pratica2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>UF</th>\n",
       "      <th>ano_2015</th>\n",
       "      <th>ano_2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rondonia</td>\n",
       "      <td>77</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acre</td>\n",
       "      <td>79</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amazonas</td>\n",
       "      <td>83</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Roraima</td>\n",
       "      <td>81</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Para</td>\n",
       "      <td>73</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         UF  ano_2015  ano_2016\n",
       "0  Rondonia        77        85\n",
       "1      Acre        79        81\n",
       "2  Amazonas        83        85\n",
       "3   Roraima        81        82\n",
       "4      Para        73        76"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('alunos.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.lineplot(data=df, x='ano_2015', y='ano_2016')\n",
    "ax;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.ano_2015\n",
    "y = sm.add_constant(df.ano_2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "rr = sm.OLS(x, y).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               ano_2015   R-squared:                       0.801\n",
      "Model:                            OLS   Adj. R-squared:                  0.793\n",
      "Method:                 Least Squares   F-statistic:                     100.6\n",
      "Date:                Fri, 07 Jun 2024   Prob (F-statistic):           3.02e-10\n",
      "Time:                        15:51:31   Log-Likelihood:                -59.298\n",
      "No. Observations:                  27   AIC:                             122.6\n",
      "Df Residuals:                      25   BIC:                             125.2\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          5.3525      7.389      0.724      0.476      -9.865      20.570\n",
      "ano_2016       0.9226      0.092     10.030      0.000       0.733       1.112\n",
      "==============================================================================\n",
      "Omnibus:                        7.388   Durbin-Watson:                   1.424\n",
      "Prob(Omnibus):                  0.025   Jarque-Bera (JB):                8.315\n",
      "Skew:                          -0.449   Prob(JB):                       0.0156\n",
      "Kurtosis:                       5.566   Cond. No.                     1.36e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.36e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print(rr.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ano_2017'] = rr.predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>UF</th>\n",
       "      <th>ano_2015</th>\n",
       "      <th>ano_2016</th>\n",
       "      <th>ano_2017</th>\n",
       "      <th>resíduo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rondonia</td>\n",
       "      <td>77</td>\n",
       "      <td>85</td>\n",
       "      <td>83.775598</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acre</td>\n",
       "      <td>79</td>\n",
       "      <td>81</td>\n",
       "      <td>80.085101</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amazonas</td>\n",
       "      <td>83</td>\n",
       "      <td>85</td>\n",
       "      <td>83.775598</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Roraima</td>\n",
       "      <td>81</td>\n",
       "      <td>82</td>\n",
       "      <td>81.007725</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Para</td>\n",
       "      <td>73</td>\n",
       "      <td>76</td>\n",
       "      <td>75.471980</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         UF  ano_2015  ano_2016   ano_2017  resíduo\n",
       "0  Rondonia        77        85  83.775598     -1.0\n",
       "1      Acre        79        81  80.085101     -1.0\n",
       "2  Amazonas        83        85  83.775598     -1.0\n",
       "3   Roraima        81        82  81.007725     -1.0\n",
       "4      Para        73        76  75.471980     -1.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['resíduo'] = df['ano_2017'] - df['ano_2016']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>UF</th>\n",
       "      <th>ano_2015</th>\n",
       "      <th>ano_2016</th>\n",
       "      <th>ano_2017</th>\n",
       "      <th>resíduo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rondonia</td>\n",
       "      <td>77</td>\n",
       "      <td>85</td>\n",
       "      <td>83.775598</td>\n",
       "      <td>-1.224402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acre</td>\n",
       "      <td>79</td>\n",
       "      <td>81</td>\n",
       "      <td>80.085101</td>\n",
       "      <td>-0.914899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amazonas</td>\n",
       "      <td>83</td>\n",
       "      <td>85</td>\n",
       "      <td>83.775598</td>\n",
       "      <td>-1.224402</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Roraima</td>\n",
       "      <td>81</td>\n",
       "      <td>82</td>\n",
       "      <td>81.007725</td>\n",
       "      <td>-0.992275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Para</td>\n",
       "      <td>73</td>\n",
       "      <td>76</td>\n",
       "      <td>75.471980</td>\n",
       "      <td>-0.528020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         UF  ano_2015  ano_2016   ano_2017   resíduo\n",
       "0  Rondonia        77        85  83.775598 -1.224402\n",
       "1      Acre        79        81  80.085101 -0.914899\n",
       "2  Amazonas        83        85  83.775598 -1.224402\n",
       "3   Roraima        81        82  81.007725 -0.992275\n",
       "4      Para        73        76  75.471980 -0.528020"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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