Exploratory-Data-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Exploratory Data Analysis\n",
    "\n",
    "\n",
    "Copyright 2019 Tinto T. Kurian\n",
    "\n",
    "MIT License: https://opensource.org/licenses/MIT"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import thinkplot\n",
    "import thinkstats2"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "df = pd.read_csv(\"./StudentsPerformance.csv\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race/ethnicity parental level of education         lunch  \\\n",
       "0  female        group B           bachelor's degree      standard   \n",
       "1  female        group C                some college      standard   \n",
       "2  female        group B             master's degree      standard   \n",
       "3    male        group A          associate's degree  free/reduced   \n",
       "4    male        group C                some college      standard   \n",
       "\n",
       "  test preparation course  math score  reading score  writing score  \n",
       "0                    none          72             72             74  \n",
       "1               completed          69             90             88  \n",
       "2                    none          90             95             93  \n",
       "3                    none          47             57             44  \n",
       "4                    none          76             78             75  "
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "List the columns"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "df.columns"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Index(['gender', 'race/ethnicity', 'parental level of education', 'lunch',\n",
       "       'test preparation course', 'math score', 'reading score',\n",
       "       'writing score'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Explore the columns"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "df.gender.value_counts().sort_index()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "female    518\n",
       "male      482\n",
       "Name: gender, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "df['race/ethnicity'].value_counts().sort_index()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "group A     89\n",
       "group B    190\n",
       "group C    319\n",
       "group D    262\n",
       "group E    140\n",
       "Name: race/ethnicity, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "df['parental level of education'].value_counts().sort_index()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "associate's degree    222\n",
       "bachelor's degree     118\n",
       "high school           196\n",
       "master's degree        59\n",
       "some college          226\n",
       "some high school      179\n",
       "Name: parental level of education, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "df['lunch'].value_counts().sort_index()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "free/reduced    355\n",
       "standard        645\n",
       "Name: lunch, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "df['test preparation course'].value_counts().sort_index()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "completed    358\n",
       "none         642\n",
       "Name: test preparation course, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "df.describe()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.00000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66.08900</td>\n",
       "      <td>69.169000</td>\n",
       "      <td>68.054000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.16308</td>\n",
       "      <td>14.600192</td>\n",
       "      <td>15.195657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>57.00000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>57.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>66.00000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>77.00000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.00000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       math score  reading score  writing score\n",
       "count  1000.00000    1000.000000    1000.000000\n",
       "mean     66.08900      69.169000      68.054000\n",
       "std      15.16308      14.600192      15.195657\n",
       "min       0.00000      17.000000      10.000000\n",
       "25%      57.00000      59.000000      57.750000\n",
       "50%      66.00000      70.000000      69.000000\n",
       "75%      77.00000      79.000000      79.000000\n",
       "max     100.00000     100.000000     100.000000"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Checking if there are any NULL values"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "df.isnull().sum()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "gender                         0\n",
       "race/ethnicity                 0\n",
       "parental level of education    0\n",
       "lunch                          0\n",
       "test preparation course        0\n",
       "math score                     0\n",
       "reading score                  0\n",
       "writing score                  0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "hist = thinkstats2.Hist(df.gender, label='gender comparison')\n",
    "thinkplot.Hist(hist)\n",
    "thinkplot.Show(xlabel='gender', ylabel='frequency')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "hist = thinkstats2.Hist(df['race/ethnicity'], label='race/ethnicity')\n",
    "thinkplot.Hist(hist)\n",
    "thinkplot.Show(xlabel='ethnicity groups', ylabel='frequency')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "hist = thinkstats2.Hist(df['parental level of education'], label='parental education')\n",
    "thinkplot.Hist(hist)\n",
    "thinkplot.Show(xlabel='level of education', ylabel='frequency')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "hist = thinkstats2.Hist(df['lunch'], label='lunch')\n",
    "thinkplot.Hist(hist)\n",
    "thinkplot.Show(xlabel='lunch type', ylabel='frequency')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "hist = thinkstats2.Hist(df['test preparation course'], label='test preparation')\n",
    "thinkplot.Hist(hist)\n",
    "thinkplot.Show(xlabel='status', ylabel='frequency')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "There does not seem to be an outlier"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "df['math score'].mean() , df['reading score'].mean() , df['writing score'].mean()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(66.089, 69.169, 68.054)"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "df['math score'].var() , df['reading score'].var() , df['writing score'].var()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(229.91899799799847, 213.1656046046047, 230.90799199199168)"
      ]
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "source": [
    "df['math score'].std() , df['reading score'].std() , df['writing score'].std()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(15.163080096009468, 14.60019193725222, 15.195657010869642)"
      ]
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "source": [
    "df['math score'].mode() , df['reading score'].mode() , df['writing score'].mode()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(0    65\n",
       " dtype: int64, 0    72\n",
       " dtype: int64, 0    74\n",
       " dtype: int64)"
      ]
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "source": [
    "hist = thinkstats2.Hist(df['math score'], label='math score')\n",
    "thinkplot.Hist(hist)\n",
    "thinkplot.Show(xlabel='marks', ylabel='frequency')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "hist = thinkstats2.Hist(df['reading score'], label='reading score')\n",
    "thinkplot.Hist(hist)\n",
    "thinkplot.Show(xlabel='marks', ylabel='frequency')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "source": [
    "hist = thinkstats2.Hist(df['writing score'], label='writing score')\n",
    "thinkplot.Hist(hist)\n",
    "thinkplot.Show(xlabel='marks', ylabel='frequency')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "data split by gender"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "source": [
    "female = df[df.gender == 'female']\n",
    "male = df[df.gender == 'male']"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Plotting math score by gender"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [
    "female_hist = thinkstats2.Hist(female['math score'], label='female math score')\n",
    "male_hist = thinkstats2.Hist(male['math score'], label='male math score')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "source": [
    "width = 0.45\n",
    "thinkplot.PrePlot(2)\n",
    "thinkplot.Hist(female_hist, align='right', width=width)\n",
    "thinkplot.Hist(male_hist, align='left', width=width)\n",
    "thinkplot.Config(xlabel='marks', ylabel='Count')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Plotting reading score by gender"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "source": [
    "female_hist = thinkstats2.Hist(female['reading score'], label='female reading score')\n",
    "male_hist = thinkstats2.Hist(male['reading score'], label='male reading score')\n",
    "width = 0.45\n",
    "thinkplot.PrePlot(2)\n",
    "thinkplot.Hist(female_hist, align='right', width=width)\n",
    "thinkplot.Hist(male_hist, align='left', width=width)\n",
    "thinkplot.Config(xlabel='marks', ylabel='Count')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "source": [
    "Plotting writing score by gender"
   ],
   "outputs": [
    {
     "output_type": "error",
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-29-f30d60ec1a19>, line 1)",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-29-f30d60ec1a19>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    Plotting writing score by gender\u001b[0m\n\u001b[0m                   ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "source": [
    "female_hist = thinkstats2.Hist(female['writing score'], label='female writing score')\n",
    "male_hist = thinkstats2.Hist(male['writing score'], label='male writing score')\n",
    "width = 0.45\n",
    "thinkplot.PrePlot(2)\n",
    "thinkplot.Hist(female_hist, align='right', width=width)\n",
    "thinkplot.Hist(male_hist, align='left', width=width)\n",
    "thinkplot.Config(xlabel='marks', ylabel='Count')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "data split by race/ethnicity"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "source": [
    "groupA = df[df['race/ethnicity'] == 'group A']\n",
    "groupB = df[df['race/ethnicity'] == 'group B']\n",
    "groupC = df[df['race/ethnicity'] == 'group C']\n",
    "groupD = df[df['race/ethnicity'] == 'group D']"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "source": [
    "groupA_hist = thinkstats2.Hist(groupA['math score'], label='groupA math score')\n",
    "groupB_hist = thinkstats2.Hist(groupB['math score'], label='groupB math score')\n",
    "groupC_hist = thinkstats2.Hist(groupC['math score'], label='groupC math score')\n",
    "groupD_hist = thinkstats2.Hist(groupD['math score'], label='groupD math score')\n",
    "width = 0.45\n",
    "thinkplot.PrePlot(4)\n",
    "thinkplot.Hist(groupA_hist, align='right', width=width)\n",
    "thinkplot.Hist(groupB_hist, align='left', width=width)\n",
    "thinkplot.Hist(groupC_hist, align='right', width=width)\n",
    "thinkplot.Hist(groupD_hist, align='left', width=width)\n",
    "thinkplot.Config(xlabel='marks', ylabel='Count')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## PMFs"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "source": [
    "female_pmf = thinkstats2.Pmf(female['math score'], label='female math score')\n",
    "male_pmf = thinkstats2.Pmf(male['math score'], label='male math score')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "source": [
    "width=0.45\n",
    "thinkplot.PrePlot(2, cols=2)\n",
    "thinkplot.Hist(female_pmf, align='right', width=width)\n",
    "thinkplot.Hist(male_pmf, align='left', width=width)\n",
    "thinkplot.Config(xlabel='math score', ylabel='PMF')\n",
    "\n",
    "thinkplot.PrePlot(2)\n",
    "thinkplot.SubPlot(2)\n",
    "thinkplot.Pmfs([female_pmf, male_pmf])\n",
    "thinkplot.Config(xlabel='math score')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "source": [
    "marks = range(0, 100)\n",
    "diffs = []\n",
    "for mark in marks:\n",
    "    p1 = female_pmf.Prob(mark)\n",
    "    p2 = male_pmf.Prob(mark)\n",
    "    diff = 100 * (p1 - p2)\n",
    "    diffs.append(diff)\n",
    "\n",
    "thinkplot.Bar(marks, diffs)\n",
    "thinkplot.Config(xlabel='math score', ylabel='Difference (percentage points)')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## CDFs"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "source": [
    "preparation_completed = df[df['test preparation course'] == 'completed']\n",
    "preparation_none = df[df['test preparation course'] == 'none']"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "source": [
    "preparation_completed_cdf = thinkstats2.Cdf(preparation_completed['math score'], label='preparation completed')\n",
    "\n",
    "preparation_none_cdf = thinkstats2.Cdf(preparation_none['math score'], label='preparation none')\n",
    "\n",
    "thinkplot.PrePlot(2)\n",
    "thinkplot.Cdfs([preparation_completed_cdf, preparation_none_cdf])\n",
    "thinkplot.Show(xlabel='math score', ylabel='CDF')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 576x432 with 0 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Using CDFs we are trying to understand if test preparation had a positive effect on students math marks\n",
    "and as we can see the CDF plot above, students who completed the test preparation consistantly scored more marks\n",
    "than the students who did not complete the test preparation course."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Analytical distribution"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "source": [
    "mathscore = df['math score']"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "source": [
    "mu, var = thinkstats2.TrimmedMeanVar(mathscore, p=0.01)\n",
    "print('Mean, Var', mu, var)\n",
    "    \n",
    "# plot the model\n",
    "sigma = np.sqrt(var)\n",
    "print('Sigma', sigma)\n",
    "xs, ps = thinkstats2.RenderNormalCdf(mu, sigma, low=0, high=12.5)\n",
    "\n",
    "thinkplot.Plot(xs, ps, label='model', color='0.6')\n",
    "\n",
    "# plot the data\n",
    "cdf = thinkstats2.Cdf(mathscore, label='data')\n",
    "\n",
    "thinkplot.PrePlot(1)\n",
    "thinkplot.Cdf(cdf) \n",
    "thinkplot.Config(title='math score',\n",
    "                 xlabel='mathscore',\n",
    "                 ylabel='CDF')"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Mean, Var 66.22244897959183 199.86276134943776\n",
      "Sigma 14.137282672049738\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Here's the observed CDF and the model. The model fits the data well"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "The following example shows that if the data are actually from a normal distribution, the plot is straight except in the left  and right tail"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "source": [
    "mean, var = thinkstats2.TrimmedMeanVar(mathscore, p=0.01)\n",
    "std = np.sqrt(var)\n",
    "\n",
    "xs = [-4, 4]\n",
    "fxs, fys = thinkstats2.FitLine(xs, mean, std)\n",
    "thinkplot.Plot(fxs, fys, linewidth=4, color='0.8')\n",
    "\n",
    "xs, ys = thinkstats2.NormalProbability(mathscore)\n",
    "thinkplot.Plot(xs, ys, label='all live')\n",
    "\n",
    "thinkplot.Config(title='Normal probability plot',\n",
    "                 xlabel='Standard deviations from mean',\n",
    "                 ylabel='Math Score')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Below we are plotting all math scores and of students who did not do any test preparation"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "source": [
    "\n",
    "no_prep_mathscore = preparation_none['math score']"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "source": [
    "mean, var = thinkstats2.TrimmedMeanVar(mathscore, p=0.01)\n",
    "std = np.sqrt(var)\n",
    "\n",
    "xs = [-4, 4]\n",
    "fxs, fys = thinkstats2.FitLine(xs, mean, std)\n",
    "thinkplot.Plot(fxs, fys, linewidth=4, color='0.8')\n",
    "\n",
    "thinkplot.PrePlot(2) \n",
    "xs, ys = thinkstats2.NormalProbability(mathscore)\n",
    "thinkplot.Plot(xs, ys, label='All math score')\n",
    "\n",
    "xs, ys = thinkstats2.NormalProbability(no_prep_mathscore)\n",
    "thinkplot.Plot(xs, ys, label='No Prep math score')\n",
    "thinkplot.Config(title='Normal probability plot',\n",
    "                 xlabel='Standard deviations from mean',\n",
    "                 ylabel='math score')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Scatter plots"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "source": [
    "test_prep = df['test preparation course']"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "source": [
    "thinkplot.Scatter(mathscore, test_prep, alpha=1)\n",
    "thinkplot.Config(xlabel='math score',\n",
    "                 ylabel='test preparartion',\n",
    "                 legend=False)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "source": [
    "thinkplot.Scatter(mathscore, df.gender, alpha=1)\n",
    "thinkplot.Config(xlabel='math score',\n",
    "                 ylabel='df.gender',\n",
    "                 legend=False)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "source": [
    "thinkplot.Scatter(mathscore, df['race/ethnicity'], alpha=1)\n",
    "thinkplot.Config(xlabel='math score',\n",
    "                 ylabel='race/ethnicity',\n",
    "                 legend=False)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "The following function computes the covariance of two variables using NumPy's dot function."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "source": [
    "def Cov(xs, ys, meanx=None, meany=None):\n",
    "    xs = np.asarray(xs)\n",
    "    ys = np.asarray(ys)\n",
    "\n",
    "    if meanx is None:\n",
    "        meanx = np.mean(xs)\n",
    "    if meany is None:\n",
    "        meany = np.mean(ys)\n",
    "\n",
    "    cov = np.dot(xs-meanx, ys-meany) / len(xs)\n",
    "    return cov"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "source": [
    "Cov(df['reading score'],df['writing score'])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "211.57487400000002"
      ]
     },
     "metadata": {},
     "execution_count": 50
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "source": [
    "Cov(df['math score'],df['writing score'])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "184.75419399999998"
      ]
     },
     "metadata": {},
     "execution_count": 51
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "source": [
    "Cov(df['math score'],df['reading score'])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "180.817959"
      ]
     },
     "metadata": {},
     "execution_count": 52
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "source": [
    "np.corrcoef(df['reading score'],df['writing score'])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[1.        , 0.95459808],\n",
       "       [0.95459808, 1.        ]])"
      ]
     },
     "metadata": {},
     "execution_count": 53
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "source": [
    "np.corrcoef(df['math score'],df['writing score'])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[1.        , 0.80264205],\n",
       "       [0.80264205, 1.        ]])"
      ]
     },
     "metadata": {},
     "execution_count": 54
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "source": [
    "np.corrcoef(df['math score'],df['reading score'])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[1.        , 0.81757966],\n",
       "       [0.81757966, 1.        ]])"
      ]
     },
     "metadata": {},
     "execution_count": 55
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "source": [
    "def SpearmanCorr(xs, ys):\n",
    "    xs = pd.Series(xs)\n",
    "    ys = pd.Series(ys)\n",
    "    return xs.corr(ys, method='spearman')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "source": [
    "SpearmanCorr(mathscore, test_prep)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "-0.16980123136105515"
      ]
     },
     "metadata": {},
     "execution_count": 61
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "source": [
    "SpearmanCorr(mathscore, df.gender)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.15997152360954472"
      ]
     },
     "metadata": {},
     "execution_count": 62
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "source": [
    "SpearmanCorr(mathscore, df['race/ethnicity'])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.22043575725337788"
      ]
     },
     "metadata": {},
     "execution_count": 63
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "source": [
    "SpearmanCorr(mathscore, df['parental level of education'])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "-0.05729430219255867"
      ]
     },
     "metadata": {},
     "execution_count": 64
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "source": [
    "SpearmanCorr(mathscore, df['lunch'])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.3373548156961253"
      ]
     },
     "metadata": {},
     "execution_count": 65
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Using the spearman rank correlation, we can see from the above examples that\n",
    "#a. mathscore, test_prep - have a negative correlation\n",
    "#b. mathscore, df.gender - have a positive correlation\n",
    "#c. mathscore, race/ethnicity - have a positive corelation.\n",
    "#d. mathscore, parental level of education - have a negative corelation.\n",
    "#e. mathscore, lunch - have a positive corelation.\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Hypothesis Testing"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "source": [
    "class DiffMeansPermute(thinkstats2.HypothesisTest):\n",
    "\n",
    "    def TestStatistic(self, data):\n",
    "        group1, group2 = data\n",
    "        test_stat = abs(group1.mean() - group2.mean())\n",
    "        return test_stat\n",
    "\n",
    "    def MakeModel(self):\n",
    "        group1, group2 = self.data\n",
    "        self.n, self.m = len(group1), len(group2)\n",
    "        self.pool = np.hstack((group1, group2))\n",
    "\n",
    "    def RunModel(self):\n",
    "        np.random.shuffle(self.pool)\n",
    "        data = self.pool[:self.n], self.pool[self.n:]\n",
    "        return data"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "source": [
    "data = preparation_completed['math score'].values, preparation_none['math score'].values\n",
    "ht = DiffMeansPermute(data)\n",
    "pvalue = ht.PValue()\n",
    "pvalue"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "metadata": {},
     "execution_count": 67
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "The p-value is 0, which means the chance of null hypothesis being true is 0%."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Regression Analysis"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "source": [
    "df.rename(columns = {'math score':'math_score'}, inplace = True)\n",
    "df.rename(columns = {'test preparation course':'test_preparation_course'}, inplace = True)\n",
    "df.rename(columns = {'race/ethnicity':'race_ethnicity'}, inplace = True)\n",
    "df.rename(columns = {'parental level of education':'parental_level_of_education'}, inplace = True)\n",
    "df.rename(columns = {'reading score':'reading_score'}, inplace = True)\n",
    "df.rename(columns = {'writing score':'writing_score'}, inplace = True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### simple regression"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "source": [
    "import statsmodels.formula.api as smf\n",
    "\n",
    "formula = 'math_score~test_preparation_course'\n",
    "model = smf.ols(formula, data=df)\n",
    "results = model.fit()\n",
    "results.summary()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>math_score</td>    <th>  R-squared:         </th> <td>   0.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   32.54</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 16 Nov 2019</td> <th>  Prob (F-statistic):</th> <td>1.54e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:36:16</td>     <th>  Log-Likelihood:    </th> <td> -4121.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th> <td>   8247.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   998</td>      <th>  BIC:               </th> <td>   8256.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                    <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                       <td>   69.6955</td> <td>    0.789</td> <td>   88.330</td> <td> 0.000</td> <td>   68.147</td> <td>   71.244</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>test_preparation_course[T.none]</th> <td>   -5.6176</td> <td>    0.985</td> <td>   -5.705</td> <td> 0.000</td> <td>   -7.550</td> <td>   -3.685</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>13.857</td> <th>  Durbin-Watson:     </th> <td>   2.086</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>  14.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.270</td> <th>  Prob(JB):          </th> <td>0.000862</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.219</td> <th>  Cond. No.          </th> <td>    3.10</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             math_score   R-squared:                       0.032\n",
       "Model:                            OLS   Adj. R-squared:                  0.031\n",
       "Method:                 Least Squares   F-statistic:                     32.54\n",
       "Date:                Sat, 16 Nov 2019   Prob (F-statistic):           1.54e-08\n",
       "Time:                        22:36:16   Log-Likelihood:                -4121.3\n",
       "No. Observations:                1000   AIC:                             8247.\n",
       "Df Residuals:                     998   BIC:                             8256.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================================\n",
       "                                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------------\n",
       "Intercept                          69.6955      0.789     88.330      0.000      68.147      71.244\n",
       "test_preparation_course[T.none]    -5.6176      0.985     -5.705      0.000      -7.550      -3.685\n",
       "==============================================================================\n",
       "Omnibus:                       13.857   Durbin-Watson:                   2.086\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               14.112\n",
       "Skew:                          -0.270   Prob(JB):                     0.000862\n",
       "Kurtosis:                       3.219   Cond. No.                         3.10\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "execution_count": 69
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "From the above we can see the slope and the intercept are statistically significant, which means that they were unlikely to occur by chance, but the R2 value for this model is small.\n",
    "which means that \"test preparation course\" doesn’t account for a substantial part of the variation in math score."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "source": [
    "formula = 'math_score~lunch'\n",
    "model = smf.ols(formula, data=df)\n",
    "results = model.fit()\n",
    "results.summary()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>math_score</td>    <th>  R-squared:         </th> <td>   0.123</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   140.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 16 Nov 2019</td> <th>  Prob (F-statistic):</th> <td>2.41e-30</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:36:17</td>     <th>  Log-Likelihood:    </th> <td> -4071.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th> <td>   8147.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   998</td>      <th>  BIC:               </th> <td>   8157.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>   58.9211</td> <td>    0.754</td> <td>   78.146</td> <td> 0.000</td> <td>   57.442</td> <td>   60.401</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lunch[T.standard]</th> <td>   11.1130</td> <td>    0.939</td> <td>   11.837</td> <td> 0.000</td> <td>    9.271</td> <td>   12.955</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.905</td> <th>  Durbin-Watson:     </th> <td>   2.086</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.032</td> <th>  Jarque-Bera (JB):  </th> <td>   6.796</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.192</td> <th>  Prob(JB):          </th> <td>  0.0334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.123</td> <th>  Cond. No.          </th> <td>    3.12</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             math_score   R-squared:                       0.123\n",
       "Model:                            OLS   Adj. R-squared:                  0.122\n",
       "Method:                 Least Squares   F-statistic:                     140.1\n",
       "Date:                Sat, 16 Nov 2019   Prob (F-statistic):           2.41e-30\n",
       "Time:                        22:36:17   Log-Likelihood:                -4071.6\n",
       "No. Observations:                1000   AIC:                             8147.\n",
       "Df Residuals:                     998   BIC:                             8157.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept            58.9211      0.754     78.146      0.000      57.442      60.401\n",
       "lunch[T.standard]    11.1130      0.939     11.837      0.000       9.271      12.955\n",
       "==============================================================================\n",
       "Omnibus:                        6.905   Durbin-Watson:                   2.086\n",
       "Prob(Omnibus):                  0.032   Jarque-Bera (JB):                6.796\n",
       "Skew:                          -0.192   Prob(JB):                       0.0334\n",
       "Kurtosis:                       3.123   Cond. No.                         3.12\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "execution_count": 70
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "From the above we can see the slope and the intercept are statistically significant, which means that they were unlik ely to occur by chance, and we see the R2 value for this model also significant. which means that \"lunch\"  accounts for a 12% of the variation in math score."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### multiple regression"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "source": [
    "formula = 'math_score~test_preparation_course+race_ethnicity'\n",
    "model = smf.ols(formula, data=df)\n",
    "results = model.fit()\n",
    "results.summary()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>math_score</td>    <th>  R-squared:         </th> <td>   0.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.080</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   18.29</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 16 Nov 2019</td> <th>  Prob (F-statistic):</th> <td>2.12e-17</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:36:19</td>     <th>  Log-Likelihood:    </th> <td> -4093.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th> <td>   8199.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   994</td>      <th>  BIC:               </th> <td>   8228.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                    <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                       <td>   65.1372</td> <td>    1.665</td> <td>   39.132</td> <td> 0.000</td> <td>   61.871</td> <td>   68.404</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>test_preparation_course[T.none]</th> <td>   -5.3830</td> <td>    0.962</td> <td>   -5.595</td> <td> 0.000</td> <td>   -7.271</td> <td>   -3.495</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group B]</th>       <td>    1.7718</td> <td>    1.869</td> <td>    0.948</td> <td> 0.343</td> <td>   -1.895</td> <td>    5.439</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group C]</th>       <td>    2.7354</td> <td>    1.744</td> <td>    1.569</td> <td> 0.117</td> <td>   -0.687</td> <td>    6.158</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group D]</th>       <td>    5.9236</td> <td>    1.785</td> <td>    3.318</td> <td> 0.001</td> <td>    2.421</td> <td>    9.427</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group E]</th>       <td>   11.7602</td> <td>    1.974</td> <td>    5.959</td> <td> 0.000</td> <td>    7.887</td> <td>   15.633</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>19.128</td> <th>  Durbin-Watson:     </th> <td>   2.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  19.970</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.315</td> <th>  Prob(JB):          </th> <td>4.61e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.286</td> <th>  Cond. No.          </th> <td>    10.2</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             math_score   R-squared:                       0.084\n",
       "Model:                            OLS   Adj. R-squared:                  0.080\n",
       "Method:                 Least Squares   F-statistic:                     18.29\n",
       "Date:                Sat, 16 Nov 2019   Prob (F-statistic):           2.12e-17\n",
       "Time:                        22:36:19   Log-Likelihood:                -4093.3\n",
       "No. Observations:                1000   AIC:                             8199.\n",
       "Df Residuals:                     994   BIC:                             8228.\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================================\n",
       "                                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------------\n",
       "Intercept                          65.1372      1.665     39.132      0.000      61.871      68.404\n",
       "test_preparation_course[T.none]    -5.3830      0.962     -5.595      0.000      -7.271      -3.495\n",
       "race_ethnicity[T.group B]           1.7718      1.869      0.948      0.343      -1.895       5.439\n",
       "race_ethnicity[T.group C]           2.7354      1.744      1.569      0.117      -0.687       6.158\n",
       "race_ethnicity[T.group D]           5.9236      1.785      3.318      0.001       2.421       9.427\n",
       "race_ethnicity[T.group E]          11.7602      1.974      5.959      0.000       7.887      15.633\n",
       "==============================================================================\n",
       "Omnibus:                       19.128   Durbin-Watson:                   2.047\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               19.970\n",
       "Skew:                          -0.315   Prob(JB):                     4.61e-05\n",
       "Kurtosis:                       3.286   Cond. No.                         10.2\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "execution_count": 71
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "From the above we can see the slope and the intercept are statistically significant, which means that they were unlikely to occur by chance, but the the R2 value for this model is still small but bigger when we added race/ethnicity along with test preparation course. which means that race/ethnicity along with test preparation course increases the effoect on the math marks but yet not statristically significant."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "source": [
    "formula = 'math_score~test_preparation_course+race_ethnicity+lunch'\n",
    "model = smf.ols(formula, data=df)\n",
    "results = model.fit()\n",
    "results.summary()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>math_score</td>    <th>  R-squared:         </th> <td>   0.202</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.197</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   41.80</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 16 Nov 2019</td> <th>  Prob (F-statistic):</th> <td>1.41e-45</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:36:20</td>     <th>  Log-Likelihood:    </th> <td> -4024.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th> <td>   8063.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   993</td>      <th>  BIC:               </th> <td>   8098.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                    <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                       <td>   58.8067</td> <td>    1.641</td> <td>   35.839</td> <td> 0.000</td> <td>   55.587</td> <td>   62.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>test_preparation_course[T.none]</th> <td>   -5.6042</td> <td>    0.899</td> <td>   -6.234</td> <td> 0.000</td> <td>   -7.368</td> <td>   -3.840</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group B]</th>       <td>    1.3203</td> <td>    1.746</td> <td>    0.756</td> <td> 0.450</td> <td>   -2.106</td> <td>    4.746</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group C]</th>       <td>    2.2189</td> <td>    1.630</td> <td>    1.362</td> <td> 0.174</td> <td>   -0.979</td> <td>    5.417</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group D]</th>       <td>    5.4759</td> <td>    1.668</td> <td>    3.283</td> <td> 0.001</td> <td>    2.203</td> <td>    8.749</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group E]</th>       <td>   10.5287</td> <td>    1.846</td> <td>    5.702</td> <td> 0.000</td> <td>    6.905</td> <td>   14.152</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lunch[T.standard]</th>               <td>   10.8725</td> <td>    0.900</td> <td>   12.083</td> <td> 0.000</td> <td>    9.107</td> <td>   12.638</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 8.744</td> <th>  Durbin-Watson:     </th> <td>   2.072</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.013</td> <th>  Jarque-Bera (JB):  </th> <td>   8.721</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.226</td> <th>  Prob(JB):          </th> <td>  0.0128</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.070</td> <th>  Cond. No.          </th> <td>    11.4</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             math_score   R-squared:                       0.202\n",
       "Model:                            OLS   Adj. R-squared:                  0.197\n",
       "Method:                 Least Squares   F-statistic:                     41.80\n",
       "Date:                Sat, 16 Nov 2019   Prob (F-statistic):           1.41e-45\n",
       "Time:                        22:36:20   Log-Likelihood:                -4024.7\n",
       "No. Observations:                1000   AIC:                             8063.\n",
       "Df Residuals:                     993   BIC:                             8098.\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================================\n",
       "                                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------------\n",
       "Intercept                          58.8067      1.641     35.839      0.000      55.587      62.027\n",
       "test_preparation_course[T.none]    -5.6042      0.899     -6.234      0.000      -7.368      -3.840\n",
       "race_ethnicity[T.group B]           1.3203      1.746      0.756      0.450      -2.106       4.746\n",
       "race_ethnicity[T.group C]           2.2189      1.630      1.362      0.174      -0.979       5.417\n",
       "race_ethnicity[T.group D]           5.4759      1.668      3.283      0.001       2.203       8.749\n",
       "race_ethnicity[T.group E]          10.5287      1.846      5.702      0.000       6.905      14.152\n",
       "lunch[T.standard]                  10.8725      0.900     12.083      0.000       9.107      12.638\n",
       "==============================================================================\n",
       "Omnibus:                        8.744   Durbin-Watson:                   2.072\n",
       "Prob(Omnibus):                  0.013   Jarque-Bera (JB):                8.721\n",
       "Skew:                          -0.226   Prob(JB):                       0.0128\n",
       "Kurtosis:                       3.070   Cond. No.                         11.4\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "execution_count": 72
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "From the above we can see test_preparation_course+race_ethnicity+lunch accounts for 20% (R2 0.202) of the variation in math score"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "source": [
    "formula = 'math_score~test_preparation_course+race_ethnicity+lunch+parental_level_of_education+gender'\n",
    "model = smf.ols(formula, data=df)\n",
    "results = model.fit()\n",
    "results.summary()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>math_score</td>    <th>  R-squared:         </th> <td>   0.255</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.246</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   28.12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 16 Nov 2019</td> <th>  Prob (F-statistic):</th> <td>2.62e-55</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:36:21</td>     <th>  Log-Likelihood:    </th> <td> -3990.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th> <td>   8007.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   987</td>      <th>  BIC:               </th> <td>   8070.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    12</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                          <td></td>                            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                                        <td>   57.6305</td> <td>    1.872</td> <td>   30.784</td> <td> 0.000</td> <td>   53.957</td> <td>   61.304</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>test_preparation_course[T.none]</th>                  <td>   -5.4947</td> <td>    0.876</td> <td>   -6.275</td> <td> 0.000</td> <td>   -7.213</td> <td>   -3.776</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group B]</th>                        <td>    2.0408</td> <td>    1.700</td> <td>    1.201</td> <td> 0.230</td> <td>   -1.295</td> <td>    5.376</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group C]</th>                        <td>    2.4700</td> <td>    1.592</td> <td>    1.552</td> <td> 0.121</td> <td>   -0.654</td> <td>    5.594</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group D]</th>                        <td>    5.3410</td> <td>    1.624</td> <td>    3.289</td> <td> 0.001</td> <td>    2.154</td> <td>    8.528</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group E]</th>                        <td>   10.1347</td> <td>    1.802</td> <td>    5.626</td> <td> 0.000</td> <td>    6.599</td> <td>   13.670</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lunch[T.standard]</th>                                <td>   10.8768</td> <td>    0.873</td> <td>   12.463</td> <td> 0.000</td> <td>    9.164</td> <td>   12.589</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>parental_level_of_education[T.bachelor's degree]</th> <td>    1.9661</td> <td>    1.502</td> <td>    1.309</td> <td> 0.191</td> <td>   -0.981</td> <td>    4.913</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>parental_level_of_education[T.high school]</th>       <td>   -4.8027</td> <td>    1.297</td> <td>   -3.703</td> <td> 0.000</td> <td>   -7.348</td> <td>   -2.257</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>parental_level_of_education[T.master's degree]</th>   <td>    2.8884</td> <td>    1.938</td> <td>    1.490</td> <td> 0.136</td> <td>   -0.915</td> <td>    6.692</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>parental_level_of_education[T.some college]</th>      <td>   -0.5827</td> <td>    1.247</td> <td>   -0.467</td> <td> 0.640</td> <td>   -3.030</td> <td>    1.864</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>parental_level_of_education[T.some high school]</th>  <td>   -4.2487</td> <td>    1.333</td> <td>   -3.187</td> <td> 0.001</td> <td>   -6.865</td> <td>   -1.633</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>gender[T.male]</th>                                   <td>    4.9953</td> <td>    0.839</td> <td>    5.954</td> <td> 0.000</td> <td>    3.349</td> <td>    6.642</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 9.026</td> <th>  Durbin-Watson:     </th> <td>   2.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.011</td> <th>  Jarque-Bera (JB):  </th> <td>   9.201</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.232</td> <th>  Prob(JB):          </th> <td>  0.0100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.928</td> <th>  Cond. No.          </th> <td>    12.9</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             math_score   R-squared:                       0.255\n",
       "Model:                            OLS   Adj. R-squared:                  0.246\n",
       "Method:                 Least Squares   F-statistic:                     28.12\n",
       "Date:                Sat, 16 Nov 2019   Prob (F-statistic):           2.62e-55\n",
       "Time:                        22:36:21   Log-Likelihood:                -3990.3\n",
       "No. Observations:                1000   AIC:                             8007.\n",
       "Df Residuals:                     987   BIC:                             8070.\n",
       "Df Model:                          12                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================================================\n",
       "                                                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------------------------------\n",
       "Intercept                                           57.6305      1.872     30.784      0.000      53.957      61.304\n",
       "test_preparation_course[T.none]                     -5.4947      0.876     -6.275      0.000      -7.213      -3.776\n",
       "race_ethnicity[T.group B]                            2.0408      1.700      1.201      0.230      -1.295       5.376\n",
       "race_ethnicity[T.group C]                            2.4700      1.592      1.552      0.121      -0.654       5.594\n",
       "race_ethnicity[T.group D]                            5.3410      1.624      3.289      0.001       2.154       8.528\n",
       "race_ethnicity[T.group E]                           10.1347      1.802      5.626      0.000       6.599      13.670\n",
       "lunch[T.standard]                                   10.8768      0.873     12.463      0.000       9.164      12.589\n",
       "parental_level_of_education[T.bachelor's degree]     1.9661      1.502      1.309      0.191      -0.981       4.913\n",
       "parental_level_of_education[T.high school]          -4.8027      1.297     -3.703      0.000      -7.348      -2.257\n",
       "parental_level_of_education[T.master's degree]       2.8884      1.938      1.490      0.136      -0.915       6.692\n",
       "parental_level_of_education[T.some college]         -0.5827      1.247     -0.467      0.640      -3.030       1.864\n",
       "parental_level_of_education[T.some high school]     -4.2487      1.333     -3.187      0.001      -6.865      -1.633\n",
       "gender[T.male]                                       4.9953      0.839      5.954      0.000       3.349       6.642\n",
       "==============================================================================\n",
       "Omnibus:                        9.026   Durbin-Watson:                   2.043\n",
       "Prob(Omnibus):                  0.011   Jarque-Bera (JB):                9.201\n",
       "Skew:                          -0.232   Prob(JB):                       0.0100\n",
       "Kurtosis:                       2.928   Cond. No.                         12.9\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "execution_count": 73
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "From the above we can see test_preparation_course+race_ethnicity+lunch+parental_level_of_education+gender accounts for 25% (R2 0.255) of the variation in math score"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "source": [
    "formula = 'reading_score~test_preparation_course+race_ethnicity+lunch+parental_level_of_education+gender'\n",
    "model = smf.ols(formula, data=df)\n",
    "results = model.fit()\n",
    "results.summary()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>reading_score</td>  <th>  R-squared:         </th> <td>   0.227</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.218</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   24.19</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 16 Nov 2019</td> <th>  Prob (F-statistic):</th> <td>8.62e-48</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:36:22</td>     <th>  Log-Likelihood:    </th> <td> -3970.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th> <td>   7967.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   987</td>      <th>  BIC:               </th> <td>   8031.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    12</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                          <td></td>                            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                                        <td>   71.2776</td> <td>    1.836</td> <td>   38.832</td> <td> 0.000</td> <td>   67.676</td> <td>   74.880</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>test_preparation_course[T.none]</th>                  <td>   -7.3625</td> <td>    0.859</td> <td>   -8.576</td> <td> 0.000</td> <td>   -9.047</td> <td>   -5.678</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group B]</th>                        <td>    1.3260</td> <td>    1.667</td> <td>    0.796</td> <td> 0.426</td> <td>   -1.944</td> <td>    4.596</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group C]</th>                        <td>    2.2739</td> <td>    1.561</td> <td>    1.457</td> <td> 0.145</td> <td>   -0.789</td> <td>    5.337</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group D]</th>                        <td>    4.1056</td> <td>    1.592</td> <td>    2.578</td> <td> 0.010</td> <td>    0.981</td> <td>    7.230</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group E]</th>                        <td>    5.5135</td> <td>    1.766</td> <td>    3.121</td> <td> 0.002</td> <td>    2.047</td> <td>    8.980</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lunch[T.standard]</th>                                <td>    7.2458</td> <td>    0.856</td> <td>    8.468</td> <td> 0.000</td> <td>    5.567</td> <td>    8.925</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>parental_level_of_education[T.bachelor's degree]</th> <td>    2.1559</td> <td>    1.473</td> <td>    1.464</td> <td> 0.144</td> <td>   -0.734</td> <td>    5.046</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>parental_level_of_education[T.high school]</th>       <td>   -4.9004</td> <td>    1.272</td> <td>   -3.853</td> <td> 0.000</td> <td>   -7.396</td> <td>   -2.405</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>parental_level_of_education[T.master's degree]</th>   <td>    4.2051</td> <td>    1.900</td> <td>    2.213</td> <td> 0.027</td> <td>    0.476</td> <td>    7.934</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>parental_level_of_education[T.some college]</th>      <td>   -1.2795</td> <td>    1.223</td> <td>   -1.046</td> <td> 0.296</td> <td>   -3.679</td> <td>    1.120</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>parental_level_of_education[T.some high school]</th>  <td>   -4.0490</td> <td>    1.307</td> <td>   -3.098</td> <td> 0.002</td> <td>   -6.614</td> <td>   -1.484</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>gender[T.male]</th>                                   <td>   -7.0714</td> <td>    0.823</td> <td>   -8.596</td> <td> 0.000</td> <td>   -8.686</td> <td>   -5.457</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>11.191</td> <th>  Durbin-Watson:     </th> <td>   2.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.004</td> <th>  Jarque-Bera (JB):  </th> <td>  11.258</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.242</td> <th>  Prob(JB):          </th> <td> 0.00359</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.812</td> <th>  Cond. No.          </th> <td>    12.9</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          reading_score   R-squared:                       0.227\n",
       "Model:                            OLS   Adj. R-squared:                  0.218\n",
       "Method:                 Least Squares   F-statistic:                     24.19\n",
       "Date:                Sat, 16 Nov 2019   Prob (F-statistic):           8.62e-48\n",
       "Time:                        22:36:22   Log-Likelihood:                -3970.5\n",
       "No. Observations:                1000   AIC:                             7967.\n",
       "Df Residuals:                     987   BIC:                             8031.\n",
       "Df Model:                          12                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================================================\n",
       "                                                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------------------------------\n",
       "Intercept                                           71.2776      1.836     38.832      0.000      67.676      74.880\n",
       "test_preparation_course[T.none]                     -7.3625      0.859     -8.576      0.000      -9.047      -5.678\n",
       "race_ethnicity[T.group B]                            1.3260      1.667      0.796      0.426      -1.944       4.596\n",
       "race_ethnicity[T.group C]                            2.2739      1.561      1.457      0.145      -0.789       5.337\n",
       "race_ethnicity[T.group D]                            4.1056      1.592      2.578      0.010       0.981       7.230\n",
       "race_ethnicity[T.group E]                            5.5135      1.766      3.121      0.002       2.047       8.980\n",
       "lunch[T.standard]                                    7.2458      0.856      8.468      0.000       5.567       8.925\n",
       "parental_level_of_education[T.bachelor's degree]     2.1559      1.473      1.464      0.144      -0.734       5.046\n",
       "parental_level_of_education[T.high school]          -4.9004      1.272     -3.853      0.000      -7.396      -2.405\n",
       "parental_level_of_education[T.master's degree]       4.2051      1.900      2.213      0.027       0.476       7.934\n",
       "parental_level_of_education[T.some college]         -1.2795      1.223     -1.046      0.296      -3.679       1.120\n",
       "parental_level_of_education[T.some high school]     -4.0490      1.307     -3.098      0.002      -6.614      -1.484\n",
       "gender[T.male]                                      -7.0714      0.823     -8.596      0.000      -8.686      -5.457\n",
       "==============================================================================\n",
       "Omnibus:                       11.191   Durbin-Watson:                   2.038\n",
       "Prob(Omnibus):                  0.004   Jarque-Bera (JB):               11.258\n",
       "Skew:                          -0.242   Prob(JB):                      0.00359\n",
       "Kurtosis:                       2.812   Cond. No.                         12.9\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "execution_count": 74
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "From the above we can see test_preparation_course+race_ethnicity+lunch+parental_level_of_education+gender accounts for 22.7%  of the variation in reading score"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "source": [
    "formula = 'writing_score~test_preparation_course+race_ethnicity+lunch+parental_level_of_education+gender'\n",
    "model = smf.ols(formula, data=df)\n",
    "results = model.fit()\n",
    "results.summary()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>writing_score</td>  <th>  R-squared:         </th> <td>   0.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.326</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   41.25</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 16 Nov 2019</td> <th>  Prob (F-statistic):</th> <td>8.17e-79</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:36:24</td>     <th>  Log-Likelihood:    </th> <td> -3936.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th> <td>   7898.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   987</td>      <th>  BIC:               </th> <td>   7962.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    12</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                          <td></td>                            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                                        <td>   71.9142</td> <td>    1.774</td> <td>   40.548</td> <td> 0.000</td> <td>   68.434</td> <td>   75.395</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>test_preparation_course[T.none]</th>                  <td>  -10.0587</td> <td>    0.830</td> <td>  -12.125</td> <td> 0.000</td> <td>  -11.687</td> <td>   -8.431</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group B]</th>                        <td>    1.2201</td> <td>    1.610</td> <td>    0.758</td> <td> 0.449</td> <td>   -1.940</td> <td>    4.380</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group C]</th>                        <td>    2.4126</td> <td>    1.508</td> <td>    1.600</td> <td> 0.110</td> <td>   -0.547</td> <td>    5.372</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group D]</th>                        <td>    5.9307</td> <td>    1.539</td> <td>    3.855</td> <td> 0.000</td> <td>    2.911</td> <td>    8.950</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race_ethnicity[T.group E]</th>                        <td>    5.1373</td> <td>    1.707</td> <td>    3.010</td> <td> 0.003</td> <td>    1.788</td> <td>    8.487</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lunch[T.standard]</th>                                <td>    8.2028</td> <td>    0.827</td> <td>    9.921</td> <td> 0.000</td> <td>    6.580</td> <td>    9.825</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>parental_level_of_education[T.bachelor's degree]</th> <td>    3.4849</td> <td>    1.423</td> <td>    2.449</td> <td> 0.014</td> <td>    0.693</td> <td>    6.277</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>parental_level_of_education[T.high school]</th>       <td>   -5.8143</td> <td>    1.229</td> <td>   -4.732</td> <td> 0.000</td> <td>   -8.226</td> <td>   -3.403</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>parental_level_of_education[T.master's degree]</th>   <td>    5.1832</td> <td>    1.836</td> <td>    2.823</td> <td> 0.005</td> <td>    1.580</td> <td>    8.787</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>parental_level_of_education[T.some college]</th>      <td>   -0.9204</td> <td>    1.181</td> <td>   -0.779</td> <td> 0.436</td> <td>   -3.239</td> <td>    1.398</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>parental_level_of_education[T.some high school]</th>  <td>   -5.3222</td> <td>    1.263</td> <td>   -4.214</td> <td> 0.000</td> <td>   -7.801</td> <td>   -2.844</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>gender[T.male]</th>                                   <td>   -9.0965</td> <td>    0.795</td> <td>  -11.444</td> <td> 0.000</td> <td>  -10.656</td> <td>   -7.537</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>16.647</td> <th>  Durbin-Watson:     </th> <td>   2.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  17.222</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.321</td> <th>  Prob(JB):          </th> <td>0.000182</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.982</td> <th>  Cond. No.          </th> <td>    12.9</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          writing_score   R-squared:                       0.334\n",
       "Model:                            OLS   Adj. R-squared:                  0.326\n",
       "Method:                 Least Squares   F-statistic:                     41.25\n",
       "Date:                Sat, 16 Nov 2019   Prob (F-statistic):           8.17e-79\n",
       "Time:                        22:36:24   Log-Likelihood:                -3936.2\n",
       "No. Observations:                1000   AIC:                             7898.\n",
       "Df Residuals:                     987   BIC:                             7962.\n",
       "Df Model:                          12                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================================================\n",
       "                                                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------------------------------\n",
       "Intercept                                           71.9142      1.774     40.548      0.000      68.434      75.395\n",
       "test_preparation_course[T.none]                    -10.0587      0.830    -12.125      0.000     -11.687      -8.431\n",
       "race_ethnicity[T.group B]                            1.2201      1.610      0.758      0.449      -1.940       4.380\n",
       "race_ethnicity[T.group C]                            2.4126      1.508      1.600      0.110      -0.547       5.372\n",
       "race_ethnicity[T.group D]                            5.9307      1.539      3.855      0.000       2.911       8.950\n",
       "race_ethnicity[T.group E]                            5.1373      1.707      3.010      0.003       1.788       8.487\n",
       "lunch[T.standard]                                    8.2028      0.827      9.921      0.000       6.580       9.825\n",
       "parental_level_of_education[T.bachelor's degree]     3.4849      1.423      2.449      0.014       0.693       6.277\n",
       "parental_level_of_education[T.high school]          -5.8143      1.229     -4.732      0.000      -8.226      -3.403\n",
       "parental_level_of_education[T.master's degree]       5.1832      1.836      2.823      0.005       1.580       8.787\n",
       "parental_level_of_education[T.some college]         -0.9204      1.181     -0.779      0.436      -3.239       1.398\n",
       "parental_level_of_education[T.some high school]     -5.3222      1.263     -4.214      0.000      -7.801      -2.844\n",
       "gender[T.male]                                      -9.0965      0.795    -11.444      0.000     -10.656      -7.537\n",
       "==============================================================================\n",
       "Omnibus:                       16.647   Durbin-Watson:                   2.038\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               17.222\n",
       "Skew:                          -0.321   Prob(JB):                     0.000182\n",
       "Kurtosis:                       2.982   Cond. No.                         12.9\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "execution_count": 75
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "From the above we can see test_preparation_course+race_ethnicity+lunch+parental_level_of_education+gender accounts for 33.4%  of the variation in writing score"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
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