{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b01d6b3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import sqrt\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io as sio\n",
    "import os\n",
    "import random\n",
    "import time\n",
    "import sys\n",
    "from matplotlib import rcParams\n",
    "from scipy.linalg import eigh\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from scipy import stats\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "mpl.rcParams['figure.dpi'] = 300 \n",
    "plt.rc('text',usetex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aea49adc",
   "metadata": {},
   "source": [
    "Matrizes relevantes: \n",
    "\n",
    "$$\\mathbf{Y}=\\left[\\begin{array}{c}\n",
    "Y_{1}\\\\\n",
    "Y_{2}\\\\\n",
    "\\vdots\\\\\n",
    "Y_{n}\n",
    "\\end{array}\\right] \\mbox{(respostas)}, \\; \\mathbb{X}=\\left[\\begin{array}{ccccc}\n",
    "1 & X_{11} & X_{12} & \\cdots & X_{1,p-1}\\\\\n",
    "1 & X_{21} & X_{22} & \\cdots & X_{2,p-1}\\\\\n",
    "\\vdots & \\vdots & \\vdots & \\vdots & \\vdots\\\\\n",
    "1 & X_{n1} & X_{n2} & \\cdots & X_{n,p-1}\n",
    "\\end{array}\\right] \\mbox{(dados das covariaveis)},\\;\\mathbb{X}^{T}\\mathbb{X}.$$\n",
    "\n",
    "\n",
    "$$ \\boldsymbol{\\beta}=\\left[\\begin{array}{c}\n",
    "\\beta_{1}\\\\\n",
    "\\beta_{2}\\\\\n",
    "\\vdots\\\\\n",
    "\\beta_{n}\n",
    "\\end{array}\\right] \\mbox{(coeficientes)},\\;\\boldsymbol{\\varepsilon}=\\left[\\begin{array}{c}\n",
    "\\varepsilon_{1}\\\\\n",
    "\\varepsilon_{2}\\\\\n",
    "\\vdots\\\\\n",
    "\\varepsilon_{n}\n",
    "\\end{array}\\right]  \\mbox{(erros)}. $$\n",
    "\n",
    "\n",
    "Estimadores por mínimos quadrados\n",
    "\n",
    "$$ \\boldsymbol{\\hat{\\beta}} = (\\mathbb{X}^{T}\\mathbb{X})^{-1} \\mathbb{X}^{T} \\mathbf{Y}  $$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "184c2c1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sales = pd.read_csv('Advertising.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "45ff6200",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>sales</td>      <th>  R-squared:         </th> <td>   0.897</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.896</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   570.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 14 Nov 2023</td> <th>  Prob (F-statistic):</th> <td>1.58e-96</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>07:21:37</td>     <th>  Log-Likelihood:    </th> <td> -386.18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   780.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   196</td>      <th>  BIC:               </th> <td>   793.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</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>    2.9389</td> <td>    0.312</td> <td>    9.422</td> <td> 0.000</td> <td>    2.324</td> <td>    3.554</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th>        <td>    0.0458</td> <td>    0.001</td> <td>   32.809</td> <td> 0.000</td> <td>    0.043</td> <td>    0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>radio</th>     <td>    0.1885</td> <td>    0.009</td> <td>   21.893</td> <td> 0.000</td> <td>    0.172</td> <td>    0.206</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>newspaper</th> <td>   -0.0010</td> <td>    0.006</td> <td>   -0.177</td> <td> 0.860</td> <td>   -0.013</td> <td>    0.011</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>60.414</td> <th>  Durbin-Watson:     </th> <td>   2.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 151.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.327</td> <th>  Prob(JB):          </th> <td>1.44e-33</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.332</td> <th>  Cond. No.          </th> <td>    454.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<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:                  sales   R-squared:                       0.897\n",
       "Model:                            OLS   Adj. R-squared:                  0.896\n",
       "Method:                 Least Squares   F-statistic:                     570.3\n",
       "Date:                Tue, 14 Nov 2023   Prob (F-statistic):           1.58e-96\n",
       "Time:                        07:21:37   Log-Likelihood:                -386.18\n",
       "No. Observations:                 200   AIC:                             780.4\n",
       "Df Residuals:                     196   BIC:                             793.6\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      2.9389      0.312      9.422      0.000       2.324       3.554\n",
       "TV             0.0458      0.001     32.809      0.000       0.043       0.049\n",
       "radio          0.1885      0.009     21.893      0.000       0.172       0.206\n",
       "newspaper     -0.0010      0.006     -0.177      0.860      -0.013       0.011\n",
       "==============================================================================\n",
       "Omnibus:                       60.414   Durbin-Watson:                   2.084\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              151.241\n",
       "Skew:                          -1.327   Prob(JB):                     1.44e-33\n",
       "Kurtosis:                       6.332   Cond. No.                         454.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.ols(formula='sales ~ TV + radio + newspaper', data=df_sales)\n",
    "\n",
    "result = model.fit()\n",
    "\n",
    "result.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2a43f67d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
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       "      <td>3</td>\n",
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       "      <td>45.9</td>\n",
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       "      <td>9.3</td>\n",
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       "      <td>7.6</td>\n",
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       "      <th>196</th>\n",
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       "      <td>8.7</td>\n",
       "      <td>13.4</td>\n",
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       "  </tbody>\n",
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       "<p>200 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0     TV  radio  newspaper  sales\n",
       "0             1  230.1   37.8       69.2   22.1\n",
       "1             2   44.5   39.3       45.1   10.4\n",
       "2             3   17.2   45.9       69.3    9.3\n",
       "3             4  151.5   41.3       58.5   18.5\n",
       "4             5  180.8   10.8       58.4   12.9\n",
       "..          ...    ...    ...        ...    ...\n",
       "195         196   38.2    3.7       13.8    7.6\n",
       "196         197   94.2    4.9        8.1    9.7\n",
       "197         198  177.0    9.3        6.4   12.8\n",
       "198         199  283.6   42.0       66.2   25.5\n",
       "199         200  232.1    8.6        8.7   13.4\n",
       "\n",
       "[200 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_sales"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d252882",
   "metadata": {},
   "source": [
    "## Efeitos de novas covariáveis em $R^2$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8a4890fd",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.526341</td>\n",
       "      <td>-1.296232</td>\n",
       "      <td>2.070419</td>\n",
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       "      <td>1.874245</td>\n",
       "      <td>0.836519</td>\n",
       "      <td>0.834024</td>\n",
       "      <td>0.305672</td>\n",
       "      <td>0.400058</td>\n",
       "      <td>0.698448</td>\n",
       "      <td>-0.523424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12.8</td>\n",
       "      <td>-1.633934</td>\n",
       "      <td>1.040581</td>\n",
       "      <td>-0.366057</td>\n",
       "      <td>-0.829202</td>\n",
       "      <td>-0.208192</td>\n",
       "      <td>-2.645596</td>\n",
       "      <td>0.101832</td>\n",
       "      <td>-0.165956</td>\n",
       "      <td>0.447729</td>\n",
       "      <td>1.931378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>25.5</td>\n",
       "      <td>-0.327619</td>\n",
       "      <td>0.321214</td>\n",
       "      <td>-0.595611</td>\n",
       "      <td>-0.857044</td>\n",
       "      <td>1.417586</td>\n",
       "      <td>0.487991</td>\n",
       "      <td>0.901993</td>\n",
       "      <td>0.138315</td>\n",
       "      <td>1.857487</td>\n",
       "      <td>0.192366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>13.4</td>\n",
       "      <td>-0.063888</td>\n",
       "      <td>-0.579758</td>\n",
       "      <td>-0.485726</td>\n",
       "      <td>-0.197697</td>\n",
       "      <td>-0.737241</td>\n",
       "      <td>1.397878</td>\n",
       "      <td>-0.531259</td>\n",
       "      <td>0.452656</td>\n",
       "      <td>-0.495069</td>\n",
       "      <td>-1.602641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0     TV  radio  newspaper  sales        x1        x2        x3  \\\n",
       "0             1  230.1   37.8       69.2   22.1  1.047096 -0.241773  0.363243   \n",
       "1             2   44.5   39.3       45.1   10.4 -0.002336  0.903512  2.976487   \n",
       "2             3   17.2   45.9       69.3    9.3  1.861288 -0.851597 -0.177813   \n",
       "3             4  151.5   41.3       58.5   18.5  1.064049  1.458411  0.021797   \n",
       "4             5  180.8   10.8       58.4   12.9  0.399576  1.314729  1.187121   \n",
       "..          ...    ...    ...        ...    ...       ...       ...       ...   \n",
       "195         196   38.2    3.7       13.8    7.6  0.521404  1.217502  0.885936   \n",
       "196         197   94.2    4.9        8.1    9.7  0.767035  1.165815 -0.457121   \n",
       "197         198  177.0    9.3        6.4   12.8 -1.633934  1.040581 -0.366057   \n",
       "198         199  283.6   42.0       66.2   25.5 -0.327619  0.321214 -0.595611   \n",
       "199         200  232.1    8.6        8.7   13.4 -0.063888 -0.579758 -0.485726   \n",
       "\n",
       "           x4        x5        x6        x7        x8        x9       x10  \n",
       "0    0.451105 -1.441241  0.842933 -0.344773  0.328789 -1.109157 -1.571567  \n",
       "1   -1.030218 -0.288372 -0.719379  0.954315  0.036471 -1.580977 -0.208567  \n",
       "2    0.377831  1.028184 -0.738243 -0.849071 -0.175226 -0.222793  0.274071  \n",
       "3   -1.358062 -0.608630 -0.516412  0.432031 -1.228225  0.099671 -0.334514  \n",
       "4    0.492900  0.286864  0.240289 -0.430977 -0.490917  0.734273  0.825744  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "195  0.985284 -0.127615  0.859646  1.286044  0.526341 -1.296232  2.070419  \n",
       "196  1.874245  0.836519  0.834024  0.305672  0.400058  0.698448 -0.523424  \n",
       "197 -0.829202 -0.208192 -2.645596  0.101832 -0.165956  0.447729  1.931378  \n",
       "198 -0.857044  1.417586  0.487991  0.901993  0.138315  1.857487  0.192366  \n",
       "199 -0.197697 -0.737241  1.397878 -0.531259  0.452656 -0.495069 -1.602641  \n",
       "\n",
       "[200 rows x 15 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sales = pd.read_csv('Advertising.csv')\n",
    "\n",
    "num_covars = 10\n",
    "covars = ['radio']\n",
    "for i in range(1,num_covars+1):\n",
    "    covars.append(str('x')+str(i))  \n",
    "    \n",
    "covars\n",
    "\n",
    "for covar in covars[1:]:\n",
    "    df_sales[covar] = np.random.normal(size=len(df_sales))\n",
    "    \n",
    "df_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4ff4ec38",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1920x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eq = 'sales ~ radio'\n",
    "R2list = []\n",
    "#R2adjustlist = []\n",
    "\n",
    "for covar in covars[1:]:\n",
    "\n",
    "    \n",
    "    model = smf.ols(formula=eq, data=df_sales)\n",
    "\n",
    "    result = model.fit()\n",
    "    \n",
    "    R2list.append(result.rsquared)\n",
    "    #R2adjustlist.append(result.rsquared_adj)\n",
    "\n",
    "    #print(eq)\n",
    "    #print('p-value: ')\n",
    "    #print(result.pvalues)\n",
    "    #print('R2: ',result.rsquared)\n",
    "    #print(' ')\n",
    "    \n",
    "    eq = eq+' + '+covar\n",
    " \n",
    "fig = plt.figure()\n",
    "plt.plot(range(1,len(covars)),R2list,'.-')  \n",
    "#plt.plot(range(1,len(covars)),R2adjustlist,'.-')  \n",
    "\n",
    "for ax in fig.get_axes():\n",
    "\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    \n",
    "plt.xlabel(r'Número de covariáveis',fontsize=13)\n",
    "plt.ylabel(r'$R^2$',fontsize=13)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b8720920",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  sales   R-squared:                       0.366\n",
      "Model:                            OLS   Adj. R-squared:                  0.333\n",
      "Method:                 Least Squares   F-statistic:                     10.93\n",
      "Date:                Tue, 14 Nov 2023   Prob (F-statistic):           1.32e-14\n",
      "Time:                        07:25:20   Log-Likelihood:                -568.04\n",
      "No. Observations:                 200   AIC:                             1158.\n",
      "Df Residuals:                     189   BIC:                             1194.\n",
      "Df Model:                          10                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      9.3265      0.568     16.411      0.000       8.205      10.448\n",
      "radio          0.2037      0.021      9.870      0.000       0.163       0.244\n",
      "x1            -0.3106      0.310     -1.002      0.318      -0.922       0.301\n",
      "x2            -0.5436      0.271     -2.009      0.046      -1.077      -0.010\n",
      "x3            -0.2702      0.309     -0.874      0.383      -0.880       0.340\n",
      "x4             0.5558      0.326      1.704      0.090      -0.088       1.199\n",
      "x5            -0.2355      0.311     -0.758      0.449      -0.848       0.377\n",
      "x6             0.1983      0.310      0.640      0.523      -0.413       0.810\n",
      "x7             0.0845      0.328      0.258      0.797      -0.563       0.732\n",
      "x8            -0.3288      0.355     -0.926      0.356      -1.030       0.372\n",
      "x9             0.0894      0.309      0.290      0.772      -0.520       0.699\n",
      "==============================================================================\n",
      "Omnibus:                       22.530   Durbin-Watson:                   1.915\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               28.691\n",
      "Skew:                          -0.744   Prob(JB):                     5.89e-07\n",
      "Kurtosis:                       4.108   Cond. No.                         52.3\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dad53ce8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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