jburroni/gist:ea5edf39faae41953a04

## gistfile1.txt
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set(style='whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('elecciones_scatter.csv', sep='\\t', decimal=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Barrio</th>\n",
       "      <th>Valor m²</th>\n",
       "      <th>Porcentaje</th>\n",
       "      <th>Formula</th>\n",
       "      <th>Cantidad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Puerto Madero</td>\n",
       "      <td>5419</td>\n",
       "      <td>0,0893</td>\n",
       "      <td>Recalde - Santoro (FPV)</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Recoleta</td>\n",
       "      <td>2675</td>\n",
       "      <td>0,1249</td>\n",
       "      <td>Recalde - Santoro (FPV)</td>\n",
       "      <td>11774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belgrano</td>\n",
       "      <td>2449</td>\n",
       "      <td>0,1258</td>\n",
       "      <td>Recalde - Santoro (FPV)</td>\n",
       "      <td>11062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Palermo</td>\n",
       "      <td>2654</td>\n",
       "      <td>0,1566</td>\n",
       "      <td>Recalde - Santoro (FPV)</td>\n",
       "      <td>23312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Retiro</td>\n",
       "      <td>2944</td>\n",
       "      <td>0,1577</td>\n",
       "      <td>Recalde - Santoro (FPV)</td>\n",
       "      <td>4023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Barrio   Valor m² Porcentaje                  Formula  Cantidad\n",
       "0  Puerto Madero      5419     0,0893  Recalde - Santoro (FPV)       243\n",
       "1       Recoleta      2675     0,1249  Recalde - Santoro (FPV)     11774\n",
       "2       Belgrano      2449     0,1258  Recalde - Santoro (FPV)     11062\n",
       "3        Palermo      2654     0,1566  Recalde - Santoro (FPV)     23312\n",
       "4         Retiro      2944     0,1577  Recalde - Santoro (FPV)      4023"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Recalde - Santoro (FPV)', 'Lousteau - Sanchez (ECO)',\n",
       "       'Larreta - Santilli ( PRO)'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Formula.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pro = df[df['Formula'] == 'Larreta - Santilli ( PRO)'][['Valor m²', 'Porcentaje']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Valor m²      37\n",
       "Porcentaje    37\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pro.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "logit = lambda x: np.log(x)-np.log(1-x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pro['logit'] = pro.Porcentaje.apply(logit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10ad0c6d0>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10af28bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pro['Valor m²'], pro.logit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ols = sm.OLS(pro.logit, sm.add_constant(pro['Valor m²'])).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>logit</td>      <th>  R-squared:         </th> <td>   0.571</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.559</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   46.59</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 08 Jul 2015</td> <th>  Prob (F-statistic):</th> <td>6.42e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:32:45</td>     <th>  Log-Likelihood:    </th> <td>  10.388</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    37</td>      <th>  AIC:               </th> <td>  -16.78</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    35</td>      <th>  BIC:               </th> <td>  -13.55</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</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>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>     <td>   -0.8833</td> <td>    0.106</td> <td>   -8.346</td> <td> 0.000</td> <td>   -1.098    -0.668</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Valor m²</th> <td>    0.0003</td> <td> 4.77e-05</td> <td>    6.826</td> <td> 0.000</td> <td>    0.000     0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.288</td> <th>  Durbin-Watson:     </th> <td>   0.624</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.319</td> <th>  Jarque-Bera (JB):  </th> <td>   1.601</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.508</td> <th>  Prob(JB):          </th> <td>   0.449</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.077</td> <th>  Cond. No.          </th> <td>7.60e+03</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  logit   R-squared:                       0.571\n",
       "Model:                            OLS   Adj. R-squared:                  0.559\n",
       "Method:                 Least Squares   F-statistic:                     46.59\n",
       "Date:                Wed, 08 Jul 2015   Prob (F-statistic):           6.42e-08\n",
       "Time:                        19:32:45   Log-Likelihood:                 10.388\n",
       "No. Observations:                  37   AIC:                            -16.78\n",
       "Df Residuals:                      35   BIC:                            -13.55\n",
       "Df Model:                           1                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.8833      0.106     -8.346      0.000        -1.098    -0.668\n",
       "Valor m²      0.0003   4.77e-05      6.826      0.000         0.000     0.000\n",
       "==============================================================================\n",
       "Omnibus:                        2.288   Durbin-Watson:                   0.624\n",
       "Prob(Omnibus):                  0.319   Jarque-Bera (JB):                1.601\n",
       "Skew:                           0.508   Prob(JB):                        0.449\n",
       "Kurtosis:                       3.077   Cond. No.                     7.60e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] The condition number is large, 7.6e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10aec8e50>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "tz7iWEaNY7toGsNdwG7ge057+7mNNhxHROyNiN+JiPPWns9P0vJz07PHkJl3ZOa1ax3xHuDftbR4\n",
       "YfS+uAj4nxFxwdq59u8C/23Uys7p287ALwD3RsT9rJbS968FeQurn28dPMf1V2gcQ0R8KSJ+l9Uv\n",
       "A3wiM++fXdwNjRwHq58H3QD0IuKVa4+5LTO/MIuwI0xyTLXtBcRZ10Ff+1btX8nMD0TEW4HfYvVF\n",
       "792Z+WezCtpg5DhmmGsSG46B1ZPiG1h9YffptbfP35+ZH5tJ0tGajqlfBT4bEQPgS6z+pEDbTHI8\n",
       "te05fbqmfXE78BlWz1u/nZmf3GhF4LWdJUkq50U2JEkqZvlKklTM8pUkqZjlK0lSMctXkqRilq8k\n",
       "ScUsX0mSiv1/bFQi0K3n184AAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b08fd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ols.resid.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "logistic = lambda x: 1/(1+np.exp(0-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "space = np.linspace(pro['Valor m²'].min(), pro['Valor m²'].max())\n",
    "y = logistic(ols.predict(sm.add_constant(space)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10b75f7d0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "B0Byk8fkLDZbAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b62c490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(space, y)\n",
    "plt.scatter(pro['Valor m²'], pro.Porcentaje)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "oBcREelgCnoREZEO9v8D5cSUfYVuK7sAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b77e210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='Valor m²', y=\"Porcentaje\", data=pro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.35, 0.75)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "ERGRPPT/cNTGTI7MP7UAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b91d450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='Valor m²', y=\"Porcentaje\", data=pro)\n",
    "plt.ylim([0.35, 0.75])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 3000)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "KaUMcUmSUsoQlyQppQxxSZJS6v8HTBzoU3ICdNMAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b9d2190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(space, y)\n",
    "plt.scatter(pro['Valor m²'], pro.Porcentaje)\n",
    "plt.xlim([1000, 3000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eco = df[df['Formula'] == 'Lousteau - Sanchez (ECO)'][['Valor m²', 'Porcentaje']].copy()\n",
    "eco['logit'] = eco.Porcentaje.apply(logit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10cd6bfd0>]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ca64b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(eco['Valor m²'], eco.logit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ols2 = sm.OLS(eco.logit, sm.add_constant(eco['Valor m²'])).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>logit</td>      <th>  R-squared:         </th> <td>   0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.3520</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 08 Jul 2015</td> <th>  Prob (F-statistic):</th>  <td> 0.557</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:45:36</td>     <th>  Log-Likelihood:    </th> <td>  10.932</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    37</td>      <th>  AIC:               </th> <td>  -17.86</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    35</td>      <th>  BIC:               </th> <td>  -14.64</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</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>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>     <td>   -1.0457</td> <td>    0.104</td> <td>  -10.027</td> <td> 0.000</td> <td>   -1.257    -0.834</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Valor m²</th> <td> -2.79e-05</td> <td>  4.7e-05</td> <td>   -0.593</td> <td> 0.557</td> <td>   -0.000  6.76e-05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 4.383</td> <th>  Durbin-Watson:     </th> <td>   0.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.112</td> <th>  Jarque-Bera (JB):  </th> <td>   4.098</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.794</td> <th>  Prob(JB):          </th> <td>   0.129</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.631</td> <th>  Cond. No.          </th> <td>7.60e+03</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  logit   R-squared:                       0.010\n",
       "Model:                            OLS   Adj. R-squared:                 -0.018\n",
       "Method:                 Least Squares   F-statistic:                    0.3520\n",
       "Date:                Wed, 08 Jul 2015   Prob (F-statistic):              0.557\n",
       "Time:                        19:45:36   Log-Likelihood:                 10.932\n",
       "No. Observations:                  37   AIC:                            -17.86\n",
       "Df Residuals:                      35   BIC:                            -14.64\n",
       "Df Model:                           1                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "const         -1.0457      0.104    -10.027      0.000        -1.257    -0.834\n",
       "Valor m²   -2.79e-05    4.7e-05     -0.593      0.557        -0.000  6.76e-05\n",
       "==============================================================================\n",
       "Omnibus:                        4.383   Durbin-Watson:                   0.037\n",
       "Prob(Omnibus):                  0.112   Jarque-Bera (JB):                4.098\n",
       "Skew:                          -0.794   Prob(JB):                        0.129\n",
       "Kurtosis:                       2.631   Cond. No.                     7.60e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] The condition number is large, 7.6e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## R-squared = 0.01!!\n",
    "practicamente independientes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "space2 = np.linspace(eco['Valor m²'].min(), eco['Valor m²'].max())\n",
    "y2 = logistic(ols2.predict(sm.add_constant(space2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10b9976d0>"
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     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "LklSThnikiTl1P8H5nuiFeeIkBkAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cab22d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(space2, y2)\n",
    "plt.scatter(eco['Valor m²'], eco.Porcentaje)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fpv = df[df['Formula'] == 'Recalde - Santoro (FPV)'][['Valor m²', 'Porcentaje']].copy()\n",
    "fpv['logit'] = fpv.Porcentaje.apply(logit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10b5e76d0>]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b799310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fpv['Valor m²'], fpv.logit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ols3 = sm.OLS(fpv.logit, sm.add_constant(fpv['Valor m²'])).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>logit</td>      <th>  R-squared:         </th> <td>   0.613</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.602</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   55.42</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 08 Jul 2015</td> <th>  Prob (F-statistic):</th> <td>1.03e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:48:02</td>     <th>  Log-Likelihood:    </th> <td>  5.4135</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    37</td>      <th>  AIC:               </th> <td>  -6.827</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    35</td>      <th>  BIC:               </th> <td>  -3.605</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</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>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>     <td>   -0.4040</td> <td>    0.121</td> <td>   -3.337</td> <td> 0.002</td> <td>   -0.650    -0.158</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Valor m²</th> <td>   -0.0004</td> <td> 5.46e-05</td> <td>   -7.444</td> <td> 0.000</td> <td>   -0.001    -0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.301</td> <th>  Durbin-Watson:     </th> <td>   0.735</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.522</td> <th>  Jarque-Bera (JB):  </th> <td>   0.586</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.279</td> <th>  Prob(JB):          </th> <td>   0.746</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.262</td> <th>  Cond. No.          </th> <td>7.60e+03</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  logit   R-squared:                       0.613\n",
       "Model:                            OLS   Adj. R-squared:                  0.602\n",
       "Method:                 Least Squares   F-statistic:                     55.42\n",
       "Date:                Wed, 08 Jul 2015   Prob (F-statistic):           1.03e-08\n",
       "Time:                        19:48:02   Log-Likelihood:                 5.4135\n",
       "No. Observations:                  37   AIC:                            -6.827\n",
       "Df Residuals:                      35   BIC:                            -3.605\n",
       "Df Model:                           1                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.4040      0.121     -3.337      0.002        -0.650    -0.158\n",
       "Valor m²     -0.0004   5.46e-05     -7.444      0.000        -0.001    -0.000\n",
       "==============================================================================\n",
       "Omnibus:                        1.301   Durbin-Watson:                   0.735\n",
       "Prob(Omnibus):                  0.522   Jarque-Bera (JB):                0.586\n",
       "Skew:                          -0.279   Prob(JB):                        0.746\n",
       "Kurtosis:                       3.262   Cond. No.                     7.60e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] The condition number is large, 7.6e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "space3 = np.linspace(fpv['Valor m²'].min(), fpv['Valor m²'].max())\n",
    "y3 = logistic(ols3.predict(sm.add_constant(space3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10ca120d0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cb8b410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(space3, y3)\n",
    "plt.scatter(eco['Valor m²'], fpv.Porcentaje)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b915290>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "oP8HqFMKW509GfYAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b899250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='Valor m²', y=\"Porcentaje\", data=fpv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}