Notebooks/students-performance-in-exams-quick-eda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Students Performance in Exames\n",
    "#### Marks secured by the students in various subjects\n",
    "\n",
    "\n",
    "\n",
    "## Inspiration\n",
    "#### To understand the influence of various factors like economic, personal and social on the students performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.stats.multicomp import pairwise_tukeyhsd, MultiComparison\n",
    "from statsmodels.formula.api import ols\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the data from the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../Datasets/StudentsPerformance/StudentsPerformance.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's remove spaces from columns names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df.columns = df.columns.str.replace(\" \", \"_\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some information on the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "    }\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>count</th>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000.00000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>518</td>\n",
       "      <td>319</td>\n",
       "      <td>226</td>\n",
       "      <td>645</td>\n",
       "      <td>642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\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": [
       "        gender race/ethnicity parental_level_of_education     lunch  \\\n",
       "count     1000           1000                        1000      1000   \n",
       "unique       2              5                           6         2   \n",
       "top     female        group C                some college  standard   \n",
       "freq       518            319                         226       645   \n",
       "mean       NaN            NaN                         NaN       NaN   \n",
       "std        NaN            NaN                         NaN       NaN   \n",
       "min        NaN            NaN                         NaN       NaN   \n",
       "25%        NaN            NaN                         NaN       NaN   \n",
       "50%        NaN            NaN                         NaN       NaN   \n",
       "75%        NaN            NaN                         NaN       NaN   \n",
       "max        NaN            NaN                         NaN       NaN   \n",
       "\n",
       "       test_preparation_course  math_score  reading_score  writing_score  \n",
       "count                     1000  1000.00000    1000.000000    1000.000000  \n",
       "unique                       2         NaN            NaN            NaN  \n",
       "top                       none         NaN            NaN            NaN  \n",
       "freq                       642         NaN            NaN            NaN  \n",
       "mean                       NaN    66.08900      69.169000      68.054000  \n",
       "std                        NaN    15.16308      14.600192      15.195657  \n",
       "min                        NaN     0.00000      17.000000      10.000000  \n",
       "25%                        NaN    57.00000      59.000000      57.750000  \n",
       "50%                        NaN    66.00000      70.000000      69.000000  \n",
       "75%                        NaN    77.00000      79.000000      79.000000  \n",
       "max                        NaN   100.00000     100.000000     100.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking if there are some null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Distribution of exam scores')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(16, 5), sharey=True)\n",
    "axs[0].hist(df[\"math_score\"], bins=20)\n",
    "axs[0].set_title(\"Math score\")\n",
    "axs[1].hist(df[\"reading_score\"], bins=20)\n",
    "axs[1].set_title(\"Reading score\")\n",
    "axs[2].hist(df[\"writing_score\"], bins=20)\n",
    "axs[2].set_title(\"Writing score\")\n",
    "fig.suptitle(\"Distribution of exam scores\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2304ab95e48>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, most of the scores seem correlated, but score for writing and reading seem to present the biggest correlation.<br>\n",
    "Let us check this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>math_score</th>\n",
       "      <th>reading_score</th>\n",
       "      <th>writing_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>math_score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.817580</td>\n",
       "      <td>0.802642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reading_score</th>\n",
       "      <td>0.817580</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.954598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>writing_score</th>\n",
       "      <td>0.802642</td>\n",
       "      <td>0.954598</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               math_score  reading_score  writing_score\n",
       "math_score       1.000000       0.817580       0.802642\n",
       "reading_score    0.817580       1.000000       0.954598\n",
       "writing_score    0.802642       0.954598       1.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This results support our previous theory with the biggest correlation scores between reading and writing.\n",
    "\n",
    "Let us check the distribution in the categorical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2304b6722e8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 5, figsize=(16, 5), sharey=True)\n",
    "sns.countplot(df.gender, ax = axs[0])\n",
    "ax0 = sns.countplot(df[\"race/ethnicity\"], ax = axs[1])\n",
    "ax0.set_xticklabels(ax0.get_xticklabels(), rotation=90)\n",
    "ax1 = sns.countplot(df[\"parental_level_of_education\"], ax = axs[2])\n",
    "ax1.set_xticklabels(ax1.get_xticklabels(), rotation=90)\n",
    "sns.countplot(df.lunch, ax = axs[3])\n",
    "sns.countplot(df[\"test_preparation_course\"], ax = axs[4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us see if there is some statistically significantly difference between these groups using 95% confident interval and the null hypothesis being that there is no difference between the groups when comparing scores.\n",
    "\n",
    "### Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Math Scores\n",
      "                 sum_sq     df          F        PR(>F)\n",
      "gender      6481.373854    1.0  28.979336  9.120186e-08\n",
      "Residual  223207.705146  998.0        NaN           NaN\n",
      "\n",
      "Reading Scores\n",
      "                 sum_sq     df          F        PR(>F)\n",
      "gender     12710.843676    1.0  63.350584  4.680539e-15\n",
      "Residual  200241.595324  998.0        NaN           NaN\n",
      "\n",
      "Writing Scores\n",
      "                 sum_sq     df          F        PR(>F)\n",
      "gender     20930.822413    1.0  99.591576  2.019878e-22\n",
      "Residual  209746.261587  998.0        NaN           NaN\n"
     ]
    }
   ],
   "source": [
    "mod = ols(\"math_score ~ gender\", data=df).fit()\n",
    "aov_table = sm.stats.anova_lm(mod, typ=2)\n",
    "print()\n",
    "print(\"Math Scores\")\n",
    "print(aov_table)\n",
    "\n",
    "mod = ols(\"reading_score ~ gender\", data=df).fit()\n",
    "aov_table = sm.stats.anova_lm(mod, typ=2)\n",
    "print()\n",
    "print(\"Reading Scores\")\n",
    "print(aov_table)\n",
    "\n",
    "mod = ols(\"writing_score ~ gender\", data=df).fit()\n",
    "aov_table = sm.stats.anova_lm(mod, typ=2)\n",
    "print()\n",
    "print(\"Writing Scores\")\n",
    "print(aov_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, in the case of gender, the null hypothesis should be rejected in all the exams and, as such, there is a statistically significantly difference between the groups for all of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2304b7b42e8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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88sjdLRVgjxnC3fYVu55dVbeP3gSsraqqByR5cpJThn3f9v84/3eC5bXbR+yq6qTMzrP7WGZH3N68cmjmLp7zo0mu6u5bdb277j6zuzd298YNGzbcmpcA2NNKbwJG8Owkz0/y9u6+sKruluQvb/og/QmW12qGYv5Sd19TVSckeVRmQyhffQvPeVCSx1XVp5P8nyQPq6o/THJlVR2RJMPyqlVXDjAOE6kAa667z+vux3X3/xq2P9ndz9pxf1X95njVAYtgNcHu+mH52CSv7u6zk+x7c0/o7ud393d291FJfjLJe7v73yc5J8mO81ROTnL2qqoGGE+NXQDATjxo7AKAca0m2F1eVb+b5ElJ3j2cY7Ka56/08iSPqKpLkjxi2AaYgvePXQAAwE3t9uQpmQW6Ryf5le6+ehhC+Qs77qyqu3T3l3b15O4+N8m5w/oXkjz81hQMME9V9dyd7P5ykvO7+8PdfYuzAQMArLXVzIr5tSRvW7F9RZIrVjxkc5KbnUwFYAI2Drd3DtuPTfKBJE+rqrO6+4zRKgPYNcPEYcmt5ojdLdFQgPXgO5Lcr7u/miRV9cIkb03y4CTnJxHsgEX062MXAIxrTwY7M8UB68GRSa5bsf0vSb67u79eVd8YqSZgyVXVO/Pt/9f6cpItSX63u9+w5kUBC2VPBjuA9eCPkvxdVe2YrfffJXlTVe2f5OPjlQUsuU8m2ZDkTcP2TyS5Msn3JnlNkqeMVBewIAzFBFihu19SVX+a2dThleRp3b1luPvJ41UGLLn7dveDV2y/s6rO6+4HV9WFo1UFLIxVBbuq2ivJYSuf192XDatmuQTWiw8l+VyGXldVR67odQBj2LCyF1XVkUkOGe67btdPA5bFbge7qnpmkhdmdtj/hmF3JzkuSbr7i3u8OoA1dpNed31mR+2+2esARvJfkryvqv4ps770PUn+0zBMfNOolQELYTVH7J6d5JjhGnQA65VeByyc7n53VR2d5B6ZBbtPdPc/D3f/2niVAYtiNcHuM5nNvgSwnul1wKI6PslRmf3/7biqSnf//rglAYviFoNdVT13WP1kknOr6k+SfHPK7+5+1ZxqAxiDXgcsnKr6gyR3T/LhzIaJJ7Nh4oIdkGT3jtgdMCwvG277DrfEteuA9WdnvQ5gbBuTHNvd/u8F7NQtBrvu/uUkqaqTuvuslfdV1UnzKgxgDDt6HsCC+ViSw5NcMXYhwGJazTl2z09y1m7sA5icqvq17n5OVb0zOxmN0N2PG6EsgB0OSfLxqvp/ufEwcb0JSLJ759g9JsmPJLlrVf3GirsOTLJ9XoUBrLE/GJa/MmoVADv3orELABbb7hyx+1ySLUkel+T8FfuvSfKf51EUwFrr7vOH5V+NXQvATelNwC3ZnXPsPpLkI1X1R939L2tQE8Caq6oLcjMTQnW3C5QDa66q3tfdJ1TVNblxj6ok3d0HjlQasGBWc47dUVX1siTHJtlvx87uvtserwpg7f3osHz6sNwxNPPJSb629uUAJN19wrA84JYeCyy3263isa9P8urMzqt7aGbXTfmDm30GwER096XdfWmSB3X3ad19wXA7Pcmjxq4PWG7DdexucR+wvFYT7O7Q3ZuT1PAfoBcledh8ygIYzf5VdcKOjap6YJL9R6wHIEm+b+VGVe2d5PiRagEW0GqGYv5zVd0uySVV9Ywklyc5dD5lAYzmlCSvq6o7D9tXJ/nZEesBllhVPT/Jf01yh6r6yo7dSa5LcuZohQELZzXB7jlJ7pjkWUlektlwzJ+eR1EAYxlmx7x3VR2Y2QiFL49dE7C8uvtlSV5WVS/r7uePXQ+wuFYT7Dqzc+q+O8k+w77XJDFTHLCuVNVjMxv2tF9VJUm6+8WjFgUspaq6R3d/IslZVXW/m97f3R8coSxgAa0m2L0xyS8kuSDJDfMpB2BcVfU7mY1OeGiS1yb58ST/b9SigGX23CSnJnnlTu7rmO8AGKwm2G3r7nPmVgnAYnhgdx9XVR/t7l+uqlcmedvYRQHLqbtPHeY4+O/d/f6x6wEW12qC3Qur6rVJNif5xo6d3e0/PMB68vVh+bWq+ldJvpDke0asB1hy3X1DVf1KkgeMXQuwuFYT7H4myT0yO79ux1DMjl+ygfXlXVV1UJJXJPlgZn3uteOWBJC/qKonJnlbd/fYxQCLZzXB7t7dfa+5VQKwALr7JcPqH1fVu5LsZ2ZMYAE8N7Pzf6+vqh0jp7q7DxyxJmCBrOYC5X9XVcfOrRKABVBVd6yqX6qq13T3N5IcWlU/OnZdwNJ7R5Kfz+yH9gOGm1AHfNNqgt0JST5cVRdX1Uer6oKq+ui8CgMYyeszO494x7ksn03y0vHKAUgy602HJ/mNqvqnqnprVT177KKAxbGaoZiPnlsVAIvj7t39E1X1U0nS3V+vHRezAxhJd7+3qv4qyfdndjmWpyW5Z5JfH7UwYGHsdrDr7kvnWQjAgriuqu6Q2aQpqaq7Z8VMwABjqKrNSfZP8rdJ/jrJ93f3VeNWBSyS1QzFBFjXhiNzv5Pkz5J8V1W9MbNLvJw2amEAyUeTXJfZUbrjktxz+BEKIMnqhmICrGvd3cM5K49M8oNJKsmzu/vz41YGLLvu/s9JUlV3yuwSVDvOubv9mHUBi2Ouwa6q9ktyXmZNZ+8kb+3uF1bVwUnenOSoJJ9O8qTu/tI8awHYTX+X5G7d/SdjFwKwQ1U9I8m/TXJ8kkuTvC6zIZkASeZ/xO4bSR7W3V+tqn2SvK+q/jTJjyXZ3N0vr6rTk5ye5BfnXAvA7nhokv9YVZcmuTazo3bd3ceNWxaw5O6Q5FVJzu/u7WMXAyyeuQa77u4kXx029xluneTEJA8Z9m9Kcm4EO2AxPGbsAgBuqrtfMXYNwGKb+zl2VbVXkvOT/Oskv9Xdf19Vh3X3FUnS3VdU1aHzrgNgd5gBGACYornPitnd13f3fZJ8Z5L7V9U9d/e5VXVqVW2pqi3btm2bX5EAq6A3AYtKf4LltWaXO+juqzMbcvnoJFdW1RFJMix3eh2W7j6zuzd298YNGzasVakAN0tvAhaV/gTLa67Brqo2VNVBw/odkvxwkk8kOSfJycPDTk5y9jzrAAAAWM/mfY7dEUk2DefZ3S7JW7r7XVX1t0neUlWnJLksyUlzrgMAAGDdmvesmB9Nct+d7P9CkofP828DAAAsizU7xw4AAID5EOwAAAAmTrADAACYOMEOAABg4gQ7AACAiRPsAAAAJk6wAwAAmDjBDgAAYOIEOwAAgIkT7AAAACZOsAMAAJg4wQ4AAGDiBDsAAICJE+wAAAAmTrADAACYOMEOAABg4gQ7AACAiRPsAAAAJk6wAwAAmDjBDgAAYOIEOwAAgIkT7AAAACZOsAMAAJg4wQ4AAGDiBDsAAICJE+wAAAAmTrADAACYOMEOAABg4gQ7AACAiRPsAAAAJk6wAwAAmDjBDgAAYOIEOwAAgImba7Crqu+qqr+sqouq6sKqevaw/+Cqek9VXTIs7zLPOgAAANazeR+x257kv3T3v0nyg0meXlXHJjk9yebuPjrJ5mEbAACAW2Guwa67r+juDw7r1yS5KMldk5yYZNPwsE1JHj/POgAAANazNTvHrqqOSnLfJH+f5LDuviKZhb8kh65VHQAAAOvNmgS7qrpTkj9O8pzu/soqnndqVW2pqi3btm2bX4EAq6A3AYtKf4LlNfdgV1X7ZBbq3tjdbxt2X1lVRwz3H5Hkqp09t7vP7O6N3b1xw4YN8y4VYLfoTcCi0p9gec17VsxK8ntJLuruV62465wkJw/rJyc5e551AAAArGd7z/n1H5TkKUkuqKoPD/v+a5KXJ3lLVZ2S5LIkJ825DgAAgHVrrsGuu9+XpHZx98Pn+bcBAACWxZrNigkAAMB8CHYAAAATJ9gBAABMnGAHAAAwcYIdAADAxAl2AAAAEyfYAQAATJxgBwAAMHGCHQAAwMQJdgAAABMn2AEAAEycYAcAADBxgh0AAMDECXYAAAATJ9gBAABMnGAHAAAwcYIdAADAxAl2AAAAEyfYAQAATJxgBwAAMHGCHQAAwMQJdgAAABMn2AEAAEycYAcAADBxgh0AAMDECXYAAAATJ9gBAABMnGAHAAAwcYIdAADAxAl2AAAAEyfYAQAATJxgBwAAMHGCHQAAwMTNNdhV1euq6qqq+tiKfQdX1Xuq6pJheZd51gAAALDezfuI3RuSPPom+05Psrm7j06yedgGAADgVpprsOvu85J88Sa7T0yyaVjflOTx86wBAABgvRvjHLvDuvuKJBmWh45QAwAAwLqx0JOnVNWpVbWlqrZs27Zt7HIAkuhNwOLSn2B5jRHsrqyqI5JkWF61qwd295ndvbG7N27YsGHNCgS4OXoTsKj0J1heYwS7c5KcPKyfnOTsEWoAAABYN+Z9uYM3JfnbJMdU1Wer6pQkL0/yiKq6JMkjhm0AAABupb3n+eLd/VO7uOvh8/y7AAAAy2ShJ08BAADglgl2AAAAEyfYAQAATJxgBwAAMHGCHQAAwMQJdgAAABMn2AEAAEycYAcAADBxgh0AAMDECXYAAAATJ9gBAABMnGAHAAAwcYIdAADAxAl2AAAAEyfYAQAATJxgBwAAMHGCHQAAwMQJdgAAABMn2AEAAEycYAcAADBxgh0AAMDECXYAAAATJ9gBAABMnGAHAAAwcYIdAADAxAl2AAAAEyfYAQAATJxgBwAAMHGCHQAAwMQJdgAAABMn2AEAAEycYAcAADBxgh0AAMDEjRbsqurRVXVxVf1jVZ0+Vh0AAABTN0qwq6q9kvxWksckOTbJT1XVsWPUAgAAMHVjHbG7f5J/7O5Pdvd1Sf5PkhNHqgUAAGDSxgp2d03ymRXbnx32AQAAsErV3Wv/R6tOSvKo7v4Pw/ZTkty/u595k8edmuTUYfOYJBevaaHjOyTJ58cugjWxrJ/1d3f3hrGLWC29KcnyfmeX0TJ+1pPsTYn+lOX8vi6rZf2sd9mfxgp2D0jyou5+1LD9/CTp7peteTELrKq2dPfGsetg/nzWTI3v7PLwWTMlvq/Lw2f97cYaivmBJEdX1fdU1b5JfjLJOSPVAgAAMGl7j/FHu3t7VT0jyZ8n2SvJ67r7wjFqAQAAmLpRgl2SdPe7k7x7rL8/EWeOXQBrxmfN1PjOLg+fNVPi+7o8fNY3Mco5dgAAAOw5Y51jBwAAwB4i2M1RVT2rqi6qqjfO6fVfVFXPm8drM66qekhVvWvsOli/9CduDb2JedObuLX0pxHPsVsS/ynJY7r7U2MXAnAT+hOwiPQmuJUcsZuTqvqdJHdLck5V/beqel1VfaCqPlRVJw6PeWpVvaOq3llVn6qqZ1TVc4fH/F1VHTw87ueG536kqv64qu64k79396r6s6o6v6r+uqrusbbvmJuqqqOq6hNV9dqq+lhVvbGqfriq3l9Vl1TV/Yfb3wyf+d9U1TE7eZ39d/b9gVtLf1puehOLSm9Cf7qNutttTrckn05ySJL/meTfD/sOSvIPSfZP8tQk/5jkgCQbknw5ydOGx/1qkucM69+x4jVfmuSZw/qLkjxvWN+c5Ohh/QeSvHfs97/styRHJdme5F6Z/YhyfpLXJakkJyZ5R5IDk+w9PP6Hk/zxsP6QJO8a1nf6/Rn7/blN+6Y/Le9Nb3Jb5JvetNw3/em23QzFXBuPTPK4+taY7v2SHDms/2V3X5Pkmqr6cpJ3DvsvSHLcsH7PqnppZl/MO2V2/b9vqqo7JXlgkrOqasfu28/jjbBqn+ruC5Kkqi5Msrm7u6ouyKx53TnJpqo6Okkn2Wcnr7Gr789F8y6epaA/LSe9iUWnNy0v/elWEuzWRiV5YndffKOdVT+Q5Bsrdt2wYvuGfOvzeUOSx3f3R6rqqZn9IrHS7ZJc3d332bNlswfc0uf7ksz+gXpCVR2V5NydvMZOvz+wh+hPy0lvYtHpTctLf7qVnGO3Nv48yTNr+Emoqu67yucfkOSKqtonyZNvemd3fyXJp6rqpOH1q6rufRtrZm3cOcnlw/pTd/GY2/r9gZujP7EzehNj05vYFf1pFwS7tfGSzA4Tf7SqPjZsr8YvJfn7JO9J8oldPObJSU6pqo8kuTCzccgsvjOSvMdlgzsAAAKzSURBVKyq3p9kr1085rZ+f+Dm6E/sjN7E2PQmdkV/2oUaTioEAABgohyxAwAAmDjBDgAAYOIEOwAAgIkT7AAAACZOsAMAAJg4wY5JqKo3VNWPj10HwEp6E7Co9KflI9ixLlXV3mPXAHBTehOwqPSn6fMBssdV1S9ldtHPzyT5fJLzk7w9yW8l2ZDka0l+rrs/UVVvSPKVJBuTHJ7ktO5+a1VVkt9M8rAkn0pSK17/+CSvSnKn4fWf2t1XVNW5Sf4myYOSnJPklXN/s8Bk6E3AotKf2BMEO/aoqtqY5IlJ7pvZ9+uDmTWnM5M8rbsvqaofSPLbmTWeJDkiyQlJ7pFZU3lrkickOSbJvZIcluTjSV5XVftk1rRO7O5tVfUTSf5Hkp8dXuug7v6hub9RYFL0JmBR6U/sKYIde9oJSc7u7q8nSVW9M8l+SR6Y5KzZj0lJktuveM47uvuGJB+vqsOGfQ9O8qbuvj7J56rqvcP+Y5LcM8l7htfaK8kVK17rzXv+LQHrgN4ELCr9iT1CsGNPq53su12Sq7v7Prt4zjd28fzexetf2N0P2MVrXXvLJQJLSG8CFpX+xB5h8hT2tPcl+XdVtV9V3SnJYzMbF/6pqjopSWrm3rfwOucl+cmq2quqjkjy0GH/xUk2VNUDhtfap6q+by7vBFhP9CZgUelP7BGCHXtUd38gs7HeH0nytiRbknw5sxOCT6mqjyS5MMmJt/BSb09ySZILkrw6yV8Nr39dkh9P8r+G1/pwZkMVAHZJbwIWlf7EnlLdOztiC7deVd2pu79aVXfM7NejU7v7g2PXBSw3vQlYVPoTe4Jz7JiHM6vq2MxO/N2kMQELQm8CFpX+xG3miB0AAMDEOccOAABg4gQ7AACAiRPsAAAAJk6wAwAAmDjBDgAAYOIEOwAAgIn7/0qes4rMyTU3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(15, 6), sharey=True)\n",
    "sns.barplot(x='gender',y='math_score',data=df, ax=axs[0])\n",
    "sns.barplot(x='gender',y='reading_score',data=df, ax=axs[1])\n",
    "sns.barplot(x='gender',y='writing_score',data=df, ax=axs[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see there is a difference in scores when separating the students in female and male. In the math exame, the male students performed better, but in both reading and writing exams it was the female students that out-performed the other students."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Race/ethnicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Math Scores\n",
      " Multiple Comparison of Means - Tukey HSD, FWER=0.05  \n",
      "======================================================\n",
      " group1  group2 meandiff p-adj   lower   upper  reject\n",
      "------------------------------------------------------\n",
      "group A group B   1.8234 0.8597   -3.36  7.0068  False\n",
      "group A group C   2.8347 0.4966 -2.0028  7.6723  False\n",
      "group A group D   5.7334 0.0138  0.7824 10.6844   True\n",
      "group A group E  12.1922  0.001  6.7215 17.6629   True\n",
      "group B group C   1.0113    0.9 -2.6867  4.7094  False\n",
      "group B group D     3.91 0.0441  0.0647  7.7552   True\n",
      "group B group E  10.3688  0.001  5.8741 14.8635   True\n",
      "group C group D   2.8986 0.1287 -0.4659  6.2632  False\n",
      "group C group E   9.3575  0.001  5.2665 13.4485   True\n",
      "group D group E   6.4588  0.001  2.2343 10.6834   True\n",
      "------------------------------------------------------\n",
      "\n",
      "Reading Scores\n",
      " Multiple Comparison of Means - Tukey HSD, FWER=0.05  \n",
      "======================================================\n",
      " group1  group2 meandiff p-adj   lower   upper  reject\n",
      "------------------------------------------------------\n",
      "group A group B   2.6785 0.5875 -2.3998  7.7567  False\n",
      "group A group C   4.4293 0.0801 -0.3101  9.1687  False\n",
      "group A group D   5.3564 0.0219  0.5058 10.2069   True\n",
      "group A group E   8.3544  0.001  2.9947 13.7141   True\n",
      "group B group C   1.7508  0.656 -1.8722  5.3738  False\n",
      "group B group D   2.6779 0.2956 -1.0893  6.4451  False\n",
      "group B group E   5.6759 0.0041  1.2724 10.0794   True\n",
      "group C group D   0.9271    0.9 -2.3692  4.2234  False\n",
      "group C group E   3.9251 0.0583 -0.0829  7.9331  False\n",
      "group D group E    2.998 0.2768 -1.1408  7.1369  False\n",
      "------------------------------------------------------\n",
      "\n",
      "Writing Scores\n",
      " Multiple Comparison of Means - Tukey HSD, FWER=0.05  \n",
      "======================================================\n",
      " group1  group2 meandiff p-adj   lower   upper  reject\n",
      "------------------------------------------------------\n",
      "group A group B   2.9258 0.5444 -2.3436  8.1953  False\n",
      "group A group C   5.1534 0.0346  0.2356 10.0712   True\n",
      "group A group D   7.4709  0.001  2.4377  12.504   True\n",
      "group A group E    8.733  0.001  3.1715 14.2945   True\n",
      "group B group C   2.2276 0.4857 -1.5318   5.987  False\n",
      "group B group D    4.545 0.0133   0.636  8.4541   True\n",
      "group B group E   5.8071 0.0049  1.2379 10.3764   True\n",
      "group C group D   2.3175 0.3448 -1.1029  5.7378  False\n",
      "group C group E   3.5796 0.1294 -0.5793  7.7385  False\n",
      "group D group E   1.2621    0.9 -3.0326  5.5568  False\n",
      "------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "mc = MultiComparison(df['math_score'], df['race/ethnicity'])\n",
    "mc_results = mc.tukeyhsd()\n",
    "print()\n",
    "print(\"Math Scores\")\n",
    "print(mc_results)\n",
    "mr = MultiComparison(df['reading_score'], df['race/ethnicity'])\n",
    "mr_results = mr.tukeyhsd()\n",
    "print()\n",
    "print(\"Reading Scores\")\n",
    "print(mr_results)\n",
    "mw = MultiComparison(df['writing_score'], df['race/ethnicity'])\n",
    "mw_results = mw.tukeyhsd()\n",
    "print()\n",
    "print(\"Writing Scores\")\n",
    "print(mw_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see there are some rejections of the hull hypothesis. Mainly envolving group D and E, with the biggest meandiff between groups being between group A and group E."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2304bd2d198>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(15, 6), sharey=True)\n",
    "sns.barplot(x='race/ethnicity',y='math_score',data=df, ax=axs[0])\n",
    "sns.barplot(x='race/ethnicity',y='reading_score',data=df, ax=axs[1])\n",
    "sns.barplot(x='race/ethnicity',y='writing_score',data=df, ax=axs[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This difference can also be seen in the barplot with the difference between group A and E being significant across the board."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parental level of education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Math Scores\n",
      "            Multiple Comparison of Means - Tukey HSD, FWER=0.05             \n",
      "============================================================================\n",
      "      group1             group2      meandiff p-adj   lower    upper  reject\n",
      "----------------------------------------------------------------------------\n",
      "associate's degree bachelor's degree   1.5069    0.9  -3.3586  6.3725  False\n",
      "associate's degree       high school  -5.7451 0.0013  -9.9311 -1.5592   True\n",
      "associate's degree   master's degree   1.8629    0.9  -4.3926  8.1184  False\n",
      "associate's degree      some college  -0.7546    0.9  -4.7903  3.2812  False\n",
      "associate's degree  some high school  -4.3857 0.0418  -8.6759 -0.0954   True\n",
      " bachelor's degree       high school  -7.2521  0.001 -12.2284 -2.2758   True\n",
      " bachelor's degree   master's degree   0.3559    0.9  -6.4538  7.1657  False\n",
      " bachelor's degree      some college  -2.2615 0.7401  -7.1121  2.5891  False\n",
      " bachelor's degree  some high school  -5.8926 0.0119  -10.957 -0.8283   True\n",
      "       high school   master's degree    7.608 0.0084    1.266   13.95   True\n",
      "       high school      some college   4.9906 0.0086    0.822  9.1591   True\n",
      "       high school  some high school   1.3595    0.9   -3.056  5.7749  False\n",
      "   master's degree      some college  -2.6174 0.8175  -8.8613  3.6264  False\n",
      "   master's degree  some high school  -6.2486 0.0612 -12.6599  0.1628  False\n",
      "      some college  some high school  -3.6311 0.1481  -7.9044  0.6422  False\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Reading Scores\n",
      "            Multiple Comparison of Means - Tukey HSD, FWER=0.05             \n",
      "============================================================================\n",
      "      group1             group2      meandiff p-adj   lower    upper  reject\n",
      "----------------------------------------------------------------------------\n",
      "associate's degree bachelor's degree   2.0721 0.7746  -2.5816  6.7258  False\n",
      "associate's degree       high school  -6.2238  0.001 -10.2275 -2.2202   True\n",
      "associate's degree   master's degree    4.445 0.2771  -1.5382 10.4281  False\n",
      "associate's degree      some college  -1.4678 0.8816  -5.3277  2.3922  False\n",
      "associate's degree  some high school  -3.9894 0.0623  -8.0928   0.114  False\n",
      " bachelor's degree       high school  -8.2959  0.001 -13.0555 -3.5363   True\n",
      " bachelor's degree   master's degree   2.3729    0.9  -4.1403  8.8861  False\n",
      " bachelor's degree      some college  -3.5398 0.2486  -8.1792  1.0996  False\n",
      " bachelor's degree  some high school  -6.0615  0.005 -10.9053 -1.2176   True\n",
      "       high school   master's degree  10.6688  0.001   4.6029 16.7347   True\n",
      "       high school      some college   4.7561 0.0089   0.7691  8.7431   True\n",
      "       high school  some high school   2.2345 0.6366  -1.9887  6.4576  False\n",
      "   master's degree      some college  -5.9127 0.0541 -11.8847  0.0593  False\n",
      "   master's degree  some high school  -8.4343 0.0013 -14.5665 -2.3022   True\n",
      "      some college  some high school  -2.5216 0.4914  -6.6088  1.5655  False\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Writing Scores\n",
      "            Multiple Comparison of Means - Tukey HSD, FWER=0.05             \n",
      "============================================================================\n",
      "      group1             group2      meandiff p-adj   lower    upper  reject\n",
      "----------------------------------------------------------------------------\n",
      "associate's degree bachelor's degree    3.485 0.2988  -1.2997  8.2696  False\n",
      "associate's degree       high school  -7.4474  0.001 -11.5637 -3.3311   True\n",
      "associate's degree   master's degree   5.7816 0.0797  -0.3699  11.933  False\n",
      "associate's degree      some college  -1.0557    0.9  -5.0243  2.9129  False\n",
      "associate's degree  some high school  -5.0081 0.0095   -9.227 -0.7893   True\n",
      " bachelor's degree       high school -10.9324  0.001 -15.8259 -6.0389   True\n",
      " bachelor's degree   master's degree   2.2966    0.9  -4.3998  8.9931  False\n",
      " bachelor's degree      some college  -4.5406 0.0728  -9.3105  0.2293  False\n",
      " bachelor's degree  some high school  -8.4931  0.001 -13.4732  -3.513   True\n",
      "       high school   master's degree   13.229  0.001   6.9925 19.4655   True\n",
      "       high school      some college   6.3917  0.001   2.2925 10.4909   True\n",
      "       high school  some high school   2.4393 0.5827  -1.9027  6.7813  False\n",
      "   master's degree      some college  -6.8373 0.0189 -12.9772 -0.6973   True\n",
      "   master's degree  some high school -10.7897  0.001 -17.0944  -4.485   True\n",
      "      some college  some high school  -3.9524 0.0792  -8.1546  0.2497  False\n",
      "----------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "mc = MultiComparison(df['math_score'], df['parental_level_of_education'])\n",
    "mc_results = mc.tukeyhsd()\n",
    "print()\n",
    "print(\"Math Scores\")\n",
    "print(mc_results)\n",
    "mr = MultiComparison(df['reading_score'], df['parental_level_of_education'])\n",
    "mr_results = mr.tukeyhsd()\n",
    "print()\n",
    "print(\"Reading Scores\")\n",
    "print(mr_results)\n",
    "mw = MultiComparison(df['writing_score'], df['parental_level_of_education'])\n",
    "mw_results = mw.tukeyhsd()\n",
    "print()\n",
    "print(\"Writing Scores\")\n",
    "print(mw_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When comparing with the parental level of edutacion, most of the rejection of the null hypothesis comes from comparisons with \"high school\" and \"some high school\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, \"bachelor's degree\"),\n",
       " Text(0, 0, 'some college'),\n",
       " Text(0, 0, \"master's degree\"),\n",
       " Text(0, 0, \"associate's degree\"),\n",
       " Text(0, 0, 'high school'),\n",
       " Text(0, 0, 'some high school')]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(15, 6), sharey=True)\n",
    "a1 = sns.barplot(x='parental_level_of_education',y='math_score',data=df, ax=axs[0])\n",
    "a2 = sns.barplot(x='parental_level_of_education',y='reading_score',data=df, ax=axs[1])\n",
    "a3 = sns.barplot(x='parental_level_of_education',y='writing_score',data=df, ax=axs[2])\n",
    "a1.set_xticklabels(a1.get_xticklabels(), rotation=90)\n",
    "a2.set_xticklabels(a2.get_xticklabels(), rotation=90)\n",
    "a3.set_xticklabels(a3.get_xticklabels(), rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can also be seen in the bar plot where the bars for these two groups are clearly lower than most for all scores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lunch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Math Scores\n",
      "                 sum_sq     df           F        PR(>F)\n",
      "lunch      28278.037838    1.0  140.118842  2.413196e-30\n",
      "Residual  201411.041162  998.0         NaN           NaN\n",
      "\n",
      "Reading Scores\n",
      "                 sum_sq     df          F        PR(>F)\n",
      "lunch      11222.155126    1.0  55.518242  2.002797e-13\n",
      "Residual  201730.283874  998.0        NaN           NaN\n",
      "\n",
      "Writing Scores\n",
      "                 sum_sq     df          F        PR(>F)\n",
      "lunch      13933.413119    1.0  64.156643  3.186190e-15\n",
      "Residual  216743.670881  998.0        NaN           NaN\n"
     ]
    }
   ],
   "source": [
    "mod = ols(\"math_score ~ lunch\", data=df).fit()\n",
    "aov_table = sm.stats.anova_lm(mod, typ=2)\n",
    "print()\n",
    "print(\"Math Scores\")\n",
    "print(aov_table)\n",
    "\n",
    "mod = ols(\"reading_score ~ lunch\", data=df).fit()\n",
    "aov_table = sm.stats.anova_lm(mod, typ=2)\n",
    "print()\n",
    "print(\"Reading Scores\")\n",
    "print(aov_table)\n",
    "\n",
    "mod = ols(\"writing_score ~ lunch\", data=df).fit()\n",
    "aov_table = sm.stats.anova_lm(mod, typ=2)\n",
    "print()\n",
    "print(\"Writing Scores\")\n",
    "print(aov_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2304c185898>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(15, 6), sharey=True)\n",
    "sns.barplot(x='lunch',y='math_score',data=df, ax=axs[0])\n",
    "sns.barplot(x='lunch',y='reading_score',data=df, ax=axs[1])\n",
    "sns.barplot(x='lunch',y='writing_score',data=df, ax=axs[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it is possible to see, also when comparing lunch costs, the students with \"free/reduced\" cost performed worse than the others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test preparation course"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Math Scores\n",
      "                                sum_sq     df          F        PR(>F)\n",
      "test_preparation_course    7253.160232    1.0  32.542648  1.535913e-08\n",
      "Residual                 222435.918768  998.0        NaN           NaN\n",
      "\n",
      "Reading Scores\n",
      "                                sum_sq     df          F        PR(>F)\n",
      "test_preparation_course   12448.726414    1.0  61.963087  9.081783e-15\n",
      "Residual                 200503.712586  998.0        NaN           NaN\n",
      "\n",
      "Writing Scores\n",
      "                                sum_sq     df           F        PR(>F)\n",
      "test_preparation_course   22591.447181    1.0  108.350892  3.685292e-24\n",
      "Residual                 208085.636819  998.0         NaN           NaN\n"
     ]
    }
   ],
   "source": [
    "mod = ols(\"math_score ~ test_preparation_course\", data=df).fit()\n",
    "aov_table = sm.stats.anova_lm(mod, typ=2)\n",
    "print()\n",
    "print(\"Math Scores\")\n",
    "print(aov_table)\n",
    "\n",
    "mod = ols(\"reading_score ~ test_preparation_course\", data=df).fit()\n",
    "aov_table = sm.stats.anova_lm(mod, typ=2)\n",
    "print()\n",
    "print(\"Reading Scores\")\n",
    "print(aov_table)\n",
    "\n",
    "mod = ols(\"writing_score ~ test_preparation_course\", data=df).fit()\n",
    "aov_table = sm.stats.anova_lm(mod, typ=2)\n",
    "print()\n",
    "print(\"Writing Scores\")\n",
    "print(aov_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2304d3cfc18>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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McmazvFmSs5Nc2Uw3fUjVS9LUS9sFSBpLhwFHAadV1WVJHgec03JNkkbIIOfY/UNV3Z5kD+AF9L6x/sQk9z0MuLxv+UhgQVVtT+9qm0cOUIcktckLqUiaclV1flW9tKr+uVm+qqoOnXg+yb+2V52kUTBIsLu3mf4Z8ImqOh1YZ3U7JfmjZp9P963eh/uHMs2nd/U5SeoCj9hJGkW7t12ApHYNEuyuS/Ip4JXAN5KsO8n9PwwcAdzXt27LqroBoJluMUAdktSm77ddgCRJ0vIGOen2lcALgQ9W1S1JtgbeMfFkkk2r6jf9OyR5CXBTVS1K8uxBi0tyCHAIwLbbbjvo7pI0sCRvXcHqW4FFVbW4qt5sb5I0quxP0via9BG7qrqrqr5aVVc2yzdU1Vl9myxYwW67Ay9NcjXwJeA5ST4P3NgEQ5rpTSt5z+Oram5VzZ09e/ZkS5Wkh2Mu8AZgm+ZxCPBs4IQkR4C9SdJICtifpHE2yFDM1XnQeSdVdVRV/VFVzQFeBXy3ql4DnAHMazabB5y+BuuQpIfjkcBTq+ptVfU2ekFvNrAncFCbhUnSKnyk7QIktWtN3v9kkCvFfQA4tbnJ5rXAfmuwDkl6OLYF7ulb/j3wmKq6O8nvWqpJ0phL8jUe/G+tW4GFwKeq6qQpL0rSSJmyG1tW1bnAuc38r4C9p+q9JWkAXwB+mGRiJMH/Ab6YZBbw0/bKkjTmrqI3euCLzfL+wI3A44ETgANbqkvSiFiTwc5LgEvqvKp6X5Jv0jtHOMAbqmph8/QB7VUmacw9par27Fv+WpLzq2rPJJe1VpWkkTFQsEsyA9iyf7+quraZ9QicpOniQuB6ml6XZNu+XidJbZjd34uSbAts3jx3z8p3kzQuJh3skrwFeBe9w/4T96QrYGeAqvr1Gq9OkqbYcr3uXnpH7f7Q6ySpJW8Dvpfkf+j1pccCf90ME5/famWSRsIgR+wOA3Zozo+TpOnKXidp5FTVN5JsDzyBXrD7WVX9tnn6w+1VJmlUDBLsfkHv6kuSNJ3Z6ySNql2BOfT+/bZzEqrq39stSdKoWG2wS/LWZvYq4NwkXwf+cMnvqvrQkGqTpDbY6ySNnCSfA7YDFtMbJg69YeIGO0nA5I7YbdhMr20e6zQPGOzedZLUBSvqdZLUtrnAjlXlv70krdBqg11VvQcgyX5V9eX+55J4Y3FJ08pEz5OkEXMpsBVwQ9uFSBpNg5xjdxTw5Umsk6TOSfLhqjo8yddYwWiEqnppC2VJ0oTNgZ8m+TEPHCZub5IETO4cuxcBLwa2SfLRvqc2ApYNqzBJmmKfa6YfbLUKSVqxd7ddgKTRNpkjdtcDC4GXAov61t8O/M0wipKkqVZVi5rpeW3XIknLszdJWp3JnGN3EXBRki9U1e+noCZJmnJJLmEVF4SqKm9QLmnKJfleVe2R5HYe2KMCVFVt1FJpkkbMIOfYzUlyNLAjsN7Eyqp63BqvSpKm3kua6Zua6cTQzAOAu6a+HEmCqtqjmW64um0ljbe1Btj2s8An6J1Xtxe9+6Z8bpV7SFJHVNU1VXUNsHtVHVFVlzSPI4EXtF2fpPHW3Mduteskja9Bgt36VbUASPMPoHcDzxlOWZLUmllJ9phYSPJMYFaL9UgSwE79C0lmAru2VIukETTIUMzfJlkLuDLJm4HrgC2GU5YkteZg4MQkGzfLtwCvb7EeSWMsyVHA3wHrJ7ltYjVwD3B8a4VJGjmDBLvDgUcAhwLvozcc87XDKEqS2tJcHfPJSTaiN0Lh1rZrkjS+qupo4OgkR1fVUW3XI2l0DRLsit45dY8B1m7WnQB4pThJ00qSP6M37Gm9JABU1XtbLUrSWEryhKr6GfDlJE9d/vmquqCFsiSNoEGC3cnAO4BLgPuGU44ktSvJJ+mNTtgL+DTwCuDHrRYlaZy9FTgEOG4FzxVe70BSY5Bgt7SqzhhaJZI0Gp5ZVTsnubiq3pPkOOCrbRclaTxV1SHNNQ7+vqq+33Y9kkbXIMHuXUk+DSwAfjexsqr8B4+k6eTuZnpXkkcBvwIe22I9ksZcVd2X5IPAbm3XIml0DRLsXgc8gd75dRNDMQu/yZY0vZyZZBPgWOACen3u0+2WJEmcleTlwFerqtouRtLoGSTYPbmqnjS0SiRpBFTV+5rZ/0hyJrCeV8aUNALeSu/833uTTIycqqraqMWaJI2QQW5Q/sMkOw6tEkkaAUkekeQfkpxQVb8DtkjykrbrkjT2/hN4I70v2jdsHoY6SX8wSLDbA1ic5IokFye5JMnFwypMklryWXrnEU+cy/JL4B/bK0eSgF5v2gr4aJL/SfKVJIe1XZSk0THIUMwXDq0KSRod21XV/kleDVBVd2fiZnaS1JKq+m6S84Cn0bsdyxuAJwIfabUwSSNj0sGuqq4ZZiGSNCLuSbI+vYumkGQ7+q4ELEltSLIAmAX8APgv4GlVdVO7VUkaJYMMxZSkaa05MvdJ4FvAo5OcTO8WL0e0WpgkwcXAPfSO0u0MPLH5EkqSgMGGYkrStFZV1Zyz8nzgGUCAw6rq5nYrkzTuqupvAJJsQO8WVBPn3K3bZl2SRofBTpIe6IfA46rq620XIkkTkrwZeBawK3ANcCK9IZmSBBjsJGl5ewF/leQa4E56R+2qqnZutyxJY2594EPAoqpa1nYxkkaPwU6SHuhFbRcgScurqmPbrkHSaBtqsEuyHnA+vfHfM4GvVNW7kmwGnALMAa4GXllVvxlmLZI0GV4BWJIkddGwr4r5O+A5VfVkYBfghUmeARwJLKiq7eldce7IIdchSZIkSdPWUINd9dzRLK7dPArYB5jfrJ8P7DvMOiRJkiRpOhv6feySzEiyGLgJOLuqfgRsWVU3ADTTLYZdhyRJkiRNV0MPdlV1b1XtAvwR8PQkT5zsvkkOSbIwycKlS5cOr0hJGoC9SdKosj9J42vowW5CVd0CnAu8ELgxydYAzfSmlexzfFXNraq5s2fPnqpSJWmV7E2SRpX9SRpfQw12SWYn2aSZXx94LvAz4AxgXrPZPOD0YdYhSZIkSdPZsO9jtzUwP8kMeiHy1Ko6M8kPgFOTHAxcC+w35DokSZIkadoaarCrqouBp6xg/a+AvYf53pIkSZI0LqbsHDtJkiRJ0nAY7CRJkiSp4wx2kiRJktRxBjtJkiRJ6jiDnSRJkiR1nMFOkiRJkjrOYCdJkiRJHWewkyRJkqSOM9hJkiRJUscZ7CRJkiSp4wx2kiRJktRxBjtJkiRJ6jiDnSRJkiR1nMFOkiRJkjrOYCdJkiRJHWewkyRJkqSOM9hJkiRJUscZ7CRJkiSp4wx2kiRJktRxBjtJkiRJ6jiDnSRJkiR1nMFOkiRJkjrOYCdJkiRJHWewkyRJkqSOM9hJkiRJUscZ7CRJkiSp4wx2kiRJktRxBjtJkiRJ6jiDnSRJkiR1nMFOkiRJkjrOYCdJkiRJHWewkyRJkqSOG2qwS/LoJOckuTzJZUkOa9ZvluTsJFc2002HWYckSZIkTWfDPmK3DHhbVf0J8AzgTUl2BI4EFlTV9sCCZlmSJEmS9BAMNdhV1Q1VdUEzfztwObANsA8wv9lsPrDvMOuQJEmSpOlsys6xSzIHeArwI2DLqroBeuEP2GKq6pAkSZKk6WZKgl2SDYD/AA6vqtsG2O+QJAuTLFy6dOnwCpSkAdibJI0q+5M0voYe7JKsTS/UnVxVX21W35hk6+b5rYGbVrRvVR1fVXOrau7s2bOHXaokTYq9SdKosj9J42vYV8UM8Bng8qr6UN9TZwDzmvl5wOnDrEOSJEmSprOZQ3793YEDgUuSLG7W/R3wAeDUJAcD1wL7DbkOSZIkSZq2hhrsqup7QFby9N7DfG9JkiRJGhdTdlVMSZIkSdJwGOwkSZIkqeMMdpIkSZLUcQY7SZIkSeo4g50kSZIkdZzBTpIkSZI6zmAnSZIkSR1nsJMkSZKkjjPYSZIkSVLHGewkSZIkqeMMdpIkSZLUcQY7SZIkSeo4g50kSZIkdZzBTpIkSZI6zmAnSZIkSR1nsJMkSZKkjjPYSZIkSVLHGewkSZIkqeMMdpIkSZLUcQY7SZIkSeo4g50kSZIkdZzBTpIkSZI6zmAnSZIkSR1nsJMkSZKkjjPYSZIkSVLHGewkSZIkqeMMdpIkSZLUcQY7SZIkSeo4g50kSZIkdZzBTpIkSZI6zmAnSZIkSR1nsJMkSZKkjhtqsEtyYpKbklzat26zJGcnubKZbjrMGiRJkiRpuhv2EbuTgBcut+5IYEFVbQ8saJYlSZIkSQ/RUINdVZ0P/Hq51fsA85v5+cC+w6xBkiRJkqa7Ns6x27KqbgBoplu0UIMkSZIkTRsjffGUJIckWZhk4dKlS9suR5IAe5Ok0WV/ksZXG8HuxiRbAzTTm1a2YVUdX1Vzq2ru7Nmzp6xASVoVe5OkUWV/ksZXG8HuDGBeMz8POL2FGiRJkiRp2hj27Q6+CPwA2CHJL5McDHwAeF6SK4HnNcuSJEmSpIdo5jBfvKpevZKn9h7m+0qSJEnSOBnpi6dIkiRJklbPYCdJkiRJHWewkyRJkqSOM9hJkiRJUscZ7CRJkiSp4wx2kiRJktRxBjtJkiRJ6jiDnSRJkiR1nMFOkiRJkjrOYCdJkiRJHWewkyRJkqSOM9hJkiRJUscZ7CRJkiSp4wx2kiRJktRxBjtJkiRJ6jiDnSRJkiR1nMFOkiRJkjrOYCdJkiRJHWewkyRJkqSOM9hJkiRJUscZ7CRJkiSp4wx2kiRJktRxBjtJkiRJ6jiDnSRJkiR1nMFOkiRJkjrOYCdJkiRJHWewkyRJkqSOM9hJkiRJUscZ7CRJkiSp4wx2kiRJktRxBjtJkiRJ6jiDnSRJkiR1XGvBLskLk1yR5L+THNlWHZIkSZLUda0EuyQzgH8DXgTsCLw6yY5t1CJJkiRJXdfWEbunA/9dVVdV1T3Al4B9WqpFkiRJkjqtrWC3DfCLvuVfNuskSZIkSQNKVU39myb7AS+oqv/bLB8IPL2q3rLcdocAhzSLOwBXTGmh7dscuLntIjQlxvWzfkxVzW67iEHZm4Dx/ZsdR+P4WXeyN4H9ifH8ex1X4/pZr7Q/tRXsdgPeXVUvaJaPAqiqo6e8mBGWZGFVzW27Dg2fn7W6xr/Z8eFnrS7x73V8+Fk/WFtDMX8CbJ/ksUnWAV4FnNFSLZIkSZLUaTPbeNOqWpbkzcC3gRnAiVV1WRu1SJIkSVLXtRLsAKrqG8A32nr/jji+7QI0Zfys1TX+zY4PP2t1iX+v48PPejmtnGMnSZIkSVpz2jrHTpIkSZK0hhjspBGS5KQkr1jNNgcledSArzsnyaUPrzpJ48reJGkU2ZseyGAndc9BwEANSpKmwEHYmySNnoMYk95ksJtizTcAlyc5IcllSc5Ksn6SXZL8MMnFSU5Lsmmz/blJ/jnJj5P8PMmzmvUzkhyb5CfNPn/V7k82vSV5bfN7vijJ55I8JsmCZt2CJNs2252U5BNJzklyVZI/TXJi85mf1Pd6dyQ5LskFzf4PutFkkl2TnJdkUZJvJ9m6+VZqLnByksXN386Dtuvb/6IkPwDeNDW/KXWVvamb7E0aB/an7rE3taSqfEzhA5gDLAN2aZZPBV4DXAz8abPuvcCHm/lzgeOa+RcD32nmDwH+vplfF1gIPLbtn286PoCdgCuAzZvlzYCvAfOa5dcD/9nMnwR8CQiwD3Ab8CR6X6Is6vvcCzigmX8n8LG+/V8BrA38P2B2s35/ercFmfibmNvMr2q7/r+pY4FL2/5d+hjdh72pew97k49xedifuvWwN7X3aO12B2Puf6tqcTO/CNgO2KSqzmvWzQe+3Lf9V/u2ndPMPx/YOfePK94Y2B7432EVPcaeA3ylqm4GqKpfJ9kN+PPm+c8Bx/Rt/7WqqiSXADdW1SUASS6j9/ktBu4DTmm2/zz3f8YTdgCeCJydBHr3e7xhBbWtcLskG/PAv6nPAS8a/EfXmLE3dYu9SePE/tQd9qaWGOza8bu++XuBTSa5/b3c/5kFeEtVfXsN16YHC71vilal//mJz+s+HvhZ38fK/5tb/vUDXFZVu02itgdtl2STFbymtDr2pm6xN2mc2J+6w97UElmXMdEAAAbNSURBVM+xGw23Ar+ZGAMOHAict4rtAb4NvDHJ2gBJHp9k1hBrHGcLgFcmeSRAks3oHcZ/VfP8AcD3BnzNtegNHQD4ixXsfwUwu/mGiyRrJ9mpee52YMNVbVdVtwC3Jtmjr0ZpUPam0WZv0jizP40ue1NLPGI3OuYBn0zyCOAq4HWr2f7T9A5PX5DeseSlwL5DrXBMVdVlSf4JOC/JvcCFwKHAiUneQe93v7rPa3l3AjslWUTvf077L/ee9zRDRT7aDA+YCXwYuIzeePJPJrkb2I1eo1vRdq9raryL3v/MpIfC3jSi7E2S/WkU2Zvak+YEQUlTKMkdVbVB23VIUj97k6RRZG+aHIdiSpIkSVLHecROkiRJkjrOI3aSJEmS1HEGO0mSJEnqOIOdJEmSJHWcwU6SJEmSOs5g1wFJNkny1w9x38Ob+7t00vL1J/lGkk3arElSj73J3iSNKvuT/WkceVXMDkgyBzizqp74EPa9GphbVTc/zBpmVtWyh/MaK3nd0Ps7vG8lz1/NGqh/KiWZUVX3tl2HNGz2JnuTNKrsT/anceQRu274ALBdksVJjk3yjiQ/SXJxkvcAJJmV5OtJLkpyaZL9kxwKPAo4J8k5K3vxJHckOS7JBUkWJJndrD83yfuTnAcclmR2kv9o3vsnSXZvtnt3ks8l+W6SK5P8ZbN+g+b1LkhySZJ9mvVzklye5OPABcCjk3wiycIkl/X9TA+qP8nVSTZv5t/a/KyXJjl8udc+oXmts5Ksv4qf/Y+TfKf5vV2QZLv0HNu87iVJ9m+2fXaSM/v2/ViSg/rqemeS7wH7JTk0yU+bz+hLfZ/Ric3v7sKJ34fUYfYme5M0quxP9qfxU1U+RvwBzAEubeafDxwPhF4wPxPYE3g5cELfPhs306uBzVfz+gUc0My/E/hYM38u8PG+7b4A7NHMbwtc3sy/G7gIWB/YHPgFvaYyE9io2WZz4L+buucA9wHP6HvtzZrpjOZ9d15R/RPLwK7AJcAsYAPgMuApzWsvA3Zptj8VeM0qfvYfAS9r5tcDHtH8Ls9uatkSuBbYGng2vW//Jvb9GHBQX11H9D13PbBuM79JM33/RC3AJsDPgVlt/3358PFQH/Yme5MPH6P6sD/Zn8bx4RG77nl+87iQ3jc2TwC2p/cf6nOT/HOSZ1XVrQO85n3AKc3854E9+p47pW/+ucDHkiwGzgA2SrJh89zpVXV39Q77nwM8nV4jen+Si4HvANvQ+48d4Jqq+mHfa78yyQXNz7UTsONqat4DOK2q7qyqO4CvAs9qnvvfqlrczC+i17AepKl9m6o6DaCqfltVdzWv/cWqureqbgTOA562mnrggb+ri4GTk7yGXrOE3ud2ZPP7O5deM9x2Eq8rdYG9qcfeJI0e+1OP/Wmam9l2ARpYgKOr6lMPeiLZFXgxcHSSs6rqvQ/xPfpPvLyzb34tYLequnu5911+n4nlA4DZwK5V9fv0xnyvt/zrJnks8HbgaVX1myQn9W23MlnFc7/rm7+X3rdhg7zGytYv44HDl5evsf939Wf0vg18KfAPSXZqXvflVXXFSl5f6jJ7U7PbKp6zN0ntsD81u63iOfvTNOARu264HZj4dufbwOuTbACQZJskWyR5FHBXVX0e+CDw1BXsuzJrAa9o5v8C+N5KtjsLePPEQpJd+p7bJ8l6SR5J77D7T4CNgZuaxrQX8JiVvO5G9P7DvjXJlsCL+p5bWf3nA/smeUSSWcDLgP9a+Y/4YFV1G/DLJPs2P8+66V1F6nxg/yQz0hszvyfwY+AaYMdmu42BvVf0uknWAh5dVecAR9AbOrABvc/uLWm6eZKnDFKvNILsTQ9mb5JGg/3pwexP05xH7Dqgqn6V5PtJLgW+SW+89g+av/E7gNcAfwwcm+Q+4PfAG5vdjwe+meSGqtprJW9xJ7BTkkXArcD+K9nuUODfmuEBM+n9R/yG5rkfA1+nd3j8fVV1fZKTga8lWQgsBn62kp/voiQX0hvrfRXw/b6nV1h/VV3QfDv142bVp6vqwvSugjWIA4FPJXkvvd/bfsBpwG70xr4XvfHfSwCSnEpvqMCV9IY+rMgM4PNNAwvwL1V1S5L3AR8GLm4a1NXASwasVxoZ9iZ7kzSq7E/2p3Hk7Q5EkjuqaoOHsf+7gTuq6oNrripJ487eJGlU2Z80ihyKKUmSJEkd5xG7MZLkR8C6y60+sKouaaOeqZTk34Ddl1v9kar6bBv1SLqfvcneJI0q+5P9qUsMdpIkSZLUcQ7FlCRJkqSOM9hJkiRJUscZ7CRJkiSp4wx2kiRJktRxBjtJkiRJ6rj/D31mRNmxwoAsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(15, 6), sharey=True)\n",
    "sns.barplot(x='test_preparation_course',y='math_score',data=df, ax=axs[0])\n",
    "sns.barplot(x='test_preparation_course',y='reading_score',data=df, ax=axs[1])\n",
    "sns.barplot(x='test_preparation_course',y='writing_score',data=df, ax=axs[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it is possible to see, students whom completed the test preparation course also performed better in all the exams."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}