Peer_Graded_Assignment.jupyterlite.ipynb

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      "display_name": "Python (Pyodide)",
      "language": "python"
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      "codemirror_mode": {
        "name": "python",
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      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
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  },
  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "source": "<p style=\"text-align:center\">\n    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkST0151ENSkillsNetwork20531532-2022-01-01\" target=\"_blank\">\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n    </a>\n</p>\n\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "#### Import the required libraries we need for the lab.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import piplite\nawait piplite.install(['numpy'],['pandas'])\nawait piplite.install(['seaborn'])",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 3
    },
    {
      "cell_type": "code",
      "source": "import pandas as pd\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt \nimport scipy.stats\nimport statsmodels.api as sm\nfrom statsmodels.formula.api import ols",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 4
    },
    {
      "cell_type": "markdown",
      "source": "#### Read the dataset in the csv file from the URL\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from js import fetch\nimport io\n\nURL = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ST0151EN-SkillsNetwork/labs/boston_housing.csv'\nresp = await fetch(URL)\nboston_url = io.BytesIO((await resp.arrayBuffer()).to_py())",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 5
    },
    {
      "cell_type": "code",
      "source": "boston_df=pd.read_csv(boston_url)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 6
    },
    {
      "cell_type": "markdown",
      "source": "#### Add your code below following the instructions given in the course to complete the peer graded assignment\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#See first 10 rows in the data\nboston_df.head(10)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 7,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   Unnamed: 0     CRIM    ZN  INDUS  CHAS    NOX     RM    AGE     DIS  RAD  \\\n0           0  0.00632  18.0   2.31   0.0  0.538  6.575   65.2  4.0900  1.0   \n1           1  0.02731   0.0   7.07   0.0  0.469  6.421   78.9  4.9671  2.0   \n2           2  0.02729   0.0   7.07   0.0  0.469  7.185   61.1  4.9671  2.0   \n3           3  0.03237   0.0   2.18   0.0  0.458  6.998   45.8  6.0622  3.0   \n4           4  0.06905   0.0   2.18   0.0  0.458  7.147   54.2  6.0622  3.0   \n5           5  0.02985   0.0   2.18   0.0  0.458  6.430   58.7  6.0622  3.0   \n6           6  0.08829  12.5   7.87   0.0  0.524  6.012   66.6  5.5605  5.0   \n7           7  0.14455  12.5   7.87   0.0  0.524  6.172   96.1  5.9505  5.0   \n8           8  0.21124  12.5   7.87   0.0  0.524  5.631  100.0  6.0821  5.0   \n9           9  0.17004  12.5   7.87   0.0  0.524  6.004   85.9  6.5921  5.0   \n\n     TAX  PTRATIO  LSTAT  MEDV  \n0  296.0     15.3   4.98  24.0  \n1  242.0     17.8   9.14  21.6  \n2  242.0     17.8   4.03  34.7  \n3  222.0     18.7   2.94  33.4  \n4  222.0     18.7   5.33  36.2  \n5  222.0     18.7   5.21  28.7  \n6  311.0     15.2  12.43  22.9  \n7  311.0     15.2  19.15  27.1  \n8  311.0     15.2  29.93  16.5  \n9  311.0     15.2  17.10  18.9  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\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>4.98</td>\n      <td>24.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\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>9.14</td>\n      <td>21.6</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\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>4.03</td>\n      <td>34.7</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\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>2.94</td>\n      <td>33.4</td>\n    </tr>\n    <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      <td>36.2</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>0.02985</td>\n      <td>0.0</td>\n      <td>2.18</td>\n      <td>0.0</td>\n      <td>0.458</td>\n      <td>6.430</td>\n      <td>58.7</td>\n      <td>6.0622</td>\n      <td>3.0</td>\n      <td>222.0</td>\n      <td>18.7</td>\n      <td>5.21</td>\n      <td>28.7</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>6</td>\n      <td>0.08829</td>\n      <td>12.5</td>\n      <td>7.87</td>\n      <td>0.0</td>\n      <td>0.524</td>\n      <td>6.012</td>\n      <td>66.6</td>\n      <td>5.5605</td>\n      <td>5.0</td>\n      <td>311.0</td>\n      <td>15.2</td>\n      <td>12.43</td>\n      <td>22.9</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>7</td>\n      <td>0.14455</td>\n      <td>12.5</td>\n      <td>7.87</td>\n      <td>0.0</td>\n      <td>0.524</td>\n      <td>6.172</td>\n      <td>96.1</td>\n      <td>5.9505</td>\n      <td>5.0</td>\n      <td>311.0</td>\n      <td>15.2</td>\n      <td>19.15</td>\n      <td>27.1</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>8</td>\n      <td>0.21124</td>\n      <td>12.5</td>\n      <td>7.87</td>\n      <td>0.0</td>\n      <td>0.524</td>\n      <td>5.631</td>\n      <td>100.0</td>\n      <td>6.0821</td>\n      <td>5.0</td>\n      <td>311.0</td>\n      <td>15.2</td>\n      <td>29.93</td>\n      <td>16.5</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>9</td>\n      <td>0.17004</td>\n      <td>12.5</td>\n      <td>7.87</td>\n      <td>0.0</td>\n      <td>0.524</td>\n      <td>6.004</td>\n      <td>85.9</td>\n      <td>6.5921</td>\n      <td>5.0</td>\n      <td>311.0</td>\n      <td>15.2</td>\n      <td>17.10</td>\n      <td>18.9</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 7
    },
    {
      "cell_type": "code",
      "source": "#See data types and if there is null value\nboston_df.info()\n#There is no null value in dataset",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 506 entries, 0 to 505\nData columns (total 14 columns):\n #   Column      Non-Null Count  Dtype  \n---  ------      --------------  -----  \n 0   Unnamed: 0  506 non-null    int64  \n 1   CRIM        506 non-null    float64\n 2   ZN          506 non-null    float64\n 3   INDUS       506 non-null    float64\n 4   CHAS        506 non-null    float64\n 5   NOX         506 non-null    float64\n 6   RM          506 non-null    float64\n 7   AGE         506 non-null    float64\n 8   DIS         506 non-null    float64\n 9   RAD         506 non-null    float64\n 10  TAX         506 non-null    float64\n 11  PTRATIO     506 non-null    float64\n 12  LSTAT       506 non-null    float64\n 13  MEDV        506 non-null    float64\ndtypes: float64(13), int64(1)\nmemory usage: 55.4 KB\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 8
    },
    {
      "cell_type": "code",
      "source": "#Row and column counts\nboston_df.shape",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 9,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(506, 14)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 9
    },
    {
      "cell_type": "code",
      "source": "#See descriptive statistics\nboston_df.describe()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 10,
          "output_type": "execute_result",
          "data": {
            "text/plain": "       Unnamed: 0        CRIM          ZN       INDUS        CHAS         NOX  \\\ncount  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \nmean   252.500000    3.613524   11.363636   11.136779    0.069170    0.554695   \nstd    146.213884    8.601545   23.322453    6.860353    0.253994    0.115878   \nmin      0.000000    0.006320    0.000000    0.460000    0.000000    0.385000   \n25%    126.250000    0.082045    0.000000    5.190000    0.000000    0.449000   \n50%    252.500000    0.256510    0.000000    9.690000    0.000000    0.538000   \n75%    378.750000    3.677083   12.500000   18.100000    0.000000    0.624000   \nmax    505.000000   88.976200  100.000000   27.740000    1.000000    0.871000   \n\n               RM         AGE         DIS         RAD         TAX     PTRATIO  \\\ncount  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \nmean     6.284634   68.574901    3.795043    9.549407  408.237154   18.455534   \nstd      0.702617   28.148861    2.105710    8.707259  168.537116    2.164946   \nmin      3.561000    2.900000    1.129600    1.000000  187.000000   12.600000   \n25%      5.885500   45.025000    2.100175    4.000000  279.000000   17.400000   \n50%      6.208500   77.500000    3.207450    5.000000  330.000000   19.050000   \n75%      6.623500   94.075000    5.188425   24.000000  666.000000   20.200000   \nmax      8.780000  100.000000   12.126500   24.000000  711.000000   22.000000   \n\n            LSTAT        MEDV  \ncount  506.000000  506.000000  \nmean    12.653063   22.532806  \nstd      7.141062    9.197104  \nmin      1.730000    5.000000  \n25%      6.950000   17.025000  \n50%     11.360000   21.200000  \n75%     16.955000   25.000000  \nmax     37.970000   50.000000  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>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    </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>252.500000</td>\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>12.653063</td>\n      <td>22.532806</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>146.213884</td>\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>7.141062</td>\n      <td>9.197104</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\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>1.730000</td>\n      <td>5.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>126.250000</td>\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>6.950000</td>\n      <td>17.025000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>252.500000</td>\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>11.360000</td>\n      <td>21.200000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>378.750000</td>\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>16.955000</td>\n      <td>25.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>505.000000</td>\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>37.970000</td>\n      <td>50.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10
    },
    {
      "cell_type": "code",
      "source": "#Box plot for MEDV value.\nsns.boxplot(x=boston_df['MEDV'])\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": "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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 11
    },
    {
      "cell_type": "code",
      "source": "#bar plot by grouping Charles River variable.\nsns.countplot(boston_df, x='CHAS')",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 12,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='CHAS', ylabel='count'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": "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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 12
    },
    {
      "cell_type": "code",
      "source": "#Creating age_group column\nboston_df.loc[(boston_df['AGE'] <= 35), 'age_group'] = '35 years and younger' \nboston_df.loc[(boston_df['AGE'] > 35)&(boston_df['AGE'] < 70), 'age_group'] = 'between 35 and 70 years'\nboston_df.loc[(boston_df['AGE'] >= 70), 'age_group'] = '70 years and older'\n#Visualizing MEDV vs age_group with Boxplot\nax = sns.boxplot(x='age_group', y='MEDV', data=boston_df)\nplt.show()",
      "metadata": {
        "trusted": true,
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10
    },
    {
      "cell_type": "code",
      "source": "#sCATTER PLOT FOR NOX & INDUS\nax = sns.scatterplot(x='NOX', y='INDUS', data=boston_df)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 29
    },
    {
      "cell_type": "code",
      "source": "#Creating histogram for PTRATIO value.\nplt.hist(boston_df['PTRATIO'], color='pink')",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 34,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(array([ 15.,   2.,  58.,  15.,  35.,  69.,  76.,  40., 178.,  18.]),\n array([12.6 , 13.54, 14.48, 15.42, 16.36, 17.3 , 18.24, 19.18, 20.12,\n        21.06, 22.  ]),\n <BarContainer object of 10 artists>)"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": "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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 34
    },
    {
      "cell_type": "markdown",
      "source": "TASK 3",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "1) Is there a significant difference in median value of houses bounded by the Charles river or not? (T-test for independent samples)\n\nState the hypothesis:\nH0: µ1 = µ2 (There is no significant difference in median values.)\nH1: µ1 ≠ µ2 (There is significant difference in median values.)\n\nWe can use the Levene's Test in Python to check test significance.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "scipy.stats.levene(boston_df[boston_df['CHAS'] == 0]['MEDV'],\n                   boston_df[boston_df['CHAS'] == 1]['MEDV'], center='mean')\n",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 7,
          "output_type": "execute_result",
          "data": {
            "text/plain": "LeveneResult(statistic=8.75190489604598, pvalue=0.003238119367639829)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 7
    },
    {
      "cell_type": "code",
      "source": "scipy.stats.ttest_ind(boston_df[boston_df['CHAS'] == 0]['MEDV'],\n                   boston_df[boston_df['CHAS'] == 1]['MEDV'], equal_var = True)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 8,
          "output_type": "execute_result",
          "data": {
            "text/plain": "TtestResult(statistic=-3.996437466090509, pvalue=7.390623170519905e-05, df=504.0)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 8
    },
    {
      "cell_type": "markdown",
      "source": "Since the p-value is higher than 0.05, we fail to reject the null hypothesis. There is no enough proof to say that there is no difference in median values.",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "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\nWe have already splitted 'AGE' variable in 3 groups before in 'age_group' variable.\n\nState the hypothesis:\nH0: µ1 = µ2 = µ3 (There is no significant difference in median values.)\nH1:  (At leat one of the means differ.)",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#Test for equality of variance\nscipy.stats.levene(boston_df[boston_df['age_group'] == '35 years and younger']['MEDV'],\n                   boston_df[boston_df['age_group'] == 'between 35 and 70 years']['MEDV'], \n                   boston_df[boston_df['age_group'] == '70 years and older']['MEDV'], \n                   center='mean')",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 11,
          "output_type": "execute_result",
          "data": {
            "text/plain": "LeveneResult(statistic=2.7806200293748304, pvalue=0.06295337343259205)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 11
    },
    {
      "cell_type": "code",
      "source": "#Seperating samples in 3\nthirtyfive_lower = boston_df[boston_df['age_group'] == '35 years and younger']['MEDV']\nthirtyfive_seventy = boston_df[boston_df['age_group'] == 'between 35 and 70 years']['MEDV']\nseventy_older = boston_df[boston_df['age_group'] == '70 years and older']['MEDV']",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 12
    },
    {
      "cell_type": "code",
      "source": "#Running one-way ANOVA\nf_statistic, p_value = scipy.stats.f_oneway(thirtyfive_lower, thirtyfive_seventy, seventy_older)\nprint(\"F_Statistic: {0}, P-Value: {1}\".format(f_statistic,p_value))",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "F_Statistic: 36.40764999196599, P-Value: 1.7105011022702984e-15\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 13
    },
    {
      "cell_type": "markdown",
      "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.",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "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\nState the hypothesis:\nH0: Nitric oxide concentrations are not correlated with proportion of non-retail business acres per town.\nH1: Nitric oxide concentrations are correlated with proportion of non-retail business acres per town.\n\nAs proper for Pearson correlation test, both variables are continuous.",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#Creating the scatterplot to see the correlation.\nax = sns.scatterplot(x=\"NOX\", y=\"INDUS\", data=boston_df)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 14
    },
    {
      "cell_type": "code",
      "source": "#Running the pearson test\nscipy.stats.pearsonr(boston_df['NOX'], boston_df['INDUS'])",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 16,
          "output_type": "execute_result",
          "data": {
            "text/plain": "PearsonRResult(statistic=0.7636514469209192, pvalue=7.913361061210442e-98)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 16
    },
    {
      "cell_type": "markdown",
      "source": "Conclusion: Since the p-value is lower than 0.05, we reject the Null hypothesis. Conclude that there exists a relationship between Nitric oxide concentrations and proportion of non-retail business acres per town.",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "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\nDependent variable: median value of owner occupied homes (MEDV)\nIndependent variable: weighted distance to the five Boston employment centres (DIS)",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "## X is the input variables (or independent variables)\nX = boston_df['DIS']\n## y is the target/dependent variable\ny = boston_df['MEDV']\n\n## add an intercept (beta_0) to our model\nX = sm.add_constant(X) \n\nmodel = sm.OLS(y, X).fit()\npredictions = model.predict(X)\n\n# Print out the statistics\nmodel.summary()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 19,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<class 'statsmodels.iolib.summary.Summary'>\n\"\"\"\n                            OLS Regression Results                            \n==============================================================================\nDep. Variable:                   MEDV   R-squared:                       0.062\nModel:                            OLS   Adj. R-squared:                  0.061\nMethod:                 Least Squares   F-statistic:                     33.58\nDate:                Mon, 08 Apr 2024   Prob (F-statistic):           1.21e-08\nTime:                        19:40:26   Log-Likelihood:                -1823.9\nNo. Observations:                 506   AIC:                             3652.\nDf Residuals:                     504   BIC:                             3660.\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nconst         18.3901      0.817     22.499      0.000      16.784      19.996\nDIS            1.0916      0.188      5.795      0.000       0.722       1.462\n==============================================================================\nOmnibus:                      139.779   Durbin-Watson:                   0.570\nProb(Omnibus):                  0.000   Jarque-Bera (JB):              305.104\nSkew:                           1.466   Prob(JB):                     5.59e-67\nKurtosis:                       5.424   Cond. No.                         9.32\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\"",
            "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>Mon, 08 Apr 2024</td> <th>  Prob (F-statistic):</th> <td>1.21e-08</td>\n</tr>\n<tr>\n  <th>Time:</th>                 <td>19:40:26</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:}             & Mon, 08 Apr 2024 & \\textbf{  Prob (F-statistic):} &  1.21e-08   \\\\\n\\textbf{Time:}             &     19:40:26     & \\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\nNotes: \\newline\n [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
          },
          "metadata": {}
        }
      ],
      "execution_count": 19
    },
    {
      "cell_type": "markdown",
      "source": "R-square is 0.062. Correlation coefficient will be 0.25. So, there is weak positive correlation between the DIS - weighted distances to five Boston employment centres and the  MEDV - Median value of owner-occupied homes values.",
      "metadata": {}
    }
  ]
}