IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork_jupyterlite_files_DA0101EN-3-Review-Exploratory-Data-Analysis.jupyterlite.ipynb

{
  "metadata": {
    "anaconda-cloud": {},
    "kernelspec": {
      "name": "python",
      "display_name": "Pyolite",
      "language": "python"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    }
  },
  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "source": "<center>\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Module%203/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n</center>\n\n# Data Analysis with Python\n\nEstimated time needed: **30** minutes\n\n## Objectives\n\nAfter completing this lab you will be able to:\n\n*   Explore features or charecteristics to predict price of car\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2>Table of Contents</h2>\n\n<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n<ol>\n    <li><a href=\"https://#import_data\">Import Data from Module</a></li>\n    <li><a href=\"https://#pattern_visualization\">Analyzing Individual Feature Patterns using Visualization</a></li>\n    <li><a href=\"https://#discriptive_statistics\">Descriptive Statistical Analysis</a></li>\n    <li><a href=\"https://#basic_grouping\">Basics of Grouping</a></li>\n    <li><a href=\"https://#correlation_causation\">Correlation and Causation</a></li>\n    <li><a href=\"https://#anova\">ANOVA</a></li>\n</ol>\n\n</div>\n\n<hr>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>What are the main characteristics that have the most impact on the car price?</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"import_data\">1. Import Data from Module 2</h2>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Setup</h4>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "you are running the lab in your  browser, so we will install the libraries using `piplite`\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#you are running the lab in your  browser, so we will install the libraries using ``piplite``\nimport piplite\nawait piplite.install(['pandas'])\nawait piplite.install(['matplotlib'])\nawait piplite.install(['scipy'])\nawait piplite.install(['seaborn'])\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Import libraries:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "If you run the lab locally using Anaconda, you can load the correct library and versions by uncommenting the following:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#If you run the lab locally using Anaconda, you can load the correct library and versions by uncommenting the following:\n#install specific version of libraries used in lab\n#! mamba install pandas==1.3.3\n#! mamba install numpy=1.21.2\n#! mamba install scipy=1.7.1-y\n#!  mamba install seaborn=0.9.0-y",
      "metadata": {
        "trusted": true
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np",
      "metadata": {
        "trusted": true
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "This function will download the dataset into your browser\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#This function will download the dataset into your browser \n\nfrom pyodide.http import pyfetch\n\nasync def download(url, filename):\n    response = await pyfetch(url)\n    if response.status == 200:\n        with open(filename, \"wb\") as f:\n            f.write(await response.bytes())\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Load the data and store it in dataframe `df`:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "This dataset was hosted on IBM Cloud object. Click <a href=\"https://cocl.us/DA101EN_object_storage?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\">HERE</a> for free storage.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "path='https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/automobileEDA.csv'",
      "metadata": {
        "trusted": true
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "you will need to download the dataset; if you are running locally, please comment out the following\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "\\#you will need to download the dataset; if you are running locally, please comment out the following\nawait download(path, \"auto.csv\")\npath=\"auto.csv\"\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "await download(path, \"auto.csv\")\nfilename=\"auto.csv\"",
      "metadata": {
        "trusted": true
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "\ndf = pd.read_csv(filename)\ndf.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 21,
      "outputs": [
        {
          "execution_count": 21,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   symboling  normalized-losses         make aspiration num-of-doors  \\\n0          3                122  alfa-romero        std          two   \n1          3                122  alfa-romero        std          two   \n2          1                122  alfa-romero        std          two   \n3          2                164         audi        std         four   \n4          2                164         audi        std         four   \n\n    body-style drive-wheels engine-location  wheel-base    length  ...  \\\n0  convertible          rwd           front        88.6  0.811148  ...   \n1  convertible          rwd           front        88.6  0.811148  ...   \n2    hatchback          rwd           front        94.5  0.822681  ...   \n3        sedan          fwd           front        99.8  0.848630  ...   \n4        sedan          4wd           front        99.4  0.848630  ...   \n\n   compression-ratio  horsepower  peak-rpm city-mpg highway-mpg    price  \\\n0                9.0       111.0    5000.0       21          27  13495.0   \n1                9.0       111.0    5000.0       21          27  16500.0   \n2                9.0       154.0    5000.0       19          26  16500.0   \n3               10.0       102.0    5500.0       24          30  13950.0   \n4                8.0       115.0    5500.0       18          22  17450.0   \n\n  city-L/100km  horsepower-binned  diesel  gas  \n0    11.190476             Medium       0    1  \n1    11.190476             Medium       0    1  \n2    12.368421             Medium       0    1  \n3     9.791667             Medium       0    1  \n4    13.055556             Medium       0    1  \n\n[5 rows x 29 columns]",
            "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>symboling</th>\n      <th>normalized-losses</th>\n      <th>make</th>\n      <th>aspiration</th>\n      <th>num-of-doors</th>\n      <th>body-style</th>\n      <th>drive-wheels</th>\n      <th>engine-location</th>\n      <th>wheel-base</th>\n      <th>length</th>\n      <th>...</th>\n      <th>compression-ratio</th>\n      <th>horsepower</th>\n      <th>peak-rpm</th>\n      <th>city-mpg</th>\n      <th>highway-mpg</th>\n      <th>price</th>\n      <th>city-L/100km</th>\n      <th>horsepower-binned</th>\n      <th>diesel</th>\n      <th>gas</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3</td>\n      <td>122</td>\n      <td>alfa-romero</td>\n      <td>std</td>\n      <td>two</td>\n      <td>convertible</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>88.6</td>\n      <td>0.811148</td>\n      <td>...</td>\n      <td>9.0</td>\n      <td>111.0</td>\n      <td>5000.0</td>\n      <td>21</td>\n      <td>27</td>\n      <td>13495.0</td>\n      <td>11.190476</td>\n      <td>Medium</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3</td>\n      <td>122</td>\n      <td>alfa-romero</td>\n      <td>std</td>\n      <td>two</td>\n      <td>convertible</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>88.6</td>\n      <td>0.811148</td>\n      <td>...</td>\n      <td>9.0</td>\n      <td>111.0</td>\n      <td>5000.0</td>\n      <td>21</td>\n      <td>27</td>\n      <td>16500.0</td>\n      <td>11.190476</td>\n      <td>Medium</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>122</td>\n      <td>alfa-romero</td>\n      <td>std</td>\n      <td>two</td>\n      <td>hatchback</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>94.5</td>\n      <td>0.822681</td>\n      <td>...</td>\n      <td>9.0</td>\n      <td>154.0</td>\n      <td>5000.0</td>\n      <td>19</td>\n      <td>26</td>\n      <td>16500.0</td>\n      <td>12.368421</td>\n      <td>Medium</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>164</td>\n      <td>audi</td>\n      <td>std</td>\n      <td>four</td>\n      <td>sedan</td>\n      <td>fwd</td>\n      <td>front</td>\n      <td>99.8</td>\n      <td>0.848630</td>\n      <td>...</td>\n      <td>10.0</td>\n      <td>102.0</td>\n      <td>5500.0</td>\n      <td>24</td>\n      <td>30</td>\n      <td>13950.0</td>\n      <td>9.791667</td>\n      <td>Medium</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>164</td>\n      <td>audi</td>\n      <td>std</td>\n      <td>four</td>\n      <td>sedan</td>\n      <td>4wd</td>\n      <td>front</td>\n      <td>99.4</td>\n      <td>0.848630</td>\n      <td>...</td>\n      <td>8.0</td>\n      <td>115.0</td>\n      <td>5500.0</td>\n      <td>18</td>\n      <td>22</td>\n      <td>17450.0</td>\n      <td>13.055556</td>\n      <td>Medium</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 29 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"pattern_visualization\">2. Analyzing Individual Feature Patterns Using Visualization</h2>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "To install Seaborn we use pip, the Python package manager.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Import visualization packages \"Matplotlib\" and \"Seaborn\". Don't forget about \"%matplotlib inline\" to plot in a Jupyter notebook.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\nimport seaborn\n%matplotlib inline",
      "metadata": {
        "trusted": true
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "<h4>How to choose the right visualization method?</h4>\n<p>When visualizing individual variables, it is important to first understand what type of variable you are dealing with. This will help us find the right visualization method for that variable.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# list the data types for each column\nprint(df.dtypes)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 24,
      "outputs": [
        {
          "name": "stdout",
          "text": "symboling              int64\nnormalized-losses      int64\nmake                  object\naspiration            object\nnum-of-doors          object\nbody-style            object\ndrive-wheels          object\nengine-location       object\nwheel-base           float64\nlength               float64\nwidth                float64\nheight               float64\ncurb-weight            int64\nengine-type           object\nnum-of-cylinders      object\nengine-size            int64\nfuel-system           object\nbore                 float64\nstroke               float64\ncompression-ratio    float64\nhorsepower           float64\npeak-rpm             float64\ncity-mpg               int64\nhighway-mpg            int64\nprice                float64\ncity-L/100km         float64\nhorsepower-binned     object\ndiesel                 int64\ngas                    int64\ndtype: object\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h3>Question  #1:</h3>\n\n<b>What is the data type of the column \"peak-rpm\"? </b>\n\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Write your code below and press Shift+Enter to execute \ndf['peak-rpm'].dtypes",
      "metadata": {
        "trusted": true
      },
      "execution_count": 25,
      "outputs": [
        {
          "execution_count": 25,
          "output_type": "execute_result",
          "data": {
            "text/plain": "dtype('float64')"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\ndf['peak-rpm'].dtypes\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "For example, we can calculate the correlation between variables  of type \"int64\" or \"float64\" using the method \"corr\":\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.corr()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 26,
      "outputs": [
        {
          "execution_count": 26,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                   symboling  normalized-losses  wheel-base    length  \\\nsymboling           1.000000           0.466264   -0.535987 -0.365404   \nnormalized-losses   0.466264           1.000000   -0.056661  0.019424   \nwheel-base         -0.535987          -0.056661    1.000000  0.876024   \nlength             -0.365404           0.019424    0.876024  1.000000   \nwidth              -0.242423           0.086802    0.814507  0.857170   \nheight             -0.550160          -0.373737    0.590742  0.492063   \ncurb-weight        -0.233118           0.099404    0.782097  0.880665   \nengine-size        -0.110581           0.112360    0.572027  0.685025   \nbore               -0.140019          -0.029862    0.493244  0.608971   \nstroke             -0.008245           0.055563    0.158502  0.124139   \ncompression-ratio  -0.182196          -0.114713    0.250313  0.159733   \nhorsepower          0.075819           0.217299    0.371147  0.579821   \npeak-rpm            0.279740           0.239543   -0.360305 -0.285970   \ncity-mpg           -0.035527          -0.225016   -0.470606 -0.665192   \nhighway-mpg         0.036233          -0.181877   -0.543304 -0.698142   \nprice              -0.082391           0.133999    0.584642  0.690628   \ncity-L/100km        0.066171           0.238567    0.476153  0.657373   \ndiesel             -0.196735          -0.101546    0.307237  0.211187   \ngas                 0.196735           0.101546   -0.307237 -0.211187   \n\n                      width    height  curb-weight  engine-size      bore  \\\nsymboling         -0.242423 -0.550160    -0.233118    -0.110581 -0.140019   \nnormalized-losses  0.086802 -0.373737     0.099404     0.112360 -0.029862   \nwheel-base         0.814507  0.590742     0.782097     0.572027  0.493244   \nlength             0.857170  0.492063     0.880665     0.685025  0.608971   \nwidth              1.000000  0.306002     0.866201     0.729436  0.544885   \nheight             0.306002  1.000000     0.307581     0.074694  0.180449   \ncurb-weight        0.866201  0.307581     1.000000     0.849072  0.644060   \nengine-size        0.729436  0.074694     0.849072     1.000000  0.572609   \nbore               0.544885  0.180449     0.644060     0.572609  1.000000   \nstroke             0.188829 -0.062704     0.167562     0.209523 -0.055390   \ncompression-ratio  0.189867  0.259737     0.156433     0.028889  0.001263   \nhorsepower         0.615077 -0.087027     0.757976     0.822676  0.566936   \npeak-rpm          -0.245800 -0.309974    -0.279361    -0.256733 -0.267392   \ncity-mpg          -0.633531 -0.049800    -0.749543    -0.650546 -0.582027   \nhighway-mpg       -0.680635 -0.104812    -0.794889    -0.679571 -0.591309   \nprice              0.751265  0.135486     0.834415     0.872335  0.543155   \ncity-L/100km       0.673363  0.003811     0.785353     0.745059  0.554610   \ndiesel             0.244356  0.281578     0.221046     0.070779  0.054458   \ngas               -0.244356 -0.281578    -0.221046    -0.070779 -0.054458   \n\n                     stroke  compression-ratio  horsepower  peak-rpm  \\\nsymboling         -0.008245          -0.182196    0.075819  0.279740   \nnormalized-losses  0.055563          -0.114713    0.217299  0.239543   \nwheel-base         0.158502           0.250313    0.371147 -0.360305   \nlength             0.124139           0.159733    0.579821 -0.285970   \nwidth              0.188829           0.189867    0.615077 -0.245800   \nheight            -0.062704           0.259737   -0.087027 -0.309974   \ncurb-weight        0.167562           0.156433    0.757976 -0.279361   \nengine-size        0.209523           0.028889    0.822676 -0.256733   \nbore              -0.055390           0.001263    0.566936 -0.267392   \nstroke             1.000000           0.187923    0.098462 -0.065713   \ncompression-ratio  0.187923           1.000000   -0.214514 -0.435780   \nhorsepower         0.098462          -0.214514    1.000000  0.107885   \npeak-rpm          -0.065713          -0.435780    0.107885  1.000000   \ncity-mpg          -0.034696           0.331425   -0.822214 -0.115413   \nhighway-mpg       -0.035201           0.268465   -0.804575 -0.058598   \nprice              0.082310           0.071107    0.809575 -0.101616   \ncity-L/100km       0.037300          -0.299372    0.889488  0.115830   \ndiesel             0.241303           0.985231   -0.169053 -0.475812   \ngas               -0.241303          -0.985231    0.169053  0.475812   \n\n                   city-mpg  highway-mpg     price  city-L/100km    diesel  \\\nsymboling         -0.035527     0.036233 -0.082391      0.066171 -0.196735   \nnormalized-losses -0.225016    -0.181877  0.133999      0.238567 -0.101546   \nwheel-base        -0.470606    -0.543304  0.584642      0.476153  0.307237   \nlength            -0.665192    -0.698142  0.690628      0.657373  0.211187   \nwidth             -0.633531    -0.680635  0.751265      0.673363  0.244356   \nheight            -0.049800    -0.104812  0.135486      0.003811  0.281578   \ncurb-weight       -0.749543    -0.794889  0.834415      0.785353  0.221046   \nengine-size       -0.650546    -0.679571  0.872335      0.745059  0.070779   \nbore              -0.582027    -0.591309  0.543155      0.554610  0.054458   \nstroke            -0.034696    -0.035201  0.082310      0.037300  0.241303   \ncompression-ratio  0.331425     0.268465  0.071107     -0.299372  0.985231   \nhorsepower        -0.822214    -0.804575  0.809575      0.889488 -0.169053   \npeak-rpm          -0.115413    -0.058598 -0.101616      0.115830 -0.475812   \ncity-mpg           1.000000     0.972044 -0.686571     -0.949713  0.265676   \nhighway-mpg        0.972044     1.000000 -0.704692     -0.930028  0.198690   \nprice             -0.686571    -0.704692  1.000000      0.789898  0.110326   \ncity-L/100km      -0.949713    -0.930028  0.789898      1.000000 -0.241282   \ndiesel             0.265676     0.198690  0.110326     -0.241282  1.000000   \ngas               -0.265676    -0.198690 -0.110326      0.241282 -1.000000   \n\n                        gas  \nsymboling          0.196735  \nnormalized-losses  0.101546  \nwheel-base        -0.307237  \nlength            -0.211187  \nwidth             -0.244356  \nheight            -0.281578  \ncurb-weight       -0.221046  \nengine-size       -0.070779  \nbore              -0.054458  \nstroke            -0.241303  \ncompression-ratio -0.985231  \nhorsepower         0.169053  \npeak-rpm           0.475812  \ncity-mpg          -0.265676  \nhighway-mpg       -0.198690  \nprice             -0.110326  \ncity-L/100km       0.241282  \ndiesel            -1.000000  \ngas                1.000000  ",
            "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>symboling</th>\n      <th>normalized-losses</th>\n      <th>wheel-base</th>\n      <th>length</th>\n      <th>width</th>\n      <th>height</th>\n      <th>curb-weight</th>\n      <th>engine-size</th>\n      <th>bore</th>\n      <th>stroke</th>\n      <th>compression-ratio</th>\n      <th>horsepower</th>\n      <th>peak-rpm</th>\n      <th>city-mpg</th>\n      <th>highway-mpg</th>\n      <th>price</th>\n      <th>city-L/100km</th>\n      <th>diesel</th>\n      <th>gas</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>symboling</th>\n      <td>1.000000</td>\n      <td>0.466264</td>\n      <td>-0.535987</td>\n      <td>-0.365404</td>\n      <td>-0.242423</td>\n      <td>-0.550160</td>\n      <td>-0.233118</td>\n      <td>-0.110581</td>\n      <td>-0.140019</td>\n      <td>-0.008245</td>\n      <td>-0.182196</td>\n      <td>0.075819</td>\n      <td>0.279740</td>\n      <td>-0.035527</td>\n      <td>0.036233</td>\n      <td>-0.082391</td>\n      <td>0.066171</td>\n      <td>-0.196735</td>\n      <td>0.196735</td>\n    </tr>\n    <tr>\n      <th>normalized-losses</th>\n      <td>0.466264</td>\n      <td>1.000000</td>\n      <td>-0.056661</td>\n      <td>0.019424</td>\n      <td>0.086802</td>\n      <td>-0.373737</td>\n      <td>0.099404</td>\n      <td>0.112360</td>\n      <td>-0.029862</td>\n      <td>0.055563</td>\n      <td>-0.114713</td>\n      <td>0.217299</td>\n      <td>0.239543</td>\n      <td>-0.225016</td>\n      <td>-0.181877</td>\n      <td>0.133999</td>\n      <td>0.238567</td>\n      <td>-0.101546</td>\n      <td>0.101546</td>\n    </tr>\n    <tr>\n      <th>wheel-base</th>\n      <td>-0.535987</td>\n      <td>-0.056661</td>\n      <td>1.000000</td>\n      <td>0.876024</td>\n      <td>0.814507</td>\n      <td>0.590742</td>\n      <td>0.782097</td>\n      <td>0.572027</td>\n      <td>0.493244</td>\n      <td>0.158502</td>\n      <td>0.250313</td>\n      <td>0.371147</td>\n      <td>-0.360305</td>\n      <td>-0.470606</td>\n      <td>-0.543304</td>\n      <td>0.584642</td>\n      <td>0.476153</td>\n      <td>0.307237</td>\n      <td>-0.307237</td>\n    </tr>\n    <tr>\n      <th>length</th>\n      <td>-0.365404</td>\n      <td>0.019424</td>\n      <td>0.876024</td>\n      <td>1.000000</td>\n      <td>0.857170</td>\n      <td>0.492063</td>\n      <td>0.880665</td>\n      <td>0.685025</td>\n      <td>0.608971</td>\n      <td>0.124139</td>\n      <td>0.159733</td>\n      <td>0.579821</td>\n      <td>-0.285970</td>\n      <td>-0.665192</td>\n      <td>-0.698142</td>\n      <td>0.690628</td>\n      <td>0.657373</td>\n      <td>0.211187</td>\n      <td>-0.211187</td>\n    </tr>\n    <tr>\n      <th>width</th>\n      <td>-0.242423</td>\n      <td>0.086802</td>\n      <td>0.814507</td>\n      <td>0.857170</td>\n      <td>1.000000</td>\n      <td>0.306002</td>\n      <td>0.866201</td>\n      <td>0.729436</td>\n      <td>0.544885</td>\n      <td>0.188829</td>\n      <td>0.189867</td>\n      <td>0.615077</td>\n      <td>-0.245800</td>\n      <td>-0.633531</td>\n      <td>-0.680635</td>\n      <td>0.751265</td>\n      <td>0.673363</td>\n      <td>0.244356</td>\n      <td>-0.244356</td>\n    </tr>\n    <tr>\n      <th>height</th>\n      <td>-0.550160</td>\n      <td>-0.373737</td>\n      <td>0.590742</td>\n      <td>0.492063</td>\n      <td>0.306002</td>\n      <td>1.000000</td>\n      <td>0.307581</td>\n      <td>0.074694</td>\n      <td>0.180449</td>\n      <td>-0.062704</td>\n      <td>0.259737</td>\n      <td>-0.087027</td>\n      <td>-0.309974</td>\n      <td>-0.049800</td>\n      <td>-0.104812</td>\n      <td>0.135486</td>\n      <td>0.003811</td>\n      <td>0.281578</td>\n      <td>-0.281578</td>\n    </tr>\n    <tr>\n      <th>curb-weight</th>\n      <td>-0.233118</td>\n      <td>0.099404</td>\n      <td>0.782097</td>\n      <td>0.880665</td>\n      <td>0.866201</td>\n      <td>0.307581</td>\n      <td>1.000000</td>\n      <td>0.849072</td>\n      <td>0.644060</td>\n      <td>0.167562</td>\n      <td>0.156433</td>\n      <td>0.757976</td>\n      <td>-0.279361</td>\n      <td>-0.749543</td>\n      <td>-0.794889</td>\n      <td>0.834415</td>\n      <td>0.785353</td>\n      <td>0.221046</td>\n      <td>-0.221046</td>\n    </tr>\n    <tr>\n      <th>engine-size</th>\n      <td>-0.110581</td>\n      <td>0.112360</td>\n      <td>0.572027</td>\n      <td>0.685025</td>\n      <td>0.729436</td>\n      <td>0.074694</td>\n      <td>0.849072</td>\n      <td>1.000000</td>\n      <td>0.572609</td>\n      <td>0.209523</td>\n      <td>0.028889</td>\n      <td>0.822676</td>\n      <td>-0.256733</td>\n      <td>-0.650546</td>\n      <td>-0.679571</td>\n      <td>0.872335</td>\n      <td>0.745059</td>\n      <td>0.070779</td>\n      <td>-0.070779</td>\n    </tr>\n    <tr>\n      <th>bore</th>\n      <td>-0.140019</td>\n      <td>-0.029862</td>\n      <td>0.493244</td>\n      <td>0.608971</td>\n      <td>0.544885</td>\n      <td>0.180449</td>\n      <td>0.644060</td>\n      <td>0.572609</td>\n      <td>1.000000</td>\n      <td>-0.055390</td>\n      <td>0.001263</td>\n      <td>0.566936</td>\n      <td>-0.267392</td>\n      <td>-0.582027</td>\n      <td>-0.591309</td>\n      <td>0.543155</td>\n      <td>0.554610</td>\n      <td>0.054458</td>\n      <td>-0.054458</td>\n    </tr>\n    <tr>\n      <th>stroke</th>\n      <td>-0.008245</td>\n      <td>0.055563</td>\n      <td>0.158502</td>\n      <td>0.124139</td>\n      <td>0.188829</td>\n      <td>-0.062704</td>\n      <td>0.167562</td>\n      <td>0.209523</td>\n      <td>-0.055390</td>\n      <td>1.000000</td>\n      <td>0.187923</td>\n      <td>0.098462</td>\n      <td>-0.065713</td>\n      <td>-0.034696</td>\n      <td>-0.035201</td>\n      <td>0.082310</td>\n      <td>0.037300</td>\n      <td>0.241303</td>\n      <td>-0.241303</td>\n    </tr>\n    <tr>\n      <th>compression-ratio</th>\n      <td>-0.182196</td>\n      <td>-0.114713</td>\n      <td>0.250313</td>\n      <td>0.159733</td>\n      <td>0.189867</td>\n      <td>0.259737</td>\n      <td>0.156433</td>\n      <td>0.028889</td>\n      <td>0.001263</td>\n      <td>0.187923</td>\n      <td>1.000000</td>\n      <td>-0.214514</td>\n      <td>-0.435780</td>\n      <td>0.331425</td>\n      <td>0.268465</td>\n      <td>0.071107</td>\n      <td>-0.299372</td>\n      <td>0.985231</td>\n      <td>-0.985231</td>\n    </tr>\n    <tr>\n      <th>horsepower</th>\n      <td>0.075819</td>\n      <td>0.217299</td>\n      <td>0.371147</td>\n      <td>0.579821</td>\n      <td>0.615077</td>\n      <td>-0.087027</td>\n      <td>0.757976</td>\n      <td>0.822676</td>\n      <td>0.566936</td>\n      <td>0.098462</td>\n      <td>-0.214514</td>\n      <td>1.000000</td>\n      <td>0.107885</td>\n      <td>-0.822214</td>\n      <td>-0.804575</td>\n      <td>0.809575</td>\n      <td>0.889488</td>\n      <td>-0.169053</td>\n      <td>0.169053</td>\n    </tr>\n    <tr>\n      <th>peak-rpm</th>\n      <td>0.279740</td>\n      <td>0.239543</td>\n      <td>-0.360305</td>\n      <td>-0.285970</td>\n      <td>-0.245800</td>\n      <td>-0.309974</td>\n      <td>-0.279361</td>\n      <td>-0.256733</td>\n      <td>-0.267392</td>\n      <td>-0.065713</td>\n      <td>-0.435780</td>\n      <td>0.107885</td>\n      <td>1.000000</td>\n      <td>-0.115413</td>\n      <td>-0.058598</td>\n      <td>-0.101616</td>\n      <td>0.115830</td>\n      <td>-0.475812</td>\n      <td>0.475812</td>\n    </tr>\n    <tr>\n      <th>city-mpg</th>\n      <td>-0.035527</td>\n      <td>-0.225016</td>\n      <td>-0.470606</td>\n      <td>-0.665192</td>\n      <td>-0.633531</td>\n      <td>-0.049800</td>\n      <td>-0.749543</td>\n      <td>-0.650546</td>\n      <td>-0.582027</td>\n      <td>-0.034696</td>\n      <td>0.331425</td>\n      <td>-0.822214</td>\n      <td>-0.115413</td>\n      <td>1.000000</td>\n      <td>0.972044</td>\n      <td>-0.686571</td>\n      <td>-0.949713</td>\n      <td>0.265676</td>\n      <td>-0.265676</td>\n    </tr>\n    <tr>\n      <th>highway-mpg</th>\n      <td>0.036233</td>\n      <td>-0.181877</td>\n      <td>-0.543304</td>\n      <td>-0.698142</td>\n      <td>-0.680635</td>\n      <td>-0.104812</td>\n      <td>-0.794889</td>\n      <td>-0.679571</td>\n      <td>-0.591309</td>\n      <td>-0.035201</td>\n      <td>0.268465</td>\n      <td>-0.804575</td>\n      <td>-0.058598</td>\n      <td>0.972044</td>\n      <td>1.000000</td>\n      <td>-0.704692</td>\n      <td>-0.930028</td>\n      <td>0.198690</td>\n      <td>-0.198690</td>\n    </tr>\n    <tr>\n      <th>price</th>\n      <td>-0.082391</td>\n      <td>0.133999</td>\n      <td>0.584642</td>\n      <td>0.690628</td>\n      <td>0.751265</td>\n      <td>0.135486</td>\n      <td>0.834415</td>\n      <td>0.872335</td>\n      <td>0.543155</td>\n      <td>0.082310</td>\n      <td>0.071107</td>\n      <td>0.809575</td>\n      <td>-0.101616</td>\n      <td>-0.686571</td>\n      <td>-0.704692</td>\n      <td>1.000000</td>\n      <td>0.789898</td>\n      <td>0.110326</td>\n      <td>-0.110326</td>\n    </tr>\n    <tr>\n      <th>city-L/100km</th>\n      <td>0.066171</td>\n      <td>0.238567</td>\n      <td>0.476153</td>\n      <td>0.657373</td>\n      <td>0.673363</td>\n      <td>0.003811</td>\n      <td>0.785353</td>\n      <td>0.745059</td>\n      <td>0.554610</td>\n      <td>0.037300</td>\n      <td>-0.299372</td>\n      <td>0.889488</td>\n      <td>0.115830</td>\n      <td>-0.949713</td>\n      <td>-0.930028</td>\n      <td>0.789898</td>\n      <td>1.000000</td>\n      <td>-0.241282</td>\n      <td>0.241282</td>\n    </tr>\n    <tr>\n      <th>diesel</th>\n      <td>-0.196735</td>\n      <td>-0.101546</td>\n      <td>0.307237</td>\n      <td>0.211187</td>\n      <td>0.244356</td>\n      <td>0.281578</td>\n      <td>0.221046</td>\n      <td>0.070779</td>\n      <td>0.054458</td>\n      <td>0.241303</td>\n      <td>0.985231</td>\n      <td>-0.169053</td>\n      <td>-0.475812</td>\n      <td>0.265676</td>\n      <td>0.198690</td>\n      <td>0.110326</td>\n      <td>-0.241282</td>\n      <td>1.000000</td>\n      <td>-1.000000</td>\n    </tr>\n    <tr>\n      <th>gas</th>\n      <td>0.196735</td>\n      <td>0.101546</td>\n      <td>-0.307237</td>\n      <td>-0.211187</td>\n      <td>-0.244356</td>\n      <td>-0.281578</td>\n      <td>-0.221046</td>\n      <td>-0.070779</td>\n      <td>-0.054458</td>\n      <td>-0.241303</td>\n      <td>-0.985231</td>\n      <td>0.169053</td>\n      <td>0.475812</td>\n      <td>-0.265676</td>\n      <td>-0.198690</td>\n      <td>-0.110326</td>\n      <td>0.241282</td>\n      <td>-1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "The diagonal elements are always one; we will study correlation more precisely Pearson correlation in-depth at the end of the notebook.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h3> Question  #2: </h3>\n\n<p>Find the correlation between the following columns: bore, stroke, compression-ratio, and horsepower.</p>\n<p>Hint: if you would like to select those columns, use the following syntax: df[['bore','stroke','compression-ratio','horsepower']]</p>\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Write your code below and press Shift+Enter to execute \ndf[['bore', 'stroke', 'compression-ratio', 'horsepower']].corr()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 27,
      "outputs": [
        {
          "execution_count": 27,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                       bore    stroke  compression-ratio  horsepower\nbore               1.000000 -0.055390           0.001263    0.566936\nstroke            -0.055390  1.000000           0.187923    0.098462\ncompression-ratio  0.001263  0.187923           1.000000   -0.214514\nhorsepower         0.566936  0.098462          -0.214514    1.000000",
            "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>bore</th>\n      <th>stroke</th>\n      <th>compression-ratio</th>\n      <th>horsepower</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>bore</th>\n      <td>1.000000</td>\n      <td>-0.055390</td>\n      <td>0.001263</td>\n      <td>0.566936</td>\n    </tr>\n    <tr>\n      <th>stroke</th>\n      <td>-0.055390</td>\n      <td>1.000000</td>\n      <td>0.187923</td>\n      <td>0.098462</td>\n    </tr>\n    <tr>\n      <th>compression-ratio</th>\n      <td>0.001263</td>\n      <td>0.187923</td>\n      <td>1.000000</td>\n      <td>-0.214514</td>\n    </tr>\n    <tr>\n      <th>horsepower</th>\n      <td>0.566936</td>\n      <td>0.098462</td>\n      <td>-0.214514</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\ndf[['bore', 'stroke', 'compression-ratio', 'horsepower']].corr()\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2>Continuous Numerical Variables:</h2> \n\n<p>Continuous numerical variables are variables that may contain any value within some range. They can be of type \"int64\" or \"float64\". A great way to visualize these variables is by using scatterplots with fitted lines.</p>\n\n<p>In order to start understanding the (linear) relationship between an individual variable and the price, we can use \"regplot\" which plots the scatterplot plus the fitted regression line for the data.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's see several examples of different linear relationships:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Positive Linear Relationship</h4>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's find the scatterplot of \"engine-size\" and \"price\".\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Engine size as potential predictor variable of price\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n# Engine size as potential predictor variable of price\nsns.regplot(x=\"engine-size\", y=\"price\", data=df)\nplt.ylim(0,)\n",
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 29,
      "outputs": [
        {
          "execution_count": 29,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(0.0, 53302.62626342584)"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>As the engine-size goes up, the price goes up: this indicates a positive direct correlation between these two variables. Engine size seems like a pretty good predictor of price since the regression line is almost a perfect diagonal line.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can examine the correlation between 'engine-size' and 'price' and see that it's approximately 0.87.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df[[\"engine-size\", \"price\"]].corr()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 30,
      "outputs": [
        {
          "execution_count": 30,
          "output_type": "execute_result",
          "data": {
            "text/plain": "             engine-size     price\nengine-size     1.000000  0.872335\nprice           0.872335  1.000000",
            "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>engine-size</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>engine-size</th>\n      <td>1.000000</td>\n      <td>0.872335</td>\n    </tr>\n    <tr>\n      <th>price</th>\n      <td>0.872335</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Highway mpg is a potential predictor variable of price. Let's find the scatterplot of \"highway-mpg\" and \"price\".\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "sns.regplot(x=\"highway-mpg\", y=\"price\", data=df)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 31,
      "outputs": [
        {
          "execution_count": 31,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='highway-mpg', ylabel='price'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>As highway-mpg goes up, the price goes down: this indicates an inverse/negative relationship between these two variables. Highway mpg could potentially be a predictor of price.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can examine the correlation between 'highway-mpg' and 'price' and see it's approximately -0.704.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df[['highway-mpg', 'price']].corr()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 32,
      "outputs": [
        {
          "execution_count": 32,
          "output_type": "execute_result",
          "data": {
            "text/plain": "             highway-mpg     price\nhighway-mpg     1.000000 -0.704692\nprice          -0.704692  1.000000",
            "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>highway-mpg</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>highway-mpg</th>\n      <td>1.000000</td>\n      <td>-0.704692</td>\n    </tr>\n    <tr>\n      <th>price</th>\n      <td>-0.704692</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Weak Linear Relationship</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's see if \"peak-rpm\" is a predictor variable of \"price\".\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "sns.regplot(x=\"peak-rpm\", y=\"price\", data=df)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 33,
      "outputs": [
        {
          "execution_count": 33,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='peak-rpm', ylabel='price'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>Peak rpm does not seem like a good predictor of the price at all since the regression line is close to horizontal. Also, the data points are very scattered and far from the fitted line, showing lots of variability. Therefore, it's not a reliable variable.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can examine the correlation between 'peak-rpm' and 'price' and see it's approximately -0.101616.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df[['peak-rpm','price']].corr()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 34,
      "outputs": [
        {
          "execution_count": 34,
          "output_type": "execute_result",
          "data": {
            "text/plain": "          peak-rpm     price\npeak-rpm  1.000000 -0.101616\nprice    -0.101616  1.000000",
            "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>peak-rpm</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>peak-rpm</th>\n      <td>1.000000</td>\n      <td>-0.101616</td>\n    </tr>\n    <tr>\n      <th>price</th>\n      <td>-0.101616</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": " <div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h1> Question  3 a): </h1>\n\n<p>Find the correlation  between x=\"stroke\" and y=\"price\".</p>\n<p>Hint: if you would like to select those columns, use the following syntax: df[[\"stroke\",\"price\"]].  </p>\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Write your code below and press Shift+Enter to execute\ndf[[\"stroke\", \"price\"]].corr()\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 36,
      "outputs": [
        {
          "execution_count": 36,
          "output_type": "execute_result",
          "data": {
            "text/plain": "         stroke    price\nstroke  1.00000  0.08231\nprice   0.08231  1.00000",
            "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>stroke</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>stroke</th>\n      <td>1.00000</td>\n      <td>0.08231</td>\n    </tr>\n    <tr>\n      <th>price</th>\n      <td>0.08231</td>\n      <td>1.00000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\n\n#The correlation is 0.0823, the non-diagonal elements of the table.\n\ndf[[\"stroke\",\"price\"]].corr()\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h1>Question  3 b):</h1>\n\n<p>Given the correlation results between \"price\" and \"stroke\", do you expect a linear relationship?</p> \n<p>Verify your results using the function \"regplot()\".</p>\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Write your code below and press Shift+Enter to execute \nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nsns.regplot(x=\"stroke\", y=\"price\", data=df)\nplt.ylim(0,)\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 37,
      "outputs": [
        {
          "execution_count": 37,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(0.0, 47414.1)"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\n\n#There is a weak correlation between the variable 'stroke' and 'price.' as such regression will not work well. We can see this using \"regplot\" to demonstrate this.\n\n#Code: \nsns.regplot(x=\"stroke\", y=\"price\", data=df)\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Categorical Variables</h3>\n\n<p>These are variables that describe a 'characteristic' of a data unit, and are selected from a small group of categories. The categorical variables can have the type \"object\" or \"int64\". A good way to visualize categorical variables is by using boxplots.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's look at the relationship between \"body-style\" and \"price\".\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "sns.boxplot(x=\"body-style\", y=\"price\", data=df)",
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 38,
      "outputs": [
        {
          "execution_count": 38,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='body-style', ylabel='price'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>We see that the distributions of price between the different body-style categories have a significant overlap, so body-style would not be a good predictor of price. Let's examine engine \"engine-location\" and \"price\":</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "sns.boxplot(x=\"engine-location\", y=\"price\", data=df)",
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 39,
      "outputs": [
        {
          "execution_count": 39,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='engine-location', ylabel='price'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>Here we see that the distribution of price between these two engine-location categories, front and rear, are distinct enough to take engine-location as a potential good predictor of price.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's examine \"drive-wheels\" and \"price\".\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# drive-wheels\nsns.boxplot(x=\"drive-wheels\", y=\"price\", data=df)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 40,
      "outputs": [
        {
          "execution_count": 40,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='drive-wheels', ylabel='price'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>Here we see that the distribution of price between the different drive-wheels categories differs. As such, drive-wheels could potentially be a predictor of price.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"discriptive_statistics\">3. Descriptive Statistical Analysis</h2>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>Let's first take a look at the variables by utilizing a description method.</p>\n\n<p>The <b>describe</b> function automatically computes basic statistics for all continuous variables. Any NaN values are automatically skipped in these statistics.</p>\n\nThis will show:\n\n<ul>\n    <li>the count of that variable</li>\n    <li>the mean</li>\n    <li>the standard deviation (std)</li> \n    <li>the minimum value</li>\n    <li>the IQR (Interquartile Range: 25%, 50% and 75%)</li>\n    <li>the maximum value</li>\n<ul>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can apply the method \"describe\" as follows:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.describe()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 41,
      "outputs": [
        {
          "execution_count": 41,
          "output_type": "execute_result",
          "data": {
            "text/plain": "        symboling  normalized-losses  wheel-base      length       width  \\\ncount  201.000000          201.00000  201.000000  201.000000  201.000000   \nmean     0.840796          122.00000   98.797015    0.837102    0.915126   \nstd      1.254802           31.99625    6.066366    0.059213    0.029187   \nmin     -2.000000           65.00000   86.600000    0.678039    0.837500   \n25%      0.000000          101.00000   94.500000    0.801538    0.890278   \n50%      1.000000          122.00000   97.000000    0.832292    0.909722   \n75%      2.000000          137.00000  102.400000    0.881788    0.925000   \nmax      3.000000          256.00000  120.900000    1.000000    1.000000   \n\n           height  curb-weight  engine-size        bore      stroke  \\\ncount  201.000000   201.000000   201.000000  201.000000  197.000000   \nmean    53.766667  2555.666667   126.875622    3.330692    3.256904   \nstd      2.447822   517.296727    41.546834    0.268072    0.319256   \nmin     47.800000  1488.000000    61.000000    2.540000    2.070000   \n25%     52.000000  2169.000000    98.000000    3.150000    3.110000   \n50%     54.100000  2414.000000   120.000000    3.310000    3.290000   \n75%     55.500000  2926.000000   141.000000    3.580000    3.410000   \nmax     59.800000  4066.000000   326.000000    3.940000    4.170000   \n\n       compression-ratio  horsepower     peak-rpm    city-mpg  highway-mpg  \\\ncount         201.000000  201.000000   201.000000  201.000000   201.000000   \nmean           10.164279  103.405534  5117.665368   25.179104    30.686567   \nstd             4.004965   37.365700   478.113805    6.423220     6.815150   \nmin             7.000000   48.000000  4150.000000   13.000000    16.000000   \n25%             8.600000   70.000000  4800.000000   19.000000    25.000000   \n50%             9.000000   95.000000  5125.369458   24.000000    30.000000   \n75%             9.400000  116.000000  5500.000000   30.000000    34.000000   \nmax            23.000000  262.000000  6600.000000   49.000000    54.000000   \n\n              price  city-L/100km      diesel         gas  \ncount    201.000000    201.000000  201.000000  201.000000  \nmean   13207.129353      9.944145    0.099502    0.900498  \nstd     7947.066342      2.534599    0.300083    0.300083  \nmin     5118.000000      4.795918    0.000000    0.000000  \n25%     7775.000000      7.833333    0.000000    1.000000  \n50%    10295.000000      9.791667    0.000000    1.000000  \n75%    16500.000000     12.368421    0.000000    1.000000  \nmax    45400.000000     18.076923    1.000000    1.000000  ",
            "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>symboling</th>\n      <th>normalized-losses</th>\n      <th>wheel-base</th>\n      <th>length</th>\n      <th>width</th>\n      <th>height</th>\n      <th>curb-weight</th>\n      <th>engine-size</th>\n      <th>bore</th>\n      <th>stroke</th>\n      <th>compression-ratio</th>\n      <th>horsepower</th>\n      <th>peak-rpm</th>\n      <th>city-mpg</th>\n      <th>highway-mpg</th>\n      <th>price</th>\n      <th>city-L/100km</th>\n      <th>diesel</th>\n      <th>gas</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>201.000000</td>\n      <td>201.00000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>197.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n      <td>201.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.840796</td>\n      <td>122.00000</td>\n      <td>98.797015</td>\n      <td>0.837102</td>\n      <td>0.915126</td>\n      <td>53.766667</td>\n      <td>2555.666667</td>\n      <td>126.875622</td>\n      <td>3.330692</td>\n      <td>3.256904</td>\n      <td>10.164279</td>\n      <td>103.405534</td>\n      <td>5117.665368</td>\n      <td>25.179104</td>\n      <td>30.686567</td>\n      <td>13207.129353</td>\n      <td>9.944145</td>\n      <td>0.099502</td>\n      <td>0.900498</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1.254802</td>\n      <td>31.99625</td>\n      <td>6.066366</td>\n      <td>0.059213</td>\n      <td>0.029187</td>\n      <td>2.447822</td>\n      <td>517.296727</td>\n      <td>41.546834</td>\n      <td>0.268072</td>\n      <td>0.319256</td>\n      <td>4.004965</td>\n      <td>37.365700</td>\n      <td>478.113805</td>\n      <td>6.423220</td>\n      <td>6.815150</td>\n      <td>7947.066342</td>\n      <td>2.534599</td>\n      <td>0.300083</td>\n      <td>0.300083</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-2.000000</td>\n      <td>65.00000</td>\n      <td>86.600000</td>\n      <td>0.678039</td>\n      <td>0.837500</td>\n      <td>47.800000</td>\n      <td>1488.000000</td>\n      <td>61.000000</td>\n      <td>2.540000</td>\n      <td>2.070000</td>\n      <td>7.000000</td>\n      <td>48.000000</td>\n      <td>4150.000000</td>\n      <td>13.000000</td>\n      <td>16.000000</td>\n      <td>5118.000000</td>\n      <td>4.795918</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>101.00000</td>\n      <td>94.500000</td>\n      <td>0.801538</td>\n      <td>0.890278</td>\n      <td>52.000000</td>\n      <td>2169.000000</td>\n      <td>98.000000</td>\n      <td>3.150000</td>\n      <td>3.110000</td>\n      <td>8.600000</td>\n      <td>70.000000</td>\n      <td>4800.000000</td>\n      <td>19.000000</td>\n      <td>25.000000</td>\n      <td>7775.000000</td>\n      <td>7.833333</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1.000000</td>\n      <td>122.00000</td>\n      <td>97.000000</td>\n      <td>0.832292</td>\n      <td>0.909722</td>\n      <td>54.100000</td>\n      <td>2414.000000</td>\n      <td>120.000000</td>\n      <td>3.310000</td>\n      <td>3.290000</td>\n      <td>9.000000</td>\n      <td>95.000000</td>\n      <td>5125.369458</td>\n      <td>24.000000</td>\n      <td>30.000000</td>\n      <td>10295.000000</td>\n      <td>9.791667</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2.000000</td>\n      <td>137.00000</td>\n      <td>102.400000</td>\n      <td>0.881788</td>\n      <td>0.925000</td>\n      <td>55.500000</td>\n      <td>2926.000000</td>\n      <td>141.000000</td>\n      <td>3.580000</td>\n      <td>3.410000</td>\n      <td>9.400000</td>\n      <td>116.000000</td>\n      <td>5500.000000</td>\n      <td>30.000000</td>\n      <td>34.000000</td>\n      <td>16500.000000</td>\n      <td>12.368421</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>3.000000</td>\n      <td>256.00000</td>\n      <td>120.900000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>59.800000</td>\n      <td>4066.000000</td>\n      <td>326.000000</td>\n      <td>3.940000</td>\n      <td>4.170000</td>\n      <td>23.000000</td>\n      <td>262.000000</td>\n      <td>6600.000000</td>\n      <td>49.000000</td>\n      <td>54.000000</td>\n      <td>45400.000000</td>\n      <td>18.076923</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "The default setting of \"describe\" skips variables of type object. We can apply the method \"describe\" on the variables of type 'object' as follows:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.describe(include=['object'])",
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 42,
      "outputs": [
        {
          "execution_count": 42,
          "output_type": "execute_result",
          "data": {
            "text/plain": "          make aspiration num-of-doors body-style drive-wheels  \\\ncount      201        201          201        201          201   \nunique      22          2            2          5            3   \ntop     toyota        std         four      sedan          fwd   \nfreq        32        165          115         94          118   \n\n       engine-location engine-type num-of-cylinders fuel-system  \\\ncount              201         201              201         201   \nunique               2           6                7           8   \ntop              front         ohc             four        mpfi   \nfreq               198         145              157          92   \n\n       horsepower-binned  \ncount                200  \nunique                 3  \ntop                  Low  \nfreq                 115  ",
            "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>make</th>\n      <th>aspiration</th>\n      <th>num-of-doors</th>\n      <th>body-style</th>\n      <th>drive-wheels</th>\n      <th>engine-location</th>\n      <th>engine-type</th>\n      <th>num-of-cylinders</th>\n      <th>fuel-system</th>\n      <th>horsepower-binned</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>201</td>\n      <td>201</td>\n      <td>201</td>\n      <td>201</td>\n      <td>201</td>\n      <td>201</td>\n      <td>201</td>\n      <td>201</td>\n      <td>201</td>\n      <td>200</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>22</td>\n      <td>2</td>\n      <td>2</td>\n      <td>5</td>\n      <td>3</td>\n      <td>2</td>\n      <td>6</td>\n      <td>7</td>\n      <td>8</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>toyota</td>\n      <td>std</td>\n      <td>four</td>\n      <td>sedan</td>\n      <td>fwd</td>\n      <td>front</td>\n      <td>ohc</td>\n      <td>four</td>\n      <td>mpfi</td>\n      <td>Low</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>32</td>\n      <td>165</td>\n      <td>115</td>\n      <td>94</td>\n      <td>118</td>\n      <td>198</td>\n      <td>145</td>\n      <td>157</td>\n      <td>92</td>\n      <td>115</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Value Counts</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>Value counts is a good way of understanding how many units of each characteristic/variable we have. We can apply the \"value_counts\" method on the column \"drive-wheels\". Don’t forget the method \"value_counts\" only works on pandas series, not pandas dataframes. As a result, we only include one bracket <code>df['drive-wheels']</code>, not two brackets <code>df[['drive-wheels']]</code>.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df['drive-wheels'].value_counts()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 43,
      "outputs": [
        {
          "execution_count": 43,
          "output_type": "execute_result",
          "data": {
            "text/plain": "fwd    118\nrwd     75\n4wd      8\nName: drive-wheels, dtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can convert the series to a dataframe as follows:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df['drive-wheels'].value_counts().to_frame()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 44,
      "outputs": [
        {
          "execution_count": 44,
          "output_type": "execute_result",
          "data": {
            "text/plain": "     drive-wheels\nfwd           118\nrwd            75\n4wd             8",
            "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>drive-wheels</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>fwd</th>\n      <td>118</td>\n    </tr>\n    <tr>\n      <th>rwd</th>\n      <td>75</td>\n    </tr>\n    <tr>\n      <th>4wd</th>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Let's repeat the above steps but save the results to the dataframe \"drive_wheels_counts\" and rename the column  'drive-wheels' to 'value_counts'.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "drive_wheels_counts = df['drive-wheels'].value_counts().to_frame()\ndrive_wheels_counts.rename(columns={'drive-wheels': 'value_counts'}, inplace=True)\ndrive_wheels_counts",
      "metadata": {
        "trusted": true
      },
      "execution_count": 45,
      "outputs": [
        {
          "execution_count": 45,
          "output_type": "execute_result",
          "data": {
            "text/plain": "     value_counts\nfwd           118\nrwd            75\n4wd             8",
            "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>value_counts</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>fwd</th>\n      <td>118</td>\n    </tr>\n    <tr>\n      <th>rwd</th>\n      <td>75</td>\n    </tr>\n    <tr>\n      <th>4wd</th>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Now let's rename the index to 'drive-wheels':\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "drive_wheels_counts.index.name = 'drive-wheels'\ndrive_wheels_counts",
      "metadata": {
        "trusted": true
      },
      "execution_count": 46,
      "outputs": [
        {
          "execution_count": 46,
          "output_type": "execute_result",
          "data": {
            "text/plain": "              value_counts\ndrive-wheels              \nfwd                    118\nrwd                     75\n4wd                      8",
            "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>value_counts</th>\n    </tr>\n    <tr>\n      <th>drive-wheels</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>fwd</th>\n      <td>118</td>\n    </tr>\n    <tr>\n      <th>rwd</th>\n      <td>75</td>\n    </tr>\n    <tr>\n      <th>4wd</th>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can repeat the above process for the variable 'engine-location'.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# engine-location as variable\nengine_loc_counts = df['engine-location'].value_counts().to_frame()\nengine_loc_counts.rename(columns={'engine-location': 'value_counts'}, inplace=True)\nengine_loc_counts.index.name = 'engine-location'\nengine_loc_counts.head(10)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 47,
      "outputs": [
        {
          "execution_count": 47,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                 value_counts\nengine-location              \nfront                     198\nrear                        3",
            "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>value_counts</th>\n    </tr>\n    <tr>\n      <th>engine-location</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>front</th>\n      <td>198</td>\n    </tr>\n    <tr>\n      <th>rear</th>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>After examining the value counts of the engine location, we see that engine location would not be a good predictor variable for the price. This is because we only have three cars with a rear engine and 198 with an engine in the front, so this result is skewed. Thus, we are not able to draw any conclusions about the engine location.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"basic_grouping\">4. Basics of Grouping</h2>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>The \"groupby\" method groups data by different categories. The data is grouped based on one or several variables, and analysis is performed on the individual groups.</p>\n\n<p>For example, let's group by the variable \"drive-wheels\". We see that there are 3 different categories of drive wheels.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df['drive-wheels'].unique()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 48,
      "outputs": [
        {
          "execution_count": 48,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array(['rwd', 'fwd', '4wd'], dtype=object)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>If we want to know, on average, which type of drive wheel is most valuable, we can group \"drive-wheels\" and then average them.</p>\n\n<p>We can select the columns 'drive-wheels', 'body-style' and 'price', then assign it to the variable \"df_group_one\".</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df_group_one = df[['drive-wheels','body-style','price']]",
      "metadata": {
        "trusted": true
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "We can then calculate the average price for each of the different categories of data.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# grouping results\ndf_group_one = df_group_one.groupby(['drive-wheels'],as_index=False).mean()\ndf_group_one",
      "metadata": {
        "trusted": true
      },
      "execution_count": 50,
      "outputs": [
        {
          "execution_count": 50,
          "output_type": "execute_result",
          "data": {
            "text/plain": "  drive-wheels         price\n0          4wd  10241.000000\n1          fwd   9244.779661\n2          rwd  19757.613333",
            "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>drive-wheels</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4wd</td>\n      <td>10241.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>fwd</td>\n      <td>9244.779661</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>rwd</td>\n      <td>19757.613333</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>From our data, it seems rear-wheel drive vehicles are, on average, the most expensive, while 4-wheel and front-wheel are approximately the same in price.</p>\n\n<p>You can also group by multiple variables. For example, let's group by both 'drive-wheels' and 'body-style'. This groups the dataframe by the unique combination of 'drive-wheels' and 'body-style'. We can store the results in the variable 'grouped_test1'.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# grouping results\ndf_gptest = df[['drive-wheels','body-style','price']]\ngrouped_test1 = df_gptest.groupby(['drive-wheels','body-style'],as_index=False).mean()\ngrouped_test1",
      "metadata": {
        "trusted": true
      },
      "execution_count": 51,
      "outputs": [
        {
          "execution_count": 51,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   drive-wheels   body-style         price\n0           4wd    hatchback   7603.000000\n1           4wd        sedan  12647.333333\n2           4wd        wagon   9095.750000\n3           fwd  convertible  11595.000000\n4           fwd      hardtop   8249.000000\n5           fwd    hatchback   8396.387755\n6           fwd        sedan   9811.800000\n7           fwd        wagon   9997.333333\n8           rwd  convertible  23949.600000\n9           rwd      hardtop  24202.714286\n10          rwd    hatchback  14337.777778\n11          rwd        sedan  21711.833333\n12          rwd        wagon  16994.222222",
            "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>drive-wheels</th>\n      <th>body-style</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4wd</td>\n      <td>hatchback</td>\n      <td>7603.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4wd</td>\n      <td>sedan</td>\n      <td>12647.333333</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4wd</td>\n      <td>wagon</td>\n      <td>9095.750000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>fwd</td>\n      <td>convertible</td>\n      <td>11595.000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>fwd</td>\n      <td>hardtop</td>\n      <td>8249.000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>fwd</td>\n      <td>hatchback</td>\n      <td>8396.387755</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>fwd</td>\n      <td>sedan</td>\n      <td>9811.800000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>fwd</td>\n      <td>wagon</td>\n      <td>9997.333333</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>rwd</td>\n      <td>convertible</td>\n      <td>23949.600000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>rwd</td>\n      <td>hardtop</td>\n      <td>24202.714286</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>rwd</td>\n      <td>hatchback</td>\n      <td>14337.777778</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>rwd</td>\n      <td>sedan</td>\n      <td>21711.833333</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>rwd</td>\n      <td>wagon</td>\n      <td>16994.222222</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>This grouped data is much easier to visualize when it is made into a pivot table. A pivot table is like an Excel spreadsheet, with one variable along the column and another along the row. We can convert the dataframe to a pivot table using the method \"pivot\" to create a pivot table from the groups.</p>\n\n<p>In this case, we will leave the drive-wheels variable as the rows of the table, and pivot body-style to become the columns of the table:</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "grouped_pivot = grouped_test1.pivot(index='drive-wheels',columns='body-style')\ngrouped_pivot",
      "metadata": {
        "trusted": true
      },
      "execution_count": 52,
      "outputs": [
        {
          "execution_count": 52,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                   price                                            \\\nbody-style   convertible       hardtop     hatchback         sedan   \ndrive-wheels                                                         \n4wd                  NaN           NaN   7603.000000  12647.333333   \nfwd              11595.0   8249.000000   8396.387755   9811.800000   \nrwd              23949.6  24202.714286  14337.777778  21711.833333   \n\n                            \nbody-style           wagon  \ndrive-wheels                \n4wd            9095.750000  \nfwd            9997.333333  \nrwd           16994.222222  ",
            "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"5\" halign=\"left\">price</th>\n    </tr>\n    <tr>\n      <th>body-style</th>\n      <th>convertible</th>\n      <th>hardtop</th>\n      <th>hatchback</th>\n      <th>sedan</th>\n      <th>wagon</th>\n    </tr>\n    <tr>\n      <th>drive-wheels</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4wd</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>7603.000000</td>\n      <td>12647.333333</td>\n      <td>9095.750000</td>\n    </tr>\n    <tr>\n      <th>fwd</th>\n      <td>11595.0</td>\n      <td>8249.000000</td>\n      <td>8396.387755</td>\n      <td>9811.800000</td>\n      <td>9997.333333</td>\n    </tr>\n    <tr>\n      <th>rwd</th>\n      <td>23949.6</td>\n      <td>24202.714286</td>\n      <td>14337.777778</td>\n      <td>21711.833333</td>\n      <td>16994.222222</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>Often, we won't have data for some of the pivot cells. We can fill these missing cells with the value 0, but any other value could potentially be used as well. It should be mentioned that missing data is quite a complex subject and is an entire course on its own.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "grouped_pivot = grouped_pivot.fillna(0) #fill missing values with 0\ngrouped_pivot",
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 53,
      "outputs": [
        {
          "execution_count": 53,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                   price                                            \\\nbody-style   convertible       hardtop     hatchback         sedan   \ndrive-wheels                                                         \n4wd                  0.0      0.000000   7603.000000  12647.333333   \nfwd              11595.0   8249.000000   8396.387755   9811.800000   \nrwd              23949.6  24202.714286  14337.777778  21711.833333   \n\n                            \nbody-style           wagon  \ndrive-wheels                \n4wd            9095.750000  \nfwd            9997.333333  \nrwd           16994.222222  ",
            "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"5\" halign=\"left\">price</th>\n    </tr>\n    <tr>\n      <th>body-style</th>\n      <th>convertible</th>\n      <th>hardtop</th>\n      <th>hatchback</th>\n      <th>sedan</th>\n      <th>wagon</th>\n    </tr>\n    <tr>\n      <th>drive-wheels</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4wd</th>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>7603.000000</td>\n      <td>12647.333333</td>\n      <td>9095.750000</td>\n    </tr>\n    <tr>\n      <th>fwd</th>\n      <td>11595.0</td>\n      <td>8249.000000</td>\n      <td>8396.387755</td>\n      <td>9811.800000</td>\n      <td>9997.333333</td>\n    </tr>\n    <tr>\n      <th>rwd</th>\n      <td>23949.6</td>\n      <td>24202.714286</td>\n      <td>14337.777778</td>\n      <td>21711.833333</td>\n      <td>16994.222222</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<div class=\"alert alert-danger alertdanger\" style=\"margin-top: 20px\">\n<h1>Question 4:</h1>\n\n<p>Use the \"groupby\" function to find the average \"price\" of each car based on \"body-style\".</p>\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Write your code below and press Shift+Enter to execute \n# grouping results\ndf.groupby('body-style')['price'].mean()\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 54,
      "outputs": [
        {
          "execution_count": 54,
          "output_type": "execute_result",
          "data": {
            "text/plain": "body-style\nconvertible    21890.500000\nhardtop        22208.500000\nhatchback       9957.441176\nsedan          14459.755319\nwagon          12371.960000\nName: price, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\n# grouping results\ndf_gptest2 = df[['body-style','price']]\ngrouped_test_bodystyle = df_gptest2.groupby(['body-style'],as_index= False).mean()\ngrouped_test_bodystyle\n\n```\n\n</details>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "If you did not import \"pyplot\", let's do it again.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\n%matplotlib inline ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Variables: Drive Wheels and Body Style vs. Price</h4>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's use a heat map to visualize the relationship between Body Style vs Price.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#use the grouped results\nplt.pcolor(grouped_pivot, cmap='RdBu')\nplt.colorbar()\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x54e1b70>",
            "image/png": 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"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>The heatmap plots the target variable (price) proportional to colour with respect to the variables 'drive-wheel' and 'body-style' on the vertical and horizontal axis, respectively. This allows us to visualize how the price is related to 'drive-wheel' and 'body-style'.</p>\n\n<p>The default labels convey no useful information to us. Let's change that:</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "fig, ax = plt.subplots()\nim = ax.pcolor(grouped_pivot, cmap='RdBu')\n\n#label names\nrow_labels = grouped_pivot.columns.levels[1]\ncol_labels = grouped_pivot.index\n\n#move ticks and labels to the center\nax.set_xticks(np.arange(grouped_pivot.shape[1]) + 0.5, minor=False)\nax.set_yticks(np.arange(grouped_pivot.shape[0]) + 0.5, minor=False)\n\n#insert labels\nax.set_xticklabels(row_labels, minor=False)\nax.set_yticklabels(col_labels, minor=False)\n\n#rotate label if too long\nplt.xticks(rotation=90)\n\nfig.colorbar(im)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x54fdfe0>",
            "image/png": 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GYREIgiEAAgBgGAIggiEAAgBgmAiXvRAXwQQx4/FXDAAAEGboAAIAYBiGgBEMARAAAMMQABEMQ8AAAABhhg4gAACG4ULQCIYACACAYSIsy9bt3LgVnPkYAgYAAAgzdAABADCMy7LkstHFs3MuQgMBEAAAw0RIirCR4SLOWiVoqBgCBgAACDN0AAEAMIzLZdlaycsqYPMRAAEAMAyrgBEMARAAAMOwCATBMAcQAAAgzNABBADAMC7L3ipgpgCajwAIAIBhWASCYAiAZ8Gev06T2+12uozQs+YvTlcQurYtcbqCkLZjUprTJYQsn6vC6RJCVtXBav3O6SKAfyEAAgBgGBaBIBgCIAAAhomwOQfQzrkIDawCBgAACDN0AAEAMAxDwAiGAAgAgGEiXJYibKzktXMuQgNDwAAAAGGGDiAAAIZhCBjBEAABADAMq4ARDAEQAADDWDY7gBYdQOMxBxAAACDM0AEEAMAwrAJGMARAAAAM45JkJ8MxPGg+/o4BAIAtK1eu1KBBg5ScnCzLsrRkyZKAx++8805ZlhWwXXfddQHHHD58WGPGjFHLli3VvHlzDR06VGVlZQHHlJeXa/jw4XK73YqLi9PIkSNVXV0dcExJSYkGDhyopk2bKiEhQZMmTVJdXd05ed+hjAAIAIBhIizL9nYmampqlJGRoeeee+4Hj7nuuuu0b98+//bmm28GPD5+/Hi99957evvtt1VQUKC9e/dqyJAhAccMHz5cW7du1bJly5SXl6eVK1dq1KhR/sc9Ho8GDhyo2tpaffLJJ3rttdc0f/58TZs27YzeTzhgCBgAAMOc7+sAXn/99br++utPe0x0dLQSExNP+VhlZaVefvllLVy4UNdee60k6dVXX1V6erpWr16tnj17atu2bVq6dKnWrl2r7t27S5KeffZZDRgwQE8++aSSk5P1wQcf6LPPPtOHH36oNm3aqGvXrvr973+vBx98UNOnT1dUVNQZvS+T0QEEAACnVFVVFbAdOXKk3s+Vn5+vhIQEXXrppRo9erS+/fZb/2PFxcU6evSosrOz/fvS0tLUtm1bFRYWSpIKCwsVFxfnD3+SlJ2dLZfLpaKiIv8xnTt3Vps2bfzH5OTkqKqqSlu3bq137SYiAAIAYJgIl/1NklJSUhQbG+vfZsyYUa96rrvuOr3++utavny5/vjHP6qgoEDXX3+9PB6PJKm0tFRRUVGKi4sLOK9NmzYqLS31H5OQkBDweGRkpOLj4wOOOTH8HX+O44/h3xgCBgDAMC7L3u3cjq8g3r17t9xut39/dHR0vZ5v2LBh/j937txZXbp00UUXXaT8/Hz169ev3nWi/ugAAgCAU3K73QFbfQPg97Vv316tWrXSzp07JUmJiYmqra1VRUVFwHFlZWX+eYOJiYnav39/wON1dXUqLy8POOb7K4eP//xD8w/DFQEQAADDuGyuALbTPfwx9uzZo2+//VZJSUmSpG7duqlRo0Zavny5/5jt27erpKREmZmZkqTMzExVVFSouLjYf8yKFSvk9XrVo0cP/zGbN28OCIrLli2T2+1Wx44dz+l7CjUMAQMAYJjzvQq4urra382TpF27dmnjxo2Kj49XfHy8Hn30UQ0dOlSJiYn68ssvNXnyZHXo0EE5OTmSpNjYWI0cOVK5ubmKj4+X2+3W/fffr8zMTPXs2VOSlJ6eruuuu0733nuv5s2bp6NHj2rs2LEaNmyYkpOTJUn9+/dXx44ddccdd2jWrFkqLS3VlClTNGbMmLPWvTQFARAAAMOcuJCjvuefiXXr1qlv377+n3NzcyVJI0aM0AsvvKBPP/1Ur732mioqKpScnKz+/fvr97//fUAomz17tlwul4YOHaojR44oJydHzz//fMDrLFiwQGPHjlW/fv38x86dO/ffdUdEKC8vT6NHj1ZmZqaaNWumESNG6LHHHqvHp2A2y+fz+ZwuIlRVVVUpNjZW+3fvCpgkix9pzV+criB0uSKcriCkNWqX5nQJIcvnom9QX1UHqxWf0UeVlZXn7Dvj+PfSR9v+qeYx9X+N6oNV+ml6u3NaK5zFf8kAABjmfA8BI/QQAAEAMIxlHdvsnA+zsQoYAAAgzNABBADAMC5ZcsnGELCNcxEaCIAAABiGIWAEwxAwAABAmKEDCACAYY7dC9je+TAbARAAAMMwBIxgGAIGAAAIM3QAAQAwDKuAEQwBEAAA09gcAib/mY8ACACAYVgEgmCYAwgAABBm6AACAGAYS/ZGcWkAmo8ACACAYVyWJZeNSYB2zkVoYAgYAAAgzNABBADAMJZsXgj6rFWChooACACAYVyyN8TH8KD5+DsGAAAIM3QAAQAwjGVZsmyMAds5F6GBAAgAgGG4EDSCYQgYAAAgzNABBADAMJbNewEzAmw+AiAAAIZhFTCCIQACAGAYFoEgGEI+AABAmKEDCACAYVgFjGAIgAAAGIgMh9NhCBgAACDM0AEEAMAwDAEjGAIgAACGYRUwgmEIGAAAIMyEdAD0+XwaNWqU4uPjZVmWNm7caOv57rzzTg0ePPis1AYAgFOODwHb2WC2kB4CXrp0qebPn6/8/Hy1b99erVq1crokAAAcZ8neKmDyn/lCOgB++eWXSkpK0tVXX+10KQAAACEjZIeA77zzTt1///0qKSmRZVlq1aqVbrjhBv/jc+bMkWVZWrp0qX9fhw4d9NJLL0mSPB6PcnNzFRcXp5YtW2ry5Mny+Xzn/X0AAHC2uSzL9gazhWwAfOaZZ/TYY4/pggsu0L59+zR79mytWrVKHo9HklRQUKBWrVopPz9fkvT111/ryy+/VFZWliTpqaee0vz58/XKK69o1apVKi8v1+LFi0/7mkeOHFFVVVXABgBAQ2NZ9jeYLWQDYGxsrGJiYhQREaHExEQNGjRIBw8e1IYNG+Tz+bRy5UpNmDDBHwDz8/P1k5/8RB06dJB0rEP40EMPaciQIUpPT9e8efMUGxt72tecMWOGYmNj/VtKSsq5fpsAAJwxy+ezvcFsIRsAvy8uLk4ZGRnKz8/X5s2bFRUVpVGjRmnDhg2qrq5WQUGB+vTpI0mqrKzUvn371KNHD//5kZGR6t69+2lf46GHHlJlZaV/27179zl9TwAAAOdCSC8C+b6srCzl5+crOjpaffr0UXx8vNLT07Vq1SoVFBRowoQJtp4/Ojpa0dHRZ6laAADOEZ/32GbnfBjNmA6gJPXp00erVq3S8uXL/XP9srKy9Oabb+qLL77w74uNjVVSUpKKior859bV1am4uNiBqgEAOLssn9f2BrMZFQB79+6tgwcPKi8vLyAALliwQElJSbrkkkv8x44bN04zZ87UkiVL9Pnnn+s3v/mNKioqnCkcAADgPDJqCLhFixbq3LmzysrKlJaWJulYKPR6vf75f8dNmDBB+/bt04gRI+RyuXT33Xfr5ptvVmVlpROlAwBw9jAEjCAsHxe/q7eqqirFxsZq/+5dcrvdTpcTetb8xekKQpcrwukKQlqjdmlOlxCyfC6j+gbnVdXBasVn9FFlZeU5+87wfy/t+aet16iqqlLCBe3Oaa1wllFDwAAAAAiOX+UAADANQ8AIggAIAIBhjl3Muf4hjgtBm48hYAAAgDBDBxAAANMwBIwgCIAAAJiGAIggCIAAAJiGAIggmAMIAAAQZugAAgBgGp9X8tIBxA8jAAIAYBjL57V5GRgCoOkYAgYAAAgzdAABADANi0AQBAEQAADT+HzHNjvnw2gMAQMAAIQZOoAAAJiGIWAEQQAEAMAwls9ncxUwQ8CmYwgYAAAgzNABBADANAwBIwgCIAAApiEAIggCIAAApiEAIgjmAAIAAIQZAiAAAIY5fi9gO9uZWLlypQYNGqTk5GRZlqUlS5YEPO7z+TRt2jQlJSWpSZMmys7O1o4dOwKOOXz4sMaMGaOWLVuqefPmGjp0qMrKygKOKS8v1/Dhw+V2uxUXF6eRI0equro64JiSkhINHDhQTZs2VUJCgiZNmqS6urozej/hgAAIAIBpvF772xmoqalRRkaGnnvuuVM+PmvWLM2dO1fz5s1TUVGRmjVrppycHB0+fNh/zPjx4/Xee+/p7bffVkFBgfbu3ashQ4YEPM/w4cO1detWLVu2THl5eVq5cqVGjRrlf9zj8WjgwIGqra3VJ598otdee03z58/XtGnTzuj9hAPL5+NiP/VVVVWl2NhY7d+9S2632+lyQs+avzhdQehyRThdQUhr1C7N6RJCls/F1PH6qjpYrfiMPqqsrDxn3xnHv5fKN6+SO6Z5/Z/nYLXiO/eqV62WZWnx4sUaPHiwpGPdv+TkZE2YMEETJ06UJFVWVqpNmzaaP3++hg0bpsrKSrVu3VoLFy7ULbfcIkn6/PPPlZ6ersLCQvXs2VPbtm1Tx44dtXbtWnXv3l2StHTpUg0YMEB79uxRcnKy/v73v+uGG27Q3r171aZNG0nSvHnz9OCDD+rAgQOKioqq92diGjqAAACY5vi9gO1sOhYoT9yOHDlyxqXs2rVLpaWlys7O9u+LjY1Vjx49VFhYKEkqLi7W0aNHA45JS0tT27Zt/ccUFhYqLi7OH/4kKTs7Wy6XS0VFRf5jOnfu7A9/kpSTk6Oqqipt3br1jGs3GQEQAADTHF8FbGeTlJKSotjYWP82Y8aMMy6ltLRUkgJC2fGfjz9WWlqqqKgoxcXFnfaYhISEgMcjIyMVHx8fcMypXufEOnAMvXwAAHBKu3fvDhgCjo6OdrAanE10AAEAMMzZWgXsdrsDtvoEwMTEREk6aUVvWVmZ/7HExETV1taqoqLitMfs378/4PG6ujqVl5cHHHOq1zmxDhxDAAQAwDRnaQj4bEhNTVViYqKWL1/u31dVVaWioiJlZmZKkrp166ZGjRoFHLN9+3aVlJT4j8nMzFRFRYWKi4v9x6xYsUJer1c9evTwH7N58+aAoLhs2TK53W517NjxrL0nEzAEDACAaXw+m3cCObMLhFRXV2vnzp3+n3ft2qWNGzcqPj5ebdu21QMPPKDHH39cF198sVJTUzV16lQlJyf7VwrHxsZq5MiRys3NVXx8vNxut+6//35lZmaqZ8+ekqT09HRdd911uvfeezVv3jwdPXpUY8eO1bBhw5ScnCxJ6t+/vzp27Kg77rhDs2bNUmlpqaZMmaIxY8YwfP09BEAAAGDLunXr1LdvX//Pubm5kqQRI0Zo/vz5mjx5smpqajRq1ChVVFSoV69eWrp0qRo3buw/Z/bs2XK5XBo6dKiOHDminJwcPf/88wGvs2DBAo0dO1b9+vXzHzt37lz/4xEREcrLy9Po0aOVmZmpZs2aacSIEXrsscfO8ScQergOoA1cB9AmrgNYf1wH0BauA1h/XAew/s7ndQD/r/h9uZs3q//zVNeoRbecc1ornMV/yQAAGMbn9cp3hnfz+P75MBuLQAAAAMIMHUAAAEzj9Rzb7JwPoxEAAQAwDQEQQTAEDAAAEGboAAIAYBifxyOfp/5dPDvnIjQQAAEAMI3Xe2yzcz6MxhAwAABAmKEDCACAabxem4tA6ACajgAIAIBhfF6PfDYCoJ1zERoIgAAAmMZncw6gjw6g6ZgDCAAAEGboAAIAYBiGgBEMARAAANNwJxAEwRAwAABAmKEDCACAabgQNIIgAAIAYBhuBYdgGAIGAAAIM3QAAQAwDXcCQRAEQAAATMMqYATBEDAAAECYoQMIAIBhfF6vfDaGce2ci9BAAAQAwDQMASMIAuBZ8GBKb0Uxmo7z6Ol3xjpdQkj77qpbnC4hZDXfsdLpEkKWr7rmPL6YzQDoIwCajtQCAAAQZugAAgBgGOYAIhgCIAAApuE6gAiCIWAAAIAwQwcQAADTsAoYQRAAAQAwjM/jkc9T/xBn51yEBoaAAQAAwgwdQAAATOP12lvIwSIQ4xEAAQAwDXMAEQRDwAAAAGGGDiAAAIbxeT3y2eji2TkXoYEACACAYbgTCIIhAAIAYBif1yefx04A9J3FatAQMQcQAAAgzNABBADAMD6P114H0Ma5CA0EQAAADMMcQATDEDAAAECYoQMIAIBhGAJGMARAAAAMQwBEMAwBAwAAhBk6gAAAGMbn8cjrsXEnEBvnIjQQAAEAMIzPZ3MVsI8hYNMxBAwAABBm6AACAGAYFoEgGAIgAACGIQAiGAIgAACG8Xl9Nu8E4juL1aAhYg4gAABAmKEDCACAYbwer7w2hnHtnIvQQAAEAMAwzAFEMAwBAwAAhBk6gAAAGIYOIIIhAAIAYBjuBIJgGAIGAAAIM3QAAQAwDEPACIYACACAYQiACIYhYAAAgDBDBxAAAMN4vV55bSwCsXMuQgMBEAAAwzAEjGAIgAAAGOZYAPTYOh9mYw4gAABAmKEDCACAYXxemxeCZg6g8egAAgBgGJ/X658HWK/tDAPg9OnTZVlWwJaWlvbvenw+TZs2TUlJSWrSpImys7O1Y8eOgOc4fPiwxowZo5YtW6p58+YaOnSoysrKAo4pLy/X8OHD5Xa7FRcXp5EjR6q6urr+H1QYIwACAADbLrvsMu3bt8+/rVq1yv/YrFmzNHfuXM2bN09FRUVq1qyZcnJydPjwYf8x48eP13vvvae3335bBQUF2rt3r4YMGRLwGsOHD9fWrVu1bNky5eXlaeXKlRo1atR5e48mYQgYAADT2FwFrHqcGxkZqcTExJP2+3w+zZkzR1OmTNFNN90kSXr99dfVpk0bLVmyRMOGDVNlZaVefvllLVy4UNdee60k6dVXX1V6erpWr16tnj17atu2bVq6dKnWrl2r7t27S5KeffZZDRgwQE8++aSSk5Pr/37DEB1AAAAM4/V4bW9naseOHUpOTlb79u01fPhwlZSUSJJ27dql0tJSZWdn+4+NjY1Vjx49VFhYKEkqLi7W0aNHA45JS0tT27Zt/ccUFhYqLi7OH/4kKTs7Wy6XS0VFRfX6nMIZHUAAAHBKVVVVAT9HR0crOjr6pON69Oih+fPn69JLL9W+ffv06KOP6qc//am2bNmi0tJSSVKbNm0CzmnTpo3/sdLSUkVFRSkuLu60xyQkJAQ8HhkZqfj4eP8x+PEIgAAAGOZsrQJOSUkJ2P/II49o+vTpJx1//fXX+//cpUsX9ejRQ+3atdNbb72l9PT0eteBc4cACACAYc7WnUB2794tt9vt33+q7t+pxMXF6ZJLLtHOnTvVt29fSVJZWZmSkpL8x5SVlalr166SpMTERNXW1qqioiKgC1hWVuafV5iYmKj9+/cHvE5dXZ3Ky8tPOfcQp8ccQAAAcEputztg+7EBsLq6Wl9++aWSkpKUmpqqxMRELV++3P94VVWVioqKlJmZKUnq1q2bGjVqFHDM9u3bVVJS4j8mMzNTFRUVKi4u9h+zYsUKeb1e9ejR42y83bBCBxAAAMP4PD75PD5b55+JiRMnatCgQWrXrp327t2rRx55RBEREbrttttkWZYeeOABPf7447r44ouVmpqqqVOnKjk5WYMHD5Z0bFHIyJEjlZubq/j4eLndbt1///3KzMxUz549JUnp6em67rrrdO+992revHk6evSoxo4dq2HDhrECuB4IgAAAGMbrrd9K3hPPPxN79uzRbbfdpm+//VatW7dWr169tHr1arVu3VqSNHnyZNXU1GjUqFGqqKhQr169tHTpUjVu3Nj/HLNnz5bL5dLQoUN15MgR5eTk6Pnnnw94nQULFmjs2LHq16+f/9i5c+fW+32GM8vn89X/V4QwV1VVpdjYWN2lFEUxmo7z6Ol3xjpdQkirzfmN0yWErOY7VjpdQsiqqq5Ry963qLKyMmBe3Vl9jX99L30yNFvNGzWq9/NUHz2qq9/98JzWCmeRWgAAAMIMQ8AAABjG65G8rvoP8Hk9Z7EYNEgEQAAADOPzeOVz2b8MDMzFEPAJpk+f7r8mEQAAgKlCNgDOnDnTv7QcAAD82/HLwNjZYLaQHAJeu3at/uM//kNdunRxuhQAABocr8dncw4gAdB0IdcBrK6u1vDhw/Xiiy+qRYsW/v1//vOf1alTJ//PS5YskWVZmjdvnn9fdna2pkyZ4v955syZatOmjWJiYjRy5EgdPnz4/LwJAAAAB4VcABwzZowGDhyo7OzsgP19+vTRZ599pgMHDkiSCgoK1KpVK+Xn50uSjh49qsLCQmVlZUmS3nrrLU2fPl1PPPGE1q1bp6SkpJMuOAkAQCg6fi9gOxvMFlIBcNGiRVq/fr1mzJhx0mOdOnVSfHy8CgoKJEn5+fmaMGGC/+c1a9bo6NGjuvrqqyVJc+bM0ciRIzVy5Ehdeumlevzxx9WxY8fTvv6RI0dUVVUVsAEA0NB4fT55vTY27hFhvJAJgLt379a4ceO0YMGCgFvHHGdZlnr37q38/HxVVFTos88+029+8xsdOXJEn3/+uQoKCnTllVeqadOmkqRt27addPPo4zec/iEzZsxQbGysf0tJSTl7bxAAAOA8CZkAWFxcrP379+uKK65QZGSkIiMjVVBQoLlz5yoyMlIej0dZWVnKz8/XRx99pMsvv1xut9sfCgsKCtSnTx9bNTz00EOqrKz0b7t37z5L7w4AgLPI7gpgFoEYL2QCYL9+/bR582Zt3LjRv3Xv3l3Dhw/Xxo0bFRER4Z8H+Pbbb/vn+mVlZenDDz/Uxx9/7N8nSenp6SoqKgp4jdWrV5+2hujoaLnd7oANAICGxuvx2t5gtpC5DExMTEzAKl9JatasmVq2bOnf36VLF7Vo0UILFy5UXl6epGMBcOLEibIsS9dcc43/3HHjxunOO+9U9+7ddc0112jBggXaunWr2rdvf/7eFAAA54DP45PPqn8Xj+sAmi9kOoA/hmVZ+ulPfyrLstSrVy9Jx0Kh2+1W9+7d1axZM/+xt956q6ZOnarJkyerW7du+uc//6nRo0c7VToAAMB5EzIdwFM5fomXEy1ZsiTgZ5fLpfLy8lOe//DDD+vhhx8O2PfHP/7xbJUHAIAj6AAimJAOgAAA4GRej1deq/7z+JgDaD6jhoABAAAQHB1AAAAM4/P55PPaGALmQtDGIwACAGAYr8cnr+of4rzMATQeQ8AAAABhhg4gAACG8Xl88qn+CzlYBWw+AiAAAIY5FgC5DAx+GEPAAAAAYYYOIAAAhmERCIIhAAIAYBif1yufZdk6H2YjAAIAYBg6gAiGOYAAAABhhg4gAACG8XltrgK2cRcRhAYCIAAApvF45fPVfw6gmANoPIaAAQAAwgwdQAAADOP1+OT12VgEwhCw8QiAAAAYxufxyWcjADIH0HwMAQMAAIQZOoAAABjG67M5BGzjXIQGAiAAAIbx+Hzy2Ahxds5FaGAIGAAAIMzQAQQAwDAe37HNzvkwGwEQAADDMASMYAiAAAAYhg4ggmEOIAAAQJihAwgAgGG8NoeAuQyM+QiAAAAYxiObQ8BnrRI0VAwBAwAAhBk6gAAAGMbj88kjVgHjhxEAAQAwjMdnbxiXVcDmYwgYAAAgzNABBADAMHQAEQwBEAAAwzAHEMEwBAwAABBm6AACAGAYr80hYC8NQOMRAAEAMAxDwAiGAAgAgGFYBIJgmAMIAAAQZugAAgBgmGMdQDtDwGexGDRIBEAAAAzDEDCCYQgYAAAgzNABBADAMKwCRjAEQAAADOOT5LV5PszGEDAAAECYoQMIAIBhGAJGMARAAAAMwypgBMMQMAAAQJihAwgAgGEYAkYwBEAAAAzDEDCCIQACAGAYOoAIhjmAAAAAYYYACACAYby+fw0D13Pz1rMB+Nxzz+nCCy9U48aN1aNHD61Zs+bsvjGcNQRAAAAM4/H5bG9n6r/+67+Um5urRx55ROvXr1dGRoZycnK0f//+c/AOYRcBEAAA2Pb000/r3nvv1V133aWOHTtq3rx5atq0qV555RWnS8MpsAjEBt+/fkOqtXXHReDMVR067HQJIa22qsrpEkKWt7rG6RJCVlXNIUn//u44l76T19ZK3uPfa1Xf+28lOjpa0dHRJx9fW6vi4mI99NBD/n0ul0vZ2dkqLCysfyE4ZwiANhw8eFCStEBfO1wJws2rv5rqdAkhjs8Pzjl48KBiY2PPyXNHRUUpMTFRC0rtfy81b95cKSkpAfseeeQRTZ8+/aRjv/nmG3k8HrVp0yZgf5s2bfT555/brgVnHwHQhuTkZO3evVsxMTGyLMvpck5SVVWllJQU7d69W2632+lyQgqfXf3x2dnD51d/Df2z8/l8OnjwoJKTk8/ZazRu3Fi7du1SbW2t7efy+XwnfbedqvuH0EQAtMHlcumCCy5wuoyg3G53g/yfYSjgs6s/Pjt7+PzqryF/dueq83eixo0bq3Hjxuf8dU7UqlUrRUREqKysLGB/WVmZEhMTz2st+HFYBAIAAGyJiopSt27dtHz5cv8+r9er5cuXKzMz08HK8EPoAAIAANtyc3M1YsQIde/eXVdddZXmzJmjmpoa3XXXXU6XhlMgABosOjpajzzyCHM26oHPrv747Ozh86s/Pjtn3XrrrTpw4ICmTZum0tJSde3aVUuXLj1pYQgaBst3PtajAwAAoMFgDiAAAECYIQACAACEGQIgAABAmCEAAgAAhBkCIAAAQJghABpo586dev/99/Xdd99JOj83HgeOHj36g499880357ESAEAwXAbGIN9++61uvfVWrVixQpZlaceOHWrfvr3uvvtutWjRQk899ZTTJcJgQ4cO1TvvvHPSvUPLysrUr18/bdmyxaHKGjaPx6P58+dr+fLl2r9/v7xeb8DjK1ascKiy0FBWVqaJEyf6P7/vf6V5PB6HKgMaNi4EbZDx48crMjJSJSUlSk9P9++/9dZblZubSwD8EbZv365nn31W27ZtkySlp6fr/vvv16WXXupwZQ1fSUmJ7rnnHr388sv+faWlperbt68uu+wyBytr2MaNG6f58+dr4MCB6tSp00kBGqd35513qqSkRFOnTlVSUhKfH/Aj0QE0SGJiot5//31lZGQoJiZGmzZtUvv27fXVV1+pS5cuqq6udrrEBu3dd9/VsGHD1L17d/+9K1evXq21a9dq0aJFGjp0qMMVNmwHDhxQ7969df311+vpp5/W3r171bdvX2VkZGjRokVyuZhxciqtWrXS66+/rgEDBjhdSkiKiYnRRx99pK5duzpdChBS6AAapKamRk2bNj1pf3l5ObdG+hEmT56shx56SI899ljA/kceeUSTJ08mAAbRunVrffDBB+rVq5ckKS8vT1dccYUWLFhA+DuNqKgodejQwekyQlZKSgrznIF64P/KBvnpT3+q119/3f+zZVnyer2aNWuW+vbt62BloWHfvn361a9+ddL+X/7yl9q3b58DFYWelJQULVu2TAsWLNBVV12lN998UxEREU6X1aBNmDBBzzzzDCGmnubMmaPf/e53+sc//uF0KUBIYQjYIFu2bFG/fv10xRVXaMWKFbrxxhu1detWlZeX6+OPP9ZFF13kdIkN2oABA/Tzn/9cd911V8D+V199VYsWLdL777/vUGUNV4sWLU455+rQoUOKjo4OCH/l5eXns7SQcfPNN+t///d/FR8fr8suu0yNGjUKePy///u/HaosNLRo0UKHDh1SXV2dmjZtetLnx7874NQYAjZIp06d9MUXX+jPf/6zYmJiVF1drSFDhmjMmDFKSkpyurwG78Ybb9SDDz6o4uJi9ezZU9KxOYBvv/22Hn30Uf31r38NOBbHui+wJy4uTjfffLPTZYQs/g0C9UMHEPiXHztPzbIsLi0BAAhpBMAQ9+mnn/7oY7t06XIOK0G4+9vf/qaIiAjl5OQE7P/ggw/k8Xh0/fXXO1QZTOfxeLRkyRL/5Zsuu+wy3Xjjjcw/BU6DABjiXC6XLMsKOoGcrhXOtS5dumjmzJknXc5k6dKlevDBB7Vp0yaHKmv43nnnHb311lsqKSlRbW1twGPr1693qKrQsHPnTg0YMEBff/21/3qd27dvV0pKiv7nf/6Huc/AD2AVcIjbtWuXvvrqK+3ateu021dffeV0qSGhoKBAgwYNUocOHdShQwfdeOON+uijj5wuKyTs2LFDHTt2PGl/Wlqadu7c6UBFoWHu3Lm666671KZNG23YsEFXXXWVWrZsqa+++oqu6Y/w29/+VhdddJF2796t9evXa/369SopKVFqaqp++9vfOl0e0GDRAQT+5T//8z911113aciQIbrmmmskSR9//LEWL16s+fPn6/bbb3e4woYtMTFRCxcu1LXXXhuw/8MPP9Ttt9+u/fv3O1RZw5aWlqZHHnlEt912W8AF3KdNm6by8nL9+c9/drrEBq1Zs2ZavXq1OnfuHLB/06ZNuuaaa7gAPvADCICG4VZm9Zeenq5Ro0Zp/PjxAfuffvppvfjii/7PFKd23333qbCwUIsXL/YPu+3cuVNDhw7VlVdeqZdeesnhChumpk2batu2bWrXrp0SEhK0bNkyZWRkaMeOHerZs6e+/fZbp0ts0OLj45WXl6err746YP/HH3+sQYMGcRkY4AcwBGyQd999V506dVJxcbEyMjKUkZGh9evXq1OnTnr33XedLq/B++qrrzRo0KCT9t94443atWuXAxWFllmzZqlZs2ZKS0tTamqqUlNTlZ6erpYtW+rJJ590urwGKzEx0R9S2rZtq9WrV0s6Nr2D38+Du+GGGzRq1CgVFRXJ5/PJ5/Np9erV+vWvf83lmoDT4DqABuFWZvakpKRo+fLlJ92W68MPP1RKSopDVYWO2NhYffLJJ1q2bJk2bdqkJk2aqEuXLurdu7fTpTVo1157rf7617/q8ssv11133aXx48frnXfe0bp16zRkyBCny2vw5s6dqxEjRigzM9N/Eei6ujrdeOONeuaZZxyuDmi4GAI2SNOmTfXpp5+eFGB27NihjIwMHTp0yKHKQsMLL7ygBx54QHfffbd/OOnjjz/W/Pnz9cwzz+i+++5zuEKYyOv1yuv1KjLy2O/jixYt0ieffKKLL75Y9913n6KiohyuMDTs2LFDn3/+uaRj0zm4vzJwegRAg3ArM/sWL16sp556KmAO5aRJk3TTTTc5XFloqKmpUUFBwSkvZ8KKTABoOAiAIe7E25Pt3btX06ZN0y9+8YtT3srs17/+tVNlIgxs2LBBAwYM0KFDh1RTU6P4+Hh98803atq0qRISErgU0Qm4gPvZk5ube8r9lmWpcePG6tChg2666SbFx8ef58qAho0AGOK4fdnZ0759e61du1YtW7YM2F9RUaErrriCABNEVlaWLrnkEs2bN0+xsbHatGmTGjVqpF/+8pcaN24c89lOcOIF3C3LOu2x/Hd7en379tX69evl8Xj8Vzv44osvFBERobS0NG3fvl2WZWnVqlWnvE4lEK5YBRzijs8fCrbxJRLcP/7xj1N+TkeOHNHXX3/tQEWhZePGjZowYYJcLpciIiJ05MgRpaSkaNasWXr44YedLq9BOfEC7u+++65SU1P1/PPPa8OGDdqwYYOef/55XXTRRaze/xFuuukmZWdna+/evSouLlZxcbH27Nmjn/3sZ7rtttv09ddfq3fv3idd3gkId6wCRtg7cRj9/fffV2xsrP9nj8ej5cuX68ILL3SgstDSqFEjf0c6ISFBJSUlSk9PV2xsrHbv3u1wdQ1Lu3bt/H/++c9/rrlz5wbcQq9Lly5KSUnR1KlTNXjwYAcqDB1/+tOftGzZMrndbv++2NhYTZ8+Xf3799e4ceM0bdo09e/f38EqgYaHABji5s6dq1GjRqlx48aaO3fuaY9lEv6pHf+CtSxLI0aMCHisUaNGuvDCC/XUU085UFloufzyy7V27VpdfPHF6tOnj6ZNm6ZvvvlGb7zxhjp16uR0eQ3W5s2blZqaetL+1NRUffbZZw5UFFoqKyu1f//+k4Z3Dxw4oKqqKklSXFzcSYuSgHDHHMAQl5qaqnXr1qlly5an/BI5zrIs5rAFkZqaqrVr16pVq1ZOlxKS1q1bp4MHD6pv377av3+/fvWrX/kvZ/LKK68oIyPD6RIbpCuuuEKdOnXSSy+95L/kS21tre655x5t2bJF69evd7jChm348OEqLCzUU089pSuvvFKStHbtWk2cOFFXX3213njjDS1atEhPPvmk1q1b53C1QMNBAAQAB61Zs0aDBg2Sz+fzr/j99NNPZVmW3nvvPV111VUOV9iwVVdXa/z48Xr99ddVV1cnSYqMjNSIESM0e/ZsNWvWTBs3bpQkde3a1blCgQaGAGiQxx57TBMnTlTTpk0D9n/33Xf605/+pGnTpjlUWcMVbNj8RAyh/zj79+/X9u3bJUlpaWlq3bq1wxU1fDU1NVqwYEHAhYxvv/12NWvWzOHKQkd1dbV/lKN9+/Zq3ry5wxUBDRsB0CARERHat2+fEhISAvZ/++23SkhIYCXwKXx/2PzAgQM6dOiQ4uLiJB27BAzXsftxDh48qN/85jdatGiR/99aRESEbr31Vj333HMBi2sAAM7iMjAG+aFrim3atImLoP6AXbt2+bc//OEP6tq1q7Zt26by8nKVl5dr27ZtuuKKK/T73//e6VIbvHvuuUdFRUXKy8tTRUWFKioqlJeXp3Xr1nEbvSDeeOMN9erVS8nJyfrnP/8pSZo9e7b+8pe/OFxZaFi3bp0mT56sYcOGaciQIQEbgFMjABqgRYsWio+Pl2VZuuSSSxQfH+/fYmNj9bOf/Uy/+MUvnC6zwZs6daqeffZZ/8VkJenSSy/V7NmzNWXKFAcrCw15eXl65ZVXlJOTI7fbLbfbrZycHL344ot67733nC6vwXrhhReUm5ur66+/Xv/3f//n7562aNFCc+bMcba4ELBo0SJdffXV2rZtmxYvXqyjR49q69atWrFiBV1n4DS4DIwB5syZI5/Pp7vvvluPPvpowP/0oqKidOGFFyozM9PBCkPDvn37/JPIT+TxeFRWVuZARaGlZcuWp/zCjY2NVYsWLRyoKDQ8++yzevHFFzV48GDNnDnTv7979+6aOHGig5WFhieeeEKzZ8/WmDFjFBMTo2eeeUapqam67777lJSU5HR5QINFADTAiBEjVFdXJ8uydO211yolJcXpkkJSv379dN999+mll17SFVdcIUkqLi7W6NGjlZ2d7XB1Dd+UKVOUm5urN954Q4mJiZKk0tJSTZo0SVOnTnW4uoZr165duvzyy0/aHx0drZqaGgcqCi1ffvmlBg4cKOnYL7w1NTWyLEvjx4/Xtddeq0cffdThCoGGiQBoiMjISI0ePVrbtm1zupSQ9corr2jEiBHq3r27GjVqJEmqq6tTTk6OXnrpJYera5guv/zygHmnO3bsUNu2bdW2bVtJUklJiaKjo3XgwAHmAf6A1NRUbdy4MeDuIJK0dOlSpaenO1RV6GjRooUOHjwoSfrJT36iLVu2qHPnzqqoqNChQ4ccrg5ouAiABrnqqqu0YcOGk75IEJzP59N3332nd999V3v27PEH6bS0NF1yySUOV9dwcZsy+3JzczVmzBgdPnxYPp9Pa9as0ZtvvqkZM2bwi8eP0Lt3by1btkydO3fWz3/+c40bN04rVqzQsmXL1K9fP6fLAxosLgNjkLfeeksPPfSQxo8fr27dup10DbHjF5nFybxerxo3bqytW7fq4osvdrochJkFCxZo+vTp+vLLLyUd62RNnz5dI0eOdLiyhq+8vFyHDx9WcnKyvF6vZs2a5b8DzZQpU5h/CvwAAqBBXK6TF3VbluW/PAzXATy9yy67TC+//LJ69uzpdCkhrba2Vvv375fX6w3Yf3xYGIG+++47+Xw+NW3aVIcOHdKWLVv08ccfq2PHjsrJyXG6vAbvV7/6lfr27avevXvroosucrocIGQQAA1y/PphP4Sh4dN77733NGvWLL3wwgvq1KmT0+WEnC+++EIjR47UJ598ErCfX0BOr3///hoyZIh+/etfq6KiQmlpaWrUqJG++eYbPf300xo9erTTJTZo99xzj1auXKmdO3fqJz/5ifr06aOsrCz16dOHbj5wGgRA4F9atGihQ4cOqa6uTlFRUWrSpEnA4+Xl5Q5VFhquueYaRUZG6ne/+52SkpJOuih5RkaGQ5U1bK1atVJBQYEuu+wyvfTSS3r22We1YcMGvfvuu5o2bRoLu36kr7/+WitXrlRBQYEKCgr0xRdfKCkpSXv27HG6NKBBYhGIYd544w3NmzdPu3btUmFhodq1a6c5c+YoNTVVN910k9PlNWhcdNeejRs3qri4WGlpaU6XElIOHTqkmJgYSdIHH3ygIUOGyOVyqWfPnkG7+vi3Fi1aqGXLlmrRooXi4uIUGRnJfaiB0yAAGuSFF17QtGnT9MADD+gPf/iDf8gtLi5Oc+bMIQAGMWLECKdLCGkdO3bUN99843QZIadDhw5asmSJbr75Zr3//vsaP368JGn//v1yu90OV9fwPfzww8rPz9eGDRuUnp6uPn366He/+5169+7NAhDgNBgCNkjHjh31xBNPaPDgwYqJidGmTZvUvn17bdmyRVlZWXw5n4HDhw+rtrY2YB9fxierqqry/3ndunWaMmWKnnjiCXXu3Nl/LcXj+PxO7Z133tHtt98uj8ejfv366YMPPpAkzZgxQytXrtTf//53hyts2Fwul1q3bq3x48dryJAhXLYJ+JEIgAZp0qSJPv/8c7Vr1y4gAO7YsUNdunTRd99953SJDVpNTY0efPBBvfXWW/r2229PepxFDCdzuVwBc/2OL/g4EYtAgistLdW+ffuUkZHhX82/Zs0aud1uhtSD2LRpkwoKCpSfn6+PPvpIUVFR/oUgWVlZBELgB/x/iYPpYlIbb2kAAAAASUVORK5CYII="
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<p>Visualization is very important in data science, and Python visualization packages provide great freedom. We will go more in-depth in a separate Python visualizations course.</p>\n\n<p>The main question we want to answer in this module is, \"What are the main characteristics which have the most impact on the car price?\".</p>\n\n<p>To get a better measure of the important characteristics, we look at the correlation of these variables with the car price. In other words: how is the car price dependent on this variable?</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"correlation_causation\">5. Correlation and Causation</h2>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p><b>Correlation</b>: a measure of the extent of interdependence between variables.</p>\n\n<p><b>Causation</b>: the relationship between cause and effect between two variables.</p>\n\n<p>It is important to know the difference between these two. Correlation does not imply causation. Determining correlation is much simpler  the determining causation as causation may require independent experimentation.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p><b>Pearson Correlation</b></p>\n<p>The Pearson Correlation measures the linear dependence between two variables X and Y.</p>\n<p>The resulting coefficient is a value between -1 and 1 inclusive, where:</p>\n<ul>\n    <li><b>1</b>: Perfect positive linear correlation.</li>\n    <li><b>0</b>: No linear correlation, the two variables most likely do not affect each other.</li>\n    <li><b>-1</b>: Perfect negative linear correlation.</li>\n</ul>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>Pearson Correlation is the default method of the function \"corr\". Like before, we can calculate the Pearson Correlation of the of the 'int64' or 'float64'  variables.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "df.corr()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 58,
      "outputs": [
        {
          "execution_count": 58,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                   symboling  normalized-losses  wheel-base    length  \\\nsymboling           1.000000           0.466264   -0.535987 -0.365404   \nnormalized-losses   0.466264           1.000000   -0.056661  0.019424   \nwheel-base         -0.535987          -0.056661    1.000000  0.876024   \nlength             -0.365404           0.019424    0.876024  1.000000   \nwidth              -0.242423           0.086802    0.814507  0.857170   \nheight             -0.550160          -0.373737    0.590742  0.492063   \ncurb-weight        -0.233118           0.099404    0.782097  0.880665   \nengine-size        -0.110581           0.112360    0.572027  0.685025   \nbore               -0.140019          -0.029862    0.493244  0.608971   \nstroke             -0.008245           0.055563    0.158502  0.124139   \ncompression-ratio  -0.182196          -0.114713    0.250313  0.159733   \nhorsepower          0.075819           0.217299    0.371147  0.579821   \npeak-rpm            0.279740           0.239543   -0.360305 -0.285970   \ncity-mpg           -0.035527          -0.225016   -0.470606 -0.665192   \nhighway-mpg         0.036233          -0.181877   -0.543304 -0.698142   \nprice              -0.082391           0.133999    0.584642  0.690628   \ncity-L/100km        0.066171           0.238567    0.476153  0.657373   \ndiesel             -0.196735          -0.101546    0.307237  0.211187   \ngas                 0.196735           0.101546   -0.307237 -0.211187   \n\n                      width    height  curb-weight  engine-size      bore  \\\nsymboling         -0.242423 -0.550160    -0.233118    -0.110581 -0.140019   \nnormalized-losses  0.086802 -0.373737     0.099404     0.112360 -0.029862   \nwheel-base         0.814507  0.590742     0.782097     0.572027  0.493244   \nlength             0.857170  0.492063     0.880665     0.685025  0.608971   \nwidth              1.000000  0.306002     0.866201     0.729436  0.544885   \nheight             0.306002  1.000000     0.307581     0.074694  0.180449   \ncurb-weight        0.866201  0.307581     1.000000     0.849072  0.644060   \nengine-size        0.729436  0.074694     0.849072     1.000000  0.572609   \nbore               0.544885  0.180449     0.644060     0.572609  1.000000   \nstroke             0.188829 -0.062704     0.167562     0.209523 -0.055390   \ncompression-ratio  0.189867  0.259737     0.156433     0.028889  0.001263   \nhorsepower         0.615077 -0.087027     0.757976     0.822676  0.566936   \npeak-rpm          -0.245800 -0.309974    -0.279361    -0.256733 -0.267392   \ncity-mpg          -0.633531 -0.049800    -0.749543    -0.650546 -0.582027   \nhighway-mpg       -0.680635 -0.104812    -0.794889    -0.679571 -0.591309   \nprice              0.751265  0.135486     0.834415     0.872335  0.543155   \ncity-L/100km       0.673363  0.003811     0.785353     0.745059  0.554610   \ndiesel             0.244356  0.281578     0.221046     0.070779  0.054458   \ngas               -0.244356 -0.281578    -0.221046    -0.070779 -0.054458   \n\n                     stroke  compression-ratio  horsepower  peak-rpm  \\\nsymboling         -0.008245          -0.182196    0.075819  0.279740   \nnormalized-losses  0.055563          -0.114713    0.217299  0.239543   \nwheel-base         0.158502           0.250313    0.371147 -0.360305   \nlength             0.124139           0.159733    0.579821 -0.285970   \nwidth              0.188829           0.189867    0.615077 -0.245800   \nheight            -0.062704           0.259737   -0.087027 -0.309974   \ncurb-weight        0.167562           0.156433    0.757976 -0.279361   \nengine-size        0.209523           0.028889    0.822676 -0.256733   \nbore              -0.055390           0.001263    0.566936 -0.267392   \nstroke             1.000000           0.187923    0.098462 -0.065713   \ncompression-ratio  0.187923           1.000000   -0.214514 -0.435780   \nhorsepower         0.098462          -0.214514    1.000000  0.107885   \npeak-rpm          -0.065713          -0.435780    0.107885  1.000000   \ncity-mpg          -0.034696           0.331425   -0.822214 -0.115413   \nhighway-mpg       -0.035201           0.268465   -0.804575 -0.058598   \nprice              0.082310           0.071107    0.809575 -0.101616   \ncity-L/100km       0.037300          -0.299372    0.889488  0.115830   \ndiesel             0.241303           0.985231   -0.169053 -0.475812   \ngas               -0.241303          -0.985231    0.169053  0.475812   \n\n                   city-mpg  highway-mpg     price  city-L/100km    diesel  \\\nsymboling         -0.035527     0.036233 -0.082391      0.066171 -0.196735   \nnormalized-losses -0.225016    -0.181877  0.133999      0.238567 -0.101546   \nwheel-base        -0.470606    -0.543304  0.584642      0.476153  0.307237   \nlength            -0.665192    -0.698142  0.690628      0.657373  0.211187   \nwidth             -0.633531    -0.680635  0.751265      0.673363  0.244356   \nheight            -0.049800    -0.104812  0.135486      0.003811  0.281578   \ncurb-weight       -0.749543    -0.794889  0.834415      0.785353  0.221046   \nengine-size       -0.650546    -0.679571  0.872335      0.745059  0.070779   \nbore              -0.582027    -0.591309  0.543155      0.554610  0.054458   \nstroke            -0.034696    -0.035201  0.082310      0.037300  0.241303   \ncompression-ratio  0.331425     0.268465  0.071107     -0.299372  0.985231   \nhorsepower        -0.822214    -0.804575  0.809575      0.889488 -0.169053   \npeak-rpm          -0.115413    -0.058598 -0.101616      0.115830 -0.475812   \ncity-mpg           1.000000     0.972044 -0.686571     -0.949713  0.265676   \nhighway-mpg        0.972044     1.000000 -0.704692     -0.930028  0.198690   \nprice             -0.686571    -0.704692  1.000000      0.789898  0.110326   \ncity-L/100km      -0.949713    -0.930028  0.789898      1.000000 -0.241282   \ndiesel             0.265676     0.198690  0.110326     -0.241282  1.000000   \ngas               -0.265676    -0.198690 -0.110326      0.241282 -1.000000   \n\n                        gas  \nsymboling          0.196735  \nnormalized-losses  0.101546  \nwheel-base        -0.307237  \nlength            -0.211187  \nwidth             -0.244356  \nheight            -0.281578  \ncurb-weight       -0.221046  \nengine-size       -0.070779  \nbore              -0.054458  \nstroke            -0.241303  \ncompression-ratio -0.985231  \nhorsepower         0.169053  \npeak-rpm           0.475812  \ncity-mpg          -0.265676  \nhighway-mpg       -0.198690  \nprice             -0.110326  \ncity-L/100km       0.241282  \ndiesel            -1.000000  \ngas                1.000000  ",
            "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>symboling</th>\n      <th>normalized-losses</th>\n      <th>wheel-base</th>\n      <th>length</th>\n      <th>width</th>\n      <th>height</th>\n      <th>curb-weight</th>\n      <th>engine-size</th>\n      <th>bore</th>\n      <th>stroke</th>\n      <th>compression-ratio</th>\n      <th>horsepower</th>\n      <th>peak-rpm</th>\n      <th>city-mpg</th>\n      <th>highway-mpg</th>\n      <th>price</th>\n      <th>city-L/100km</th>\n      <th>diesel</th>\n      <th>gas</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>symboling</th>\n      <td>1.000000</td>\n      <td>0.466264</td>\n      <td>-0.535987</td>\n      <td>-0.365404</td>\n      <td>-0.242423</td>\n      <td>-0.550160</td>\n      <td>-0.233118</td>\n      <td>-0.110581</td>\n      <td>-0.140019</td>\n      <td>-0.008245</td>\n      <td>-0.182196</td>\n      <td>0.075819</td>\n      <td>0.279740</td>\n      <td>-0.035527</td>\n      <td>0.036233</td>\n      <td>-0.082391</td>\n      <td>0.066171</td>\n      <td>-0.196735</td>\n      <td>0.196735</td>\n    </tr>\n    <tr>\n      <th>normalized-losses</th>\n      <td>0.466264</td>\n      <td>1.000000</td>\n      <td>-0.056661</td>\n      <td>0.019424</td>\n      <td>0.086802</td>\n      <td>-0.373737</td>\n      <td>0.099404</td>\n      <td>0.112360</td>\n      <td>-0.029862</td>\n      <td>0.055563</td>\n      <td>-0.114713</td>\n      <td>0.217299</td>\n      <td>0.239543</td>\n      <td>-0.225016</td>\n      <td>-0.181877</td>\n      <td>0.133999</td>\n      <td>0.238567</td>\n      <td>-0.101546</td>\n      <td>0.101546</td>\n    </tr>\n    <tr>\n      <th>wheel-base</th>\n      <td>-0.535987</td>\n      <td>-0.056661</td>\n      <td>1.000000</td>\n      <td>0.876024</td>\n      <td>0.814507</td>\n      <td>0.590742</td>\n      <td>0.782097</td>\n      <td>0.572027</td>\n      <td>0.493244</td>\n      <td>0.158502</td>\n      <td>0.250313</td>\n      <td>0.371147</td>\n      <td>-0.360305</td>\n      <td>-0.470606</td>\n      <td>-0.543304</td>\n      <td>0.584642</td>\n      <td>0.476153</td>\n      <td>0.307237</td>\n      <td>-0.307237</td>\n    </tr>\n    <tr>\n      <th>length</th>\n      <td>-0.365404</td>\n      <td>0.019424</td>\n      <td>0.876024</td>\n      <td>1.000000</td>\n      <td>0.857170</td>\n      <td>0.492063</td>\n      <td>0.880665</td>\n      <td>0.685025</td>\n      <td>0.608971</td>\n      <td>0.124139</td>\n      <td>0.159733</td>\n      <td>0.579821</td>\n      <td>-0.285970</td>\n      <td>-0.665192</td>\n      <td>-0.698142</td>\n      <td>0.690628</td>\n      <td>0.657373</td>\n      <td>0.211187</td>\n      <td>-0.211187</td>\n    </tr>\n    <tr>\n      <th>width</th>\n      <td>-0.242423</td>\n      <td>0.086802</td>\n      <td>0.814507</td>\n      <td>0.857170</td>\n      <td>1.000000</td>\n      <td>0.306002</td>\n      <td>0.866201</td>\n      <td>0.729436</td>\n      <td>0.544885</td>\n      <td>0.188829</td>\n      <td>0.189867</td>\n      <td>0.615077</td>\n      <td>-0.245800</td>\n      <td>-0.633531</td>\n      <td>-0.680635</td>\n      <td>0.751265</td>\n      <td>0.673363</td>\n      <td>0.244356</td>\n      <td>-0.244356</td>\n    </tr>\n    <tr>\n      <th>height</th>\n      <td>-0.550160</td>\n      <td>-0.373737</td>\n      <td>0.590742</td>\n      <td>0.492063</td>\n      <td>0.306002</td>\n      <td>1.000000</td>\n      <td>0.307581</td>\n      <td>0.074694</td>\n      <td>0.180449</td>\n      <td>-0.062704</td>\n      <td>0.259737</td>\n      <td>-0.087027</td>\n      <td>-0.309974</td>\n      <td>-0.049800</td>\n      <td>-0.104812</td>\n      <td>0.135486</td>\n      <td>0.003811</td>\n      <td>0.281578</td>\n      <td>-0.281578</td>\n    </tr>\n    <tr>\n      <th>curb-weight</th>\n      <td>-0.233118</td>\n      <td>0.099404</td>\n      <td>0.782097</td>\n      <td>0.880665</td>\n      <td>0.866201</td>\n      <td>0.307581</td>\n      <td>1.000000</td>\n      <td>0.849072</td>\n      <td>0.644060</td>\n      <td>0.167562</td>\n      <td>0.156433</td>\n      <td>0.757976</td>\n      <td>-0.279361</td>\n      <td>-0.749543</td>\n      <td>-0.794889</td>\n      <td>0.834415</td>\n      <td>0.785353</td>\n      <td>0.221046</td>\n      <td>-0.221046</td>\n    </tr>\n    <tr>\n      <th>engine-size</th>\n      <td>-0.110581</td>\n      <td>0.112360</td>\n      <td>0.572027</td>\n      <td>0.685025</td>\n      <td>0.729436</td>\n      <td>0.074694</td>\n      <td>0.849072</td>\n      <td>1.000000</td>\n      <td>0.572609</td>\n      <td>0.209523</td>\n      <td>0.028889</td>\n      <td>0.822676</td>\n      <td>-0.256733</td>\n      <td>-0.650546</td>\n      <td>-0.679571</td>\n      <td>0.872335</td>\n      <td>0.745059</td>\n      <td>0.070779</td>\n      <td>-0.070779</td>\n    </tr>\n    <tr>\n      <th>bore</th>\n      <td>-0.140019</td>\n      <td>-0.029862</td>\n      <td>0.493244</td>\n      <td>0.608971</td>\n      <td>0.544885</td>\n      <td>0.180449</td>\n      <td>0.644060</td>\n      <td>0.572609</td>\n      <td>1.000000</td>\n      <td>-0.055390</td>\n      <td>0.001263</td>\n      <td>0.566936</td>\n      <td>-0.267392</td>\n      <td>-0.582027</td>\n      <td>-0.591309</td>\n      <td>0.543155</td>\n      <td>0.554610</td>\n      <td>0.054458</td>\n      <td>-0.054458</td>\n    </tr>\n    <tr>\n      <th>stroke</th>\n      <td>-0.008245</td>\n      <td>0.055563</td>\n      <td>0.158502</td>\n      <td>0.124139</td>\n      <td>0.188829</td>\n      <td>-0.062704</td>\n      <td>0.167562</td>\n      <td>0.209523</td>\n      <td>-0.055390</td>\n      <td>1.000000</td>\n      <td>0.187923</td>\n      <td>0.098462</td>\n      <td>-0.065713</td>\n      <td>-0.034696</td>\n      <td>-0.035201</td>\n      <td>0.082310</td>\n      <td>0.037300</td>\n      <td>0.241303</td>\n      <td>-0.241303</td>\n    </tr>\n    <tr>\n      <th>compression-ratio</th>\n      <td>-0.182196</td>\n      <td>-0.114713</td>\n      <td>0.250313</td>\n      <td>0.159733</td>\n      <td>0.189867</td>\n      <td>0.259737</td>\n      <td>0.156433</td>\n      <td>0.028889</td>\n      <td>0.001263</td>\n      <td>0.187923</td>\n      <td>1.000000</td>\n      <td>-0.214514</td>\n      <td>-0.435780</td>\n      <td>0.331425</td>\n      <td>0.268465</td>\n      <td>0.071107</td>\n      <td>-0.299372</td>\n      <td>0.985231</td>\n      <td>-0.985231</td>\n    </tr>\n    <tr>\n      <th>horsepower</th>\n      <td>0.075819</td>\n      <td>0.217299</td>\n      <td>0.371147</td>\n      <td>0.579821</td>\n      <td>0.615077</td>\n      <td>-0.087027</td>\n      <td>0.757976</td>\n      <td>0.822676</td>\n      <td>0.566936</td>\n      <td>0.098462</td>\n      <td>-0.214514</td>\n      <td>1.000000</td>\n      <td>0.107885</td>\n      <td>-0.822214</td>\n      <td>-0.804575</td>\n      <td>0.809575</td>\n      <td>0.889488</td>\n      <td>-0.169053</td>\n      <td>0.169053</td>\n    </tr>\n    <tr>\n      <th>peak-rpm</th>\n      <td>0.279740</td>\n      <td>0.239543</td>\n      <td>-0.360305</td>\n      <td>-0.285970</td>\n      <td>-0.245800</td>\n      <td>-0.309974</td>\n      <td>-0.279361</td>\n      <td>-0.256733</td>\n      <td>-0.267392</td>\n      <td>-0.065713</td>\n      <td>-0.435780</td>\n      <td>0.107885</td>\n      <td>1.000000</td>\n      <td>-0.115413</td>\n      <td>-0.058598</td>\n      <td>-0.101616</td>\n      <td>0.115830</td>\n      <td>-0.475812</td>\n      <td>0.475812</td>\n    </tr>\n    <tr>\n      <th>city-mpg</th>\n      <td>-0.035527</td>\n      <td>-0.225016</td>\n      <td>-0.470606</td>\n      <td>-0.665192</td>\n      <td>-0.633531</td>\n      <td>-0.049800</td>\n      <td>-0.749543</td>\n      <td>-0.650546</td>\n      <td>-0.582027</td>\n      <td>-0.034696</td>\n      <td>0.331425</td>\n      <td>-0.822214</td>\n      <td>-0.115413</td>\n      <td>1.000000</td>\n      <td>0.972044</td>\n      <td>-0.686571</td>\n      <td>-0.949713</td>\n      <td>0.265676</td>\n      <td>-0.265676</td>\n    </tr>\n    <tr>\n      <th>highway-mpg</th>\n      <td>0.036233</td>\n      <td>-0.181877</td>\n      <td>-0.543304</td>\n      <td>-0.698142</td>\n      <td>-0.680635</td>\n      <td>-0.104812</td>\n      <td>-0.794889</td>\n      <td>-0.679571</td>\n      <td>-0.591309</td>\n      <td>-0.035201</td>\n      <td>0.268465</td>\n      <td>-0.804575</td>\n      <td>-0.058598</td>\n      <td>0.972044</td>\n      <td>1.000000</td>\n      <td>-0.704692</td>\n      <td>-0.930028</td>\n      <td>0.198690</td>\n      <td>-0.198690</td>\n    </tr>\n    <tr>\n      <th>price</th>\n      <td>-0.082391</td>\n      <td>0.133999</td>\n      <td>0.584642</td>\n      <td>0.690628</td>\n      <td>0.751265</td>\n      <td>0.135486</td>\n      <td>0.834415</td>\n      <td>0.872335</td>\n      <td>0.543155</td>\n      <td>0.082310</td>\n      <td>0.071107</td>\n      <td>0.809575</td>\n      <td>-0.101616</td>\n      <td>-0.686571</td>\n      <td>-0.704692</td>\n      <td>1.000000</td>\n      <td>0.789898</td>\n      <td>0.110326</td>\n      <td>-0.110326</td>\n    </tr>\n    <tr>\n      <th>city-L/100km</th>\n      <td>0.066171</td>\n      <td>0.238567</td>\n      <td>0.476153</td>\n      <td>0.657373</td>\n      <td>0.673363</td>\n      <td>0.003811</td>\n      <td>0.785353</td>\n      <td>0.745059</td>\n      <td>0.554610</td>\n      <td>0.037300</td>\n      <td>-0.299372</td>\n      <td>0.889488</td>\n      <td>0.115830</td>\n      <td>-0.949713</td>\n      <td>-0.930028</td>\n      <td>0.789898</td>\n      <td>1.000000</td>\n      <td>-0.241282</td>\n      <td>0.241282</td>\n    </tr>\n    <tr>\n      <th>diesel</th>\n      <td>-0.196735</td>\n      <td>-0.101546</td>\n      <td>0.307237</td>\n      <td>0.211187</td>\n      <td>0.244356</td>\n      <td>0.281578</td>\n      <td>0.221046</td>\n      <td>0.070779</td>\n      <td>0.054458</td>\n      <td>0.241303</td>\n      <td>0.985231</td>\n      <td>-0.169053</td>\n      <td>-0.475812</td>\n      <td>0.265676</td>\n      <td>0.198690</td>\n      <td>0.110326</td>\n      <td>-0.241282</td>\n      <td>1.000000</td>\n      <td>-1.000000</td>\n    </tr>\n    <tr>\n      <th>gas</th>\n      <td>0.196735</td>\n      <td>0.101546</td>\n      <td>-0.307237</td>\n      <td>-0.211187</td>\n      <td>-0.244356</td>\n      <td>-0.281578</td>\n      <td>-0.221046</td>\n      <td>-0.070779</td>\n      <td>-0.054458</td>\n      <td>-0.241303</td>\n      <td>-0.985231</td>\n      <td>0.169053</td>\n      <td>0.475812</td>\n      <td>-0.265676</td>\n      <td>-0.198690</td>\n      <td>-0.110326</td>\n      <td>0.241282</td>\n      <td>-1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Sometimes we would like to know the significant of the correlation estimate.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<b>P-value</b>\n\n<p>What is this P-value? The P-value is the probability value that the correlation between these two variables is statistically significant. Normally, we choose a significance level of 0.05, which means that we are 95% confident that the correlation between the variables is significant.</p>\n\nBy convention, when the\n\n<ul>\n    <li>p-value is $<$ 0.001: we say there is strong evidence that the correlation is significant.</li>\n    <li>the p-value is $<$ 0.05: there is moderate evidence that the correlation is significant.</li>\n    <li>the p-value is $<$ 0.1: there is weak evidence that the correlation is significant.</li>\n    <li>the p-value is $>$ 0.1: there is no evidence that the correlation is significant.</li>\n</ul>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can obtain this information using  \"stats\" module in the \"scipy\"  library.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from scipy import stats",
      "metadata": {
        "trusted": true
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Wheel-Base vs. Price</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's calculate the  Pearson Correlation Coefficient and P-value of 'wheel-base' and 'price'.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pearson_coef, p_value = stats.pearsonr(df['wheel-base'], df['price'])\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value)  ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 60,
      "outputs": [
        {
          "name": "stdout",
          "text": "The Pearson Correlation Coefficient is 0.5846418222655085  with a P-value of P = 8.076488270732243e-20\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Conclusion:</h4>\n<p>Since the p-value is $<$ 0.001, the correlation between wheel-base and price is statistically significant, although the linear relationship isn't extremely strong (~0.585).</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Horsepower vs. Price</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's calculate the  Pearson Correlation Coefficient and P-value of 'horsepower' and 'price'.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pearson_coef, p_value = stats.pearsonr(df['horsepower'], df['price'])\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value)  ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 61,
      "outputs": [
        {
          "name": "stdout",
          "text": "The Pearson Correlation Coefficient is 0.8095745670036559  with a P-value of P =  6.369057428260101e-48\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Conclusion:</h4>\n\n<p>Since the p-value is $<$ 0.001, the correlation between horsepower and price is statistically significant, and the linear relationship is quite strong (~0.809, close to 1).</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Length vs. Price</h3>\n\nLet's calculate the  Pearson Correlation Coefficient and P-value of 'length' and 'price'.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pearson_coef, p_value = stats.pearsonr(df['length'], df['price'])\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value)  ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 62,
      "outputs": [
        {
          "name": "stdout",
          "text": "The Pearson Correlation Coefficient is 0.6906283804483643  with a P-value of P =  8.01647746615853e-30\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Conclusion:</h4>\n<p>Since the p-value is $<$ 0.001, the correlation between length and price is statistically significant, and the linear relationship is moderately strong (~0.691).</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Width vs. Price</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's calculate the Pearson Correlation Coefficient and P-value of 'width' and 'price':\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pearson_coef, p_value = stats.pearsonr(df['width'], df['price'])\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value ) ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 63,
      "outputs": [
        {
          "name": "stdout",
          "text": "The Pearson Correlation Coefficient is 0.7512653440522666  with a P-value of P = 9.200335510483739e-38\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "#### Conclusion:\n\nSince the p-value is < 0.001, the correlation between width and price is statistically significant, and the linear relationship is quite strong (\\~0.751).\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Curb-Weight vs. Price\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's calculate the Pearson Correlation Coefficient and P-value of 'curb-weight' and 'price':\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pearson_coef, p_value = stats.pearsonr(df['curb-weight'], df['price'])\nprint( \"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value)  ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 64,
      "outputs": [
        {
          "name": "stdout",
          "text": "The Pearson Correlation Coefficient is 0.8344145257702845  with a P-value of P =  2.189577238893816e-53\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Conclusion:</h4>\n<p>Since the p-value is $<$ 0.001, the correlation between curb-weight and price is statistically significant, and the linear relationship is quite strong (~0.834).</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Engine-Size vs. Price</h3>\n\nLet's calculate the Pearson Correlation Coefficient and P-value of 'engine-size' and 'price':\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pearson_coef, p_value = stats.pearsonr(df['engine-size'], df['price'])\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value) ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 65,
      "outputs": [
        {
          "name": "stdout",
          "text": "The Pearson Correlation Coefficient is 0.8723351674455188  with a P-value of P = 9.265491622196808e-64\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Conclusion:</h4>\n\n<p>Since the p-value is $<$ 0.001, the correlation between engine-size and price is statistically significant, and the linear relationship is very strong (~0.872).</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Bore vs. Price</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's calculate the  Pearson Correlation Coefficient and P-value of 'bore' and 'price':\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pearson_coef, p_value = stats.pearsonr(df['bore'], df['price'])\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =  \", p_value ) ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 66,
      "outputs": [
        {
          "name": "stdout",
          "text": "The Pearson Correlation Coefficient is 0.54315538326266  with a P-value of P =   8.049189483935489e-17\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Conclusion:</h4>\n<p>Since the p-value is $<$ 0.001, the correlation between bore and price is statistically significant, but the linear relationship is only moderate (~0.521).</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We can relate the process for each 'city-mpg'  and 'highway-mpg':\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>City-mpg vs. Price</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pearson_coef, p_value = stats.pearsonr(df['city-mpg'], df['price'])\nprint(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value)  ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 67,
      "outputs": [
        {
          "name": "stdout",
          "text": "The Pearson Correlation Coefficient is -0.6865710067844684  with a P-value of P =  2.3211320655672453e-29\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h4>Conclusion:</h4>\n<p>Since the p-value is $<$ 0.001, the correlation between city-mpg and price is statistically significant, and the coefficient of about -0.687 shows that the relationship is negative and moderately strong.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Highway-mpg vs. Price</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "pearson_coef, p_value = stats.pearsonr(df['highway-mpg'], df['price'])\nprint( \"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value ) ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 68,
      "outputs": [
        {
          "name": "stdout",
          "text": "The Pearson Correlation Coefficient is -0.7046922650589534  with a P-value of P =  1.749547114447437e-31\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "#### Conclusion:\n\nSince the p-value is < 0.001, the correlation between highway-mpg and price is statistically significant, and the coefficient of about -0.705 shows that the relationship is negative and moderately strong.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h2 id=\"anova\">6. ANOVA</h2>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>ANOVA: Analysis of Variance</h3>\n<p>The Analysis of Variance  (ANOVA) is a statistical method used to test whether there are significant differences between the means of two or more groups. ANOVA returns two parameters:</p>\n\n<p><b>F-test score</b>: ANOVA assumes the means of all groups are the same, calculates how much the actual means deviate from the assumption, and reports it as the F-test score. A larger score means there is a larger difference between the means.</p>\n\n<p><b>P-value</b>:  P-value tells how statistically significant our calculated score value is.</p>\n\n<p>If our price variable is strongly correlated with the variable we are analyzing, we expect ANOVA to return a sizeable F-test score and a small p-value.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Drive Wheels</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>Since ANOVA analyzes the difference between different groups of the same variable, the groupby function will come in handy. Because the ANOVA algorithm averages the data automatically, we do not need to take the average before hand.</p>\n\n<p>To see if different types of 'drive-wheels' impact  'price', we group the data.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "grouped_test2=df_gptest[['drive-wheels', 'price']].groupby(['drive-wheels'])\ngrouped_test2.head(2)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 69,
      "outputs": [
        {
          "execution_count": 69,
          "output_type": "execute_result",
          "data": {
            "text/plain": "    drive-wheels    price\n0            rwd  13495.0\n1            rwd  16500.0\n3            fwd  13950.0\n4            4wd  17450.0\n5            fwd  15250.0\n136          4wd   7603.0",
            "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>drive-wheels</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>rwd</td>\n      <td>13495.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>rwd</td>\n      <td>16500.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>fwd</td>\n      <td>13950.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4wd</td>\n      <td>17450.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>fwd</td>\n      <td>15250.0</td>\n    </tr>\n    <tr>\n      <th>136</th>\n      <td>4wd</td>\n      <td>7603.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "df_gptest",
      "metadata": {
        "trusted": true
      },
      "execution_count": 70,
      "outputs": [
        {
          "execution_count": 70,
          "output_type": "execute_result",
          "data": {
            "text/plain": "    drive-wheels   body-style    price\n0            rwd  convertible  13495.0\n1            rwd  convertible  16500.0\n2            rwd    hatchback  16500.0\n3            fwd        sedan  13950.0\n4            4wd        sedan  17450.0\n..           ...          ...      ...\n196          rwd        sedan  16845.0\n197          rwd        sedan  19045.0\n198          rwd        sedan  21485.0\n199          rwd        sedan  22470.0\n200          rwd        sedan  22625.0\n\n[201 rows x 3 columns]",
            "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>drive-wheels</th>\n      <th>body-style</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>rwd</td>\n      <td>convertible</td>\n      <td>13495.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>rwd</td>\n      <td>convertible</td>\n      <td>16500.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>rwd</td>\n      <td>hatchback</td>\n      <td>16500.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>fwd</td>\n      <td>sedan</td>\n      <td>13950.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4wd</td>\n      <td>sedan</td>\n      <td>17450.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>196</th>\n      <td>rwd</td>\n      <td>sedan</td>\n      <td>16845.0</td>\n    </tr>\n    <tr>\n      <th>197</th>\n      <td>rwd</td>\n      <td>sedan</td>\n      <td>19045.0</td>\n    </tr>\n    <tr>\n      <th>198</th>\n      <td>rwd</td>\n      <td>sedan</td>\n      <td>21485.0</td>\n    </tr>\n    <tr>\n      <th>199</th>\n      <td>rwd</td>\n      <td>sedan</td>\n      <td>22470.0</td>\n    </tr>\n    <tr>\n      <th>200</th>\n      <td>rwd</td>\n      <td>sedan</td>\n      <td>22625.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>201 rows × 3 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can obtain the values of the method group using the method \"get_group\".\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "grouped_test2.get_group('4wd')['price']",
      "metadata": {
        "trusted": true
      },
      "execution_count": 71,
      "outputs": [
        {
          "execution_count": 71,
          "output_type": "execute_result",
          "data": {
            "text/plain": "4      17450.0\n136     7603.0\n140     9233.0\n141    11259.0\n144     8013.0\n145    11694.0\n150     7898.0\n151     8778.0\nName: price, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can use the function 'f_oneway' in the module 'stats' to obtain the <b>F-test score</b> and <b>P-value</b>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# ANOVA\nf_val, p_val = stats.f_oneway(grouped_test2.get_group('fwd')['price'], grouped_test2.get_group('rwd')['price'], grouped_test2.get_group('4wd')['price'])  \n \nprint( \"ANOVA results: F=\", f_val, \", P =\", p_val)   ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 72,
      "outputs": [
        {
          "name": "stdout",
          "text": "ANOVA results: F= 67.95406500780399 , P = 3.3945443577151245e-23\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "This is a great result with a large F-test score showing a strong correlation and a P-value of almost 0 implying almost certain statistical significance. But does this mean all three tested groups are all this highly correlated?\n\nLet's examine them separately.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "#### fwd and rwd\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "f_val, p_val = stats.f_oneway(grouped_test2.get_group('fwd')['price'], grouped_test2.get_group('rwd')['price'])  \n \nprint( \"ANOVA results: F=\", f_val, \", P =\", p_val )",
      "metadata": {
        "trusted": true
      },
      "execution_count": 73,
      "outputs": [
        {
          "name": "stdout",
          "text": "ANOVA results: F= 130.5533160959111 , P = 2.2355306355677845e-23\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Let's examine the other groups.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "#### 4wd and rwd\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "f_val, p_val = stats.f_oneway(grouped_test2.get_group('4wd')['price'], grouped_test2.get_group('rwd')['price'])  \n   \nprint( \"ANOVA results: F=\", f_val, \", P =\", p_val)   ",
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 74,
      "outputs": [
        {
          "name": "stdout",
          "text": "ANOVA results: F= 8.580681368924756 , P = 0.004411492211225333\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h4>4wd and fwd</h4>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "f_val, p_val = stats.f_oneway(grouped_test2.get_group('4wd')['price'], grouped_test2.get_group('fwd')['price'])  \n \nprint(\"ANOVA results: F=\", f_val, \", P =\", p_val)   ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 75,
      "outputs": [
        {
          "name": "stdout",
          "text": "ANOVA results: F= 0.665465750252303 , P = 0.41620116697845655\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Conclusion: Important Variables</h3>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<p>We now have a better idea of what our data looks like and which variables are important to take into account when predicting the car price. We have narrowed it down to the following variables:</p>\n\nContinuous numerical variables:\n\n<ul>\n    <li>Length</li>\n    <li>Width</li>\n    <li>Curb-weight</li>\n    <li>Engine-size</li>\n    <li>Horsepower</li>\n    <li>City-mpg</li>\n    <li>Highway-mpg</li>\n    <li>Wheel-base</li>\n    <li>Bore</li>\n</ul>\n\nCategorical variables:\n\n<ul>\n    <li>Drive-wheels</li>\n</ul>\n\n<p>As we now move into building machine learning models to automate our analysis, feeding the model with variables that meaningfully affect our target variable will improve our model's prediction performance.</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Thank you for completing this lab!\n\n## Author\n\n<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\" target=\"_blank\">Joseph Santarcangelo</a>\n\n### Other Contributors\n\n<a href=\"https://www.linkedin.com/in/mahdi-noorian-58219234/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\" target=\"_blank\">Mahdi Noorian PhD</a>\n\nBahare Talayian\n\nEric Xiao\n\nSteven Dong\n\nParizad\n\nHima Vasudevan\n\n<a href=\"https://www.linkedin.com/in/fiorellawever/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2021-01-01\" target=\"_blank\">Fiorella Wenver</a>\n\n<a href=\"https:// https://www.linkedin.com/in/yi-leng-yao-84451275/ \" target=\"_blank\" >Yi Yao</a>.\n\n## Change Log\n\n| Date (YYYY-MM-DD) | Version | Changed By | Change Description                 |\n| ----------------- | ------- | ---------- | ---------------------------------- |\n| 2023-05-09        | 2.2     | Nafisa Idris | I have responded to the questions and made the necessary updates to the queries before proceeding to the GitHub repository |\n| 2020-10-30        | 2.1     | Lakshmi    | changed URL of csv                 |\n| 2020-08-27        | 2.0     | Lavanya    | Moved lab to course repo in GitLab |\n\n<hr>\n\n## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
      "metadata": {}
    }
  ]
}