linreg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"matpor_concat.csv\", delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()\n",
    "#print(list(df))\n",
    "df.replace(('yes', 'no'), (1, 0), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>G3</td>        <th>  R-squared:         </th> <td>   0.248</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.239</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   28.35</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 07 Nov 2018</td> <th>  Prob (F-statistic):</th> <td>4.45e-56</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:52:37</td>     <th>  Log-Likelihood:    </th> <td> -2743.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1044</td>      <th>  AIC:               </th> <td>   5513.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1031</td>      <th>  BIC:               </th> <td>   5577.</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>   11.4037</td> <td>    0.569</td> <td>   20.037</td> <td> 0.000</td> <td>   10.287</td> <td>   12.521</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mjob[T.health]</th>   <td>    1.9078</td> <td>    0.452</td> <td>    4.218</td> <td> 0.000</td> <td>    1.020</td> <td>    2.795</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mjob[T.other]</th>    <td>    0.3645</td> <td>    0.298</td> <td>    1.224</td> <td> 0.221</td> <td>   -0.220</td> <td>    0.949</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mjob[T.services]</th> <td>    1.1622</td> <td>    0.334</td> <td>    3.481</td> <td> 0.001</td> <td>    0.507</td> <td>    1.817</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mjob[T.teacher]</th>  <td>    0.9487</td> <td>    0.393</td> <td>    2.413</td> <td> 0.016</td> <td>    0.177</td> <td>    1.720</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>school[T.MS]</th>     <td>   -1.1193</td> <td>    0.256</td> <td>   -4.375</td> <td> 0.000</td> <td>   -1.621</td> <td>   -0.617</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>subject[T.P]</th>     <td>    1.7028</td> <td>    0.225</td> <td>    7.581</td> <td> 0.000</td> <td>    1.262</td> <td>    2.144</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>goout</th>            <td>   -0.2728</td> <td>    0.091</td> <td>   -2.983</td> <td> 0.003</td> <td>   -0.452</td> <td>   -0.093</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>romantic</th>         <td>   -0.6348</td> <td>    0.221</td> <td>   -2.872</td> <td> 0.004</td> <td>   -1.069</td> <td>   -0.201</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>studytime</th>        <td>    0.4951</td> <td>    0.129</td> <td>    3.847</td> <td> 0.000</td> <td>    0.243</td> <td>    0.748</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>schoolsup</th>        <td>   -1.2177</td> <td>    0.335</td> <td>   -3.630</td> <td> 0.000</td> <td>   -1.876</td> <td>   -0.560</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>failures</th>         <td>   -1.8706</td> <td>    0.165</td> <td>  -11.334</td> <td> 0.000</td> <td>   -2.194</td> <td>   -1.547</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>health</th>           <td>   -0.2138</td> <td>    0.074</td> <td>   -2.889</td> <td> 0.004</td> <td>   -0.359</td> <td>   -0.069</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>161.642</td> <th>  Durbin-Watson:     </th> <td>   1.981</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 330.224</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.902</td>  <th>  Prob(JB):          </th> <td>1.96e-72</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.082</td>  <th>  Cond. No.          </th> <td>    35.3</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:                     G3   R-squared:                       0.248\n",
       "Model:                            OLS   Adj. R-squared:                  0.239\n",
       "Method:                 Least Squares   F-statistic:                     28.35\n",
       "Date:                Wed, 07 Nov 2018   Prob (F-statistic):           4.45e-56\n",
       "Time:                        14:52:37   Log-Likelihood:                -2743.4\n",
       "No. Observations:                1044   AIC:                             5513.\n",
       "Df Residuals:                    1031   BIC:                             5577.\n",
       "Df Model:                          12                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Intercept           11.4037      0.569     20.037      0.000      10.287      12.521\n",
       "Mjob[T.health]       1.9078      0.452      4.218      0.000       1.020       2.795\n",
       "Mjob[T.other]        0.3645      0.298      1.224      0.221      -0.220       0.949\n",
       "Mjob[T.services]     1.1622      0.334      3.481      0.001       0.507       1.817\n",
       "Mjob[T.teacher]      0.9487      0.393      2.413      0.016       0.177       1.720\n",
       "school[T.MS]        -1.1193      0.256     -4.375      0.000      -1.621      -0.617\n",
       "subject[T.P]         1.7028      0.225      7.581      0.000       1.262       2.144\n",
       "goout               -0.2728      0.091     -2.983      0.003      -0.452      -0.093\n",
       "romantic            -0.6348      0.221     -2.872      0.004      -1.069      -0.201\n",
       "studytime            0.4951      0.129      3.847      0.000       0.243       0.748\n",
       "schoolsup           -1.2177      0.335     -3.630      0.000      -1.876      -0.560\n",
       "failures            -1.8706      0.165    -11.334      0.000      -2.194      -1.547\n",
       "health              -0.2138      0.074     -2.889      0.004      -0.359      -0.069\n",
       "==============================================================================\n",
       "Omnibus:                      161.642   Durbin-Watson:                   1.981\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              330.224\n",
       "Skew:                          -0.902   Prob(JB):                     1.96e-72\n",
       "Kurtosis:                       5.082   Cond. No.                         35.3\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.formula.api import ols\n",
    "#G3: -paid -absences -famsup -freetime -health (+Mjob +school +goout +romantic?)\n",
    "#result = ols(formula=\"G3 ~ studytime + schoolsup + paid + subject + failures + famsup + absences + health + freetime\", data=df).fit()\n",
    "result = ols(formula=\"G3 ~ Mjob + school + goout + romantic + studytime + schoolsup + subject + failures + health\", data=df).fit()\n",
    "#studytime: -paid -famsup -age -freetime\n",
    "#result = ols(formula=\"G3 ~ schoolsup + subject + paid + failures + famsup + higher + health + age + freetime\", data=df).fit()\n",
    "#schoolsup: -subject -failures\n",
    "#result = ols(formula=\"schoolsup ~ subject + failures + school\", data=df).fit()\n",
    "#freetime: -schoolsup -paid -traveltime -romantic\n",
    "#result = ols(formula=\"freetime ~ schoolsup + paid + traveltime + activities + goout + romantic\", data=df).fit()\n",
    "#activities:\n",
    "#result = ols(formula=\"activities ~ age\", data=df).fit()\n",
    "#goout:\n",
    "#result = ols(formula=\"goout ~ age\", data=df).fit()\n",
    "#romantic: -goout\n",
    "#result = ols(formula=\"romantic ~ age + goout\", data=df).fit()\n",
    "#Paid: -famrel\n",
    "#result = ols(formula=\"paid ~ subject + famrel + famsup + failures\", data=df).fit()\n",
    "#Failures:\n",
    "#result = ols(formula=\"failures ~ absences + subject\", data=df).fit()\n",
    "#Absences: -traveltime -health\n",
    "#result = ols(formula=\"absences ~ subject + traveltime + health\", data=df).fit()\n",
    "#traveltime\n",
    "#result = ols(formula=\"traveltime ~ school\", data=df).fit()\n",
    "#School\n",
    "#result = ols(formula=\"school ~ higher\", data=df).fit() #Can't test\n",
    "#Famrel: -Mjob -Fjob\n",
    "#result = ols(formula=\"famrel ~ Mjob + Fjob\", data=df).fit()\n",
    "#Famsup: -subject -famrel\n",
    "#result = ols(formula=\"famsup ~ subject + famrel + higher\", data=df).fit()\n",
    "#Higher:\n",
    "#result = ols(formula=\"higher ~ Medu + Fedu\", data=df).fit()\n",
    "#Mjob\n",
    "#result = ols(formula=\"Mjob ~ Medu\", data=df).fit() #Can't test\n",
    "#Fjob\n",
    "#result = ols(formula=\"Fjob ~ Fedu\", data=df).fit() #Can't test\n",
    "\n",
    "result.summary()\n",
    "\n",
    "#import statsmodels.api as sm\n",
    "#from statsmodels.graphics.regressionplots import abline_plot\n",
    "\n",
    "# scatter-plot data\n",
    "#ax = df.plot(x='G3', y='health', kind='scatter')"
   ]
  }
 ],
 "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": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}