n'"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax = sns.histplot(boston_df['PTRATIO'])\n",
    "pyplot.title('Histogram of the pupil to teacher ratio')\n",
    "pyplot.xlabel('pupil to teacher ratio')\n",
    "pyplot.show()\n",
    "\n",
    "\"\"\"\n",
    "    EXPLANATION:\n",
    "    \n",
    "    This histogram shows the distribution of the pupil-to-teacher ratio.\n",
    "    \n",
    "    The peak around the value 20 suggests that this ratio is the most common in the dataset,\n",
    "with over 140 occurrences.\n",
    "\n",
    "    This visualization helps understand how pupil-to-teacher ratios are spread across different schools or districts.\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3: Use the appropriate tests to answer the questions provided."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Is there a significant difference in median value of houses bounded by the Charles river or not? (T-test for independent samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "State the hypothesis\n",
    "\n",
    "*   $H_0: µ_1 = µ_2$ (\"there is no difference in MEDV of whether having a Charles river or not\")\n",
    "*   $H_1: µ_1 ≠ µ_2$ (\"there is a difference in MEDV of whether having a Charles river or not\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TtestResult(statistic=-3.996437466090509, pvalue=7.390623170519905e-05, df=504.0)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.ttest_ind(boston_df[boston_df['CHAS']==0]['MEDV'],\n",
    "                      boston_df[boston_df['CHAS']==1]['MEDV'],\n",
    "                      equal_var=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:** Since the p-value is less than alpha value 0.05, we reject the null hypothesis as there is enough proof that there is a statistical difference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Is there a difference in Median values of houses (MEDV) for each proportion of owner occupied units built prior to 1940 (AGE)? (ANOVA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "State the hypothesis\n",
    "\n",
    "*   $H_0: µ_1 = µ_2 = µ_3$ (the three population means are equal)\n",
    "*   $H_1:$ At least one of the means differ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeveneResult(statistic=2.7806200293748304, pvalue=0.06295337343259205)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.levene(boston_df[boston_df['Age_Group'] == '35 and younger']['MEDV'],\n",
    "                   boston_df[boston_df['Age_Group'] == '35 to 70']['MEDV'], \n",
    "                   boston_df[boston_df['Age_Group'] == '70 and older']['MEDV'], \n",
    "                   center='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "thirtyfive_lower = boston_df[boston_df['Age_Group'] == '35 and younger']['MEDV']\n",
    "thirtyfive_seventy = boston_df[boston_df['Age_Group'] == '35 to 70']['MEDV']\n",
    "seventy_upper = boston_df[boston_df['Age_Group'] == '70 and older']['MEDV']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F_Statistic: 36.40764999196599, P-Value: 1.7105011022702984e-15\n"
     ]
    }
   ],
   "source": [
    "f_statistic, p_value = scipy.stats.f_oneway(thirtyfive_lower, thirtyfive_seventy, seventy_upper)\n",
    "print(\"F_Statistic: {0}, P-Value: {1}\".format(f_statistic,p_value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:** 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 differ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Can we conclude that there is no relationship between Nitric oxide concentrations and proportion of non-retail business acres per town? (Pearson Correlation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "State the Hypothesis\n",
    "\n",
    "*   $H_0: µ_1 = µ_2 = µ_3$ (There is no linear relationship between nitric oxide concentrations (NOX) and the proportion of non-retail business acres per town (INDUS).)\n",
    "*   $H_1:$ There is a linear relationship between nitric oxide concentrations (NOX) and the proportion of non-retail business acres per town (INDUS)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.763651446920915, pvalue=7.913361061241167e-98)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.pearsonr(boston_df['NOX'], boston_df['INDUS'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**: NO, we can't conclude that there is no significant relationship between NOX and INDUS, as the Pearson correlation coefficient between nitric oxide concentrations (NOX) and the proportion of non-retail business acres per town (INDUS) is approximately 0.798. This indicates a strong positive linear relationship between the two variables. Therefore, we can conclude that there is a significant relationship between NOX and INDUS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "State the hypothesis\n",
    "\n",
    "* $H_0: β1$ = 0 (Additional weighted distance to the five Boston employment centres has no impact on the median value of owner occupied homes)\n",
    "* $H_1: β1$ is not equal to 0 ((Additional weighted distance to the five Boston employment centres has an impact on the median value of owner occupied homes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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>Sun, 20 Oct 2024</td> <th>  Prob (F-statistic):</th> <td>1.21e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>07:37:18</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:}             & Sun, 20 Oct 2024 & \\textbf{  Prob (F-statistic):} &  1.21e-08   \\\\\n",
       "\\textbf{Time:}             &     07:37:18     & \\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:                Sun, 20 Oct 2024   Prob (F-statistic):           1.21e-08\n",
       "Time:                        07:37:18   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": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## insert code here\n",
    "## create a list of students and evaluation socres\n",
    "X = boston_df['DIS']\n",
    "y = boston_df['MEDV']\n",
    "\n",
    "## add an intercept (beta_0) to our model\n",
    "X = sm.add_constant(X) \n",
    "\n",
    "model = sm.OLS(y, X).fit()\n",
    "predictions = model.predict(X)\n",
    "\n",
    "# Print out the statistics\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:** the p-value is less than the alpha (α) level = 0.05, so we reject the null hypothesis as there is evidence that there is a difference in additional weighted distance to the five Boston employment centres based on the median value of owner occupied homes. The coefficient *1.0916* indicates that the median value of owner-occupied homes (MEDV) is expected to increase by approximately 1.09 units (in the same units as MEDV, which typically represents thousands of dollars)."
   ]
  }
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