taylor_lin_fit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Microinteractions\n",
    "### Computational pipeline for fitting linear regression model for interaction coefficients inference.\n",
    "\n",
    "The pre-release version of this pipeline assumes the data to be in a very specific format.\n",
    "Please contact [alex@gavruskin.com](mailto:alex@gavruskin.com) if you wish to give it a try.\n",
    "\n",
    "#### Instructions for people working on this project\n",
    "- Clone this repository `git clone https://github.com/gavruskin/microinteractions.git`\n",
    "- `cd microinteractions`\n",
    "- Put the data files in this folder\n",
    "- Make sure that read-out columns are called `total_CFUs`, `DailyFecundity`, `Development`, `Survival`\n",
    "- Make sure that the column that enumerates combinations of bacteria is called `treat`\n",
    "- `python data_preprocess_total_CFUs.py`\n",
    "- `python data_preprocess_DailyFecundity.py`\n",
    "- `python data_preprocess_Development.py`\n",
    "- `python data_preprocess_Survival.py`\n",
    "- `jupyter notebook`\n",
    "- Run all cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load dependencies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.formula.api as smf\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "\n",
    "from IPython.display import set_matplotlib_formats\n",
    "set_matplotlib_formats(\"png\", \"pdf\", \"svg\")\n",
    "\n",
    "matplotlib.style.use('ggplot')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### total_CFUs:\n",
    "- Read the data and fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_total_CFUs = pd.read_csv(\"flygut_cfus_expts1345_totals_processed.csv\")\n",
    "\n",
    "lm_total_CFUs = smf.ols(formula=\"total_CFUs ~ a + a1 + a2 + a3 + a4 + a5 +\"\n",
    "                     \"b12 + b13 + b14 + b15 + b23 + b24 + b25 + b34 + b35 + b45 +\"\n",
    "                     \"c123 + c124 + c125 + c134 + c135 + c145 + c234 + c235 + c245 + c345 +\"\n",
    "                     \"d1234 + d1235 + d1245 + d1345 + d2345 + e12345\", data=data_total_CFUs).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Output summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>total_CFUs</td>    <th>  R-squared:         </th> <td>   0.229</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.213</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   14.38</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 03 May 2018</td> <th>  Prob (F-statistic):</th> <td>1.56e-64</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:11:34</td>     <th>  Log-Likelihood:    </th> <td> -21789.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1536</td>      <th>  AIC:               </th> <td>4.364e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1504</td>      <th>  BIC:               </th> <td>4.381e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    31</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>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>-3.671e+17</td> <td> 1.61e+18</td> <td>   -0.228</td> <td> 0.820</td> <td>-3.53e+18   2.8e+18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a</th>         <td> 3.671e+17</td> <td> 1.61e+18</td> <td>    0.228</td> <td> 0.820</td> <td> -2.8e+18  3.53e+18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a1</th>        <td> 2.314e+05</td> <td> 7.27e+04</td> <td>    3.182</td> <td> 0.001</td> <td> 8.87e+04  3.74e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a2</th>        <td> 4.755e+05</td> <td> 7.23e+04</td> <td>    6.579</td> <td> 0.000</td> <td> 3.34e+05  6.17e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a3</th>        <td> 1.027e+05</td> <td> 7.23e+04</td> <td>    1.421</td> <td> 0.156</td> <td>-3.91e+04  2.44e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a4</th>        <td> 3.077e+05</td> <td> 7.23e+04</td> <td>    4.258</td> <td> 0.000</td> <td> 1.66e+05   4.5e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a5</th>        <td> 2.792e+05</td> <td> 7.23e+04</td> <td>    3.863</td> <td> 0.000</td> <td> 1.37e+05  4.21e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b12</th>       <td>-2.922e+05</td> <td> 1.02e+05</td> <td>   -2.851</td> <td> 0.004</td> <td>-4.93e+05 -9.11e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b13</th>       <td> 2.765e+05</td> <td> 1.02e+05</td> <td>    2.698</td> <td> 0.007</td> <td> 7.55e+04  4.78e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b14</th>       <td>-5.695e+04</td> <td> 1.02e+05</td> <td>   -0.556</td> <td> 0.579</td> <td>-2.58e+05  1.44e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b15</th>       <td> 4.012e+04</td> <td> 1.02e+05</td> <td>    0.391</td> <td> 0.695</td> <td>-1.61e+05  2.41e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b23</th>       <td>   4.8e+04</td> <td> 1.02e+05</td> <td>    0.470</td> <td> 0.639</td> <td>-1.53e+05  2.48e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b24</th>       <td>-1.279e+05</td> <td> 1.02e+05</td> <td>   -1.251</td> <td> 0.211</td> <td>-3.28e+05  7.26e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b25</th>       <td>-8.324e+04</td> <td> 1.02e+05</td> <td>   -0.814</td> <td> 0.416</td> <td>-2.84e+05  1.17e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b34</th>       <td>-1.382e+05</td> <td> 1.02e+05</td> <td>   -1.352</td> <td> 0.176</td> <td>-3.39e+05  6.23e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b35</th>       <td>-1.378e+05</td> <td> 1.02e+05</td> <td>   -1.348</td> <td> 0.178</td> <td>-3.38e+05  6.27e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b45</th>       <td>-2.515e+05</td> <td> 1.02e+05</td> <td>   -2.461</td> <td> 0.014</td> <td>-4.52e+05  -5.1e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c123</th>      <td>-2.776e+05</td> <td> 1.45e+05</td> <td>   -1.918</td> <td> 0.055</td> <td>-5.62e+05  6268.849</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c124</th>      <td>-7.909e+04</td> <td> 1.45e+05</td> <td>   -0.546</td> <td> 0.585</td> <td>-3.63e+05  2.05e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c125</th>      <td>-4.199e+04</td> <td> 1.45e+05</td> <td>   -0.290</td> <td> 0.772</td> <td>-3.26e+05  2.42e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c134</th>      <td>-1.647e+05</td> <td> 1.45e+05</td> <td>   -1.138</td> <td> 0.255</td> <td>-4.49e+05  1.19e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c135</th>      <td>-2.518e+05</td> <td> 1.45e+05</td> <td>   -1.740</td> <td> 0.082</td> <td>-5.36e+05  3.21e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c145</th>      <td> 1.514e+05</td> <td> 1.45e+05</td> <td>    1.046</td> <td> 0.296</td> <td>-1.33e+05  4.35e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c234</th>      <td>-1.557e+05</td> <td> 1.45e+05</td> <td>   -1.077</td> <td> 0.281</td> <td>-4.39e+05  1.28e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c235</th>      <td> 7086.9542</td> <td> 1.45e+05</td> <td>    0.049</td> <td> 0.961</td> <td>-2.76e+05  2.91e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c245</th>      <td> 7.908e+04</td> <td> 1.45e+05</td> <td>    0.547</td> <td> 0.584</td> <td>-2.04e+05  3.63e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c345</th>      <td> 1.527e+05</td> <td> 1.45e+05</td> <td>    1.056</td> <td> 0.291</td> <td>-1.31e+05  4.36e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d1234</th>     <td> 5.254e+05</td> <td> 2.05e+05</td> <td>    2.568</td> <td> 0.010</td> <td> 1.24e+05  9.27e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d1235</th>     <td> 2.699e+05</td> <td> 2.05e+05</td> <td>    1.319</td> <td> 0.187</td> <td>-1.31e+05  6.71e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d1245</th>     <td> 1.083e+05</td> <td> 2.05e+05</td> <td>    0.530</td> <td> 0.596</td> <td>-2.93e+05   5.1e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d1345</th>     <td>-1.859e+04</td> <td> 2.05e+05</td> <td>   -0.091</td> <td> 0.928</td> <td> -4.2e+05  3.83e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d2345</th>     <td> 1.282e+05</td> <td> 2.04e+05</td> <td>    0.627</td> <td> 0.531</td> <td>-2.73e+05  5.29e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>e12345</th>    <td>-3.793e+05</td> <td> 2.89e+05</td> <td>   -1.311</td> <td> 0.190</td> <td>-9.47e+05  1.88e+05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>426.550</td> <th>  Durbin-Watson:     </th> <td>   1.867</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>1427.933</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.355</td>  <th>  Prob(JB):          </th> <td>8.48e-311</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.869</td>  <th>  Cond. No.          </th> <td>5.44e+14</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             total_CFUs   R-squared:                       0.229\n",
       "Model:                            OLS   Adj. R-squared:                  0.213\n",
       "Method:                 Least Squares   F-statistic:                     14.38\n",
       "Date:                Thu, 03 May 2018   Prob (F-statistic):           1.56e-64\n",
       "Time:                        11:11:34   Log-Likelihood:                -21789.\n",
       "No. Observations:                1536   AIC:                         4.364e+04\n",
       "Df Residuals:                    1504   BIC:                         4.381e+04\n",
       "Df Model:                          31                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept  -3.671e+17   1.61e+18     -0.228      0.820     -3.53e+18   2.8e+18\n",
       "a           3.671e+17   1.61e+18      0.228      0.820      -2.8e+18  3.53e+18\n",
       "a1          2.314e+05   7.27e+04      3.182      0.001      8.87e+04  3.74e+05\n",
       "a2          4.755e+05   7.23e+04      6.579      0.000      3.34e+05  6.17e+05\n",
       "a3          1.027e+05   7.23e+04      1.421      0.156     -3.91e+04  2.44e+05\n",
       "a4          3.077e+05   7.23e+04      4.258      0.000      1.66e+05   4.5e+05\n",
       "a5          2.792e+05   7.23e+04      3.863      0.000      1.37e+05  4.21e+05\n",
       "b12        -2.922e+05   1.02e+05     -2.851      0.004     -4.93e+05 -9.11e+04\n",
       "b13         2.765e+05   1.02e+05      2.698      0.007      7.55e+04  4.78e+05\n",
       "b14        -5.695e+04   1.02e+05     -0.556      0.579     -2.58e+05  1.44e+05\n",
       "b15         4.012e+04   1.02e+05      0.391      0.695     -1.61e+05  2.41e+05\n",
       "b23           4.8e+04   1.02e+05      0.470      0.639     -1.53e+05  2.48e+05\n",
       "b24        -1.279e+05   1.02e+05     -1.251      0.211     -3.28e+05  7.26e+04\n",
       "b25        -8.324e+04   1.02e+05     -0.814      0.416     -2.84e+05  1.17e+05\n",
       "b34        -1.382e+05   1.02e+05     -1.352      0.176     -3.39e+05  6.23e+04\n",
       "b35        -1.378e+05   1.02e+05     -1.348      0.178     -3.38e+05  6.27e+04\n",
       "b45        -2.515e+05   1.02e+05     -2.461      0.014     -4.52e+05  -5.1e+04\n",
       "c123       -2.776e+05   1.45e+05     -1.918      0.055     -5.62e+05  6268.849\n",
       "c124       -7.909e+04   1.45e+05     -0.546      0.585     -3.63e+05  2.05e+05\n",
       "c125       -4.199e+04   1.45e+05     -0.290      0.772     -3.26e+05  2.42e+05\n",
       "c134       -1.647e+05   1.45e+05     -1.138      0.255     -4.49e+05  1.19e+05\n",
       "c135       -2.518e+05   1.45e+05     -1.740      0.082     -5.36e+05  3.21e+04\n",
       "c145        1.514e+05   1.45e+05      1.046      0.296     -1.33e+05  4.35e+05\n",
       "c234       -1.557e+05   1.45e+05     -1.077      0.281     -4.39e+05  1.28e+05\n",
       "c235        7086.9542   1.45e+05      0.049      0.961     -2.76e+05  2.91e+05\n",
       "c245        7.908e+04   1.45e+05      0.547      0.584     -2.04e+05  3.63e+05\n",
       "c345        1.527e+05   1.45e+05      1.056      0.291     -1.31e+05  4.36e+05\n",
       "d1234       5.254e+05   2.05e+05      2.568      0.010      1.24e+05  9.27e+05\n",
       "d1235       2.699e+05   2.05e+05      1.319      0.187     -1.31e+05  6.71e+05\n",
       "d1245       1.083e+05   2.05e+05      0.530      0.596     -2.93e+05   5.1e+05\n",
       "d1345      -1.859e+04   2.05e+05     -0.091      0.928      -4.2e+05  3.83e+05\n",
       "d2345       1.282e+05   2.04e+05      0.627      0.531     -2.73e+05  5.29e+05\n",
       "e12345     -3.793e+05   2.89e+05     -1.311      0.190     -9.47e+05  1.88e+05\n",
       "==============================================================================\n",
       "Omnibus:                      426.550   Durbin-Watson:                   1.867\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1427.933\n",
       "Skew:                           1.355   Prob(JB):                    8.48e-311\n",
       "Kurtosis:                       6.869   Cond. No.                     5.44e+14\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 2.41e-26. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_total_CFUs.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Plot inferred coefficients with confidence intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0ffcd83828>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HjViOwTKMVI7tvRfDmWNn/k7qJcdIfzbqJYfP6Oj5jA5FjtH4Ga2X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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ffcd91c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_int_total_CFUs = pd.DataFrame(lm_total_CFUs.conf_int())\n",
    "conf_int_total_CFUs[2] = [lm_total_CFUs.params.a,\n",
    "                            lm_total_CFUs.params.a,\n",
    "                            lm_total_CFUs.params.a1,\n",
    "                            lm_total_CFUs.params.a2,\n",
    "                            lm_total_CFUs.params.a3,\n",
    "                            lm_total_CFUs.params.a4,\n",
    "                            lm_total_CFUs.params.a5,\n",
    "                            lm_total_CFUs.params.b12,\n",
    "                            lm_total_CFUs.params.b13,\n",
    "                            lm_total_CFUs.params.b14,\n",
    "                            lm_total_CFUs.params.b15,\n",
    "                            lm_total_CFUs.params.b23,\n",
    "                            lm_total_CFUs.params.b24,\n",
    "                            lm_total_CFUs.params.b25,\n",
    "                            lm_total_CFUs.params.b34,\n",
    "                            lm_total_CFUs.params.b35,\n",
    "                            lm_total_CFUs.params.b45,\n",
    "                            lm_total_CFUs.params.c123,\n",
    "                            lm_total_CFUs.params.c124,\n",
    "                            lm_total_CFUs.params.c125,\n",
    "                            lm_total_CFUs.params.c134,\n",
    "                            lm_total_CFUs.params.c135,\n",
    "                            lm_total_CFUs.params.c145,\n",
    "                            lm_total_CFUs.params.c234,\n",
    "                            lm_total_CFUs.params.c235,\n",
    "                            lm_total_CFUs.params.c245,\n",
    "                            lm_total_CFUs.params.c345,\n",
    "                            lm_total_CFUs.params.d1234,\n",
    "                            lm_total_CFUs.params.d1235,\n",
    "                            lm_total_CFUs.params.d1245,\n",
    "                            lm_total_CFUs.params.d1345,\n",
    "                            lm_total_CFUs.params.d2345,\n",
    "                            lm_total_CFUs.params.e12345]\n",
    "conf_int_total_CFUs.columns = [\"95% conf. int. bottom\", \"95% conf. int. top\", \"coef\"]\n",
    "# Set Intercept and a to 0, as otherwise the rest of the plot vanishes.\n",
    "conf_int_total_CFUs[\"coef\"].Intercept = 0\n",
    "conf_int_total_CFUs[\"95% conf. int. bottom\"].Intercept = 0\n",
    "conf_int_total_CFUs[\"95% conf. int. top\"].Intercept = 0\n",
    "conf_int_total_CFUs[\"coef\"].a = 0\n",
    "conf_int_total_CFUs[\"95% conf. int. bottom\"].a = 0\n",
    "conf_int_total_CFUs[\"95% conf. int. top\"].a = 0\n",
    "conf_int_total_CFUs.plot.bar(figsize=(20,10))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DailyFecundity:\n",
    "- Read the data and fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_DailyFecundity = pd.read_csv(\"DailyFecundityData_processed.csv\")\n",
    "\n",
    "lm_DailyFecundity = smf.ols(formula=\"DailyFecundity ~ a + a1 + a2 + a3 + a4 + a5 +\"\n",
    "                     \"b12 + b13 + b14 + b15 + b23 + b24 + b25 + b34 + b35 + b45 +\"\n",
    "                     \"c123 + c124 + c125 + c134 + c135 + c145 + c234 + c235 + c245 + c345 +\"\n",
    "                     \"d1234 + d1235 + d1245 + d1345 + d2345 + e12345\", data=data_DailyFecundity).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Output summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>DailyFecundity</td>  <th>  R-squared:         </th> <td>   0.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   3.622</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 03 May 2018</td> <th>  Prob (F-statistic):</th> <td>8.80e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:11:36</td>     <th>  Log-Likelihood:    </th> <td> -4709.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  2054</td>      <th>  AIC:               </th> <td>   9482.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  2022</td>      <th>  BIC:               </th> <td>   9662.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    31</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>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td> 8.971e+12</td> <td> 4.56e+12</td> <td>    1.969</td> <td> 0.049</td> <td> 3.55e+10  1.79e+13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a</th>         <td>-8.971e+12</td> <td> 4.56e+12</td> <td>   -1.969</td> <td> 0.049</td> <td>-1.79e+13 -3.55e+10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a1</th>        <td>   -0.7626</td> <td>    0.451</td> <td>   -1.692</td> <td> 0.091</td> <td>   -1.647     0.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a2</th>        <td>   -0.9675</td> <td>    0.474</td> <td>   -2.043</td> <td> 0.041</td> <td>   -1.896    -0.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a3</th>        <td>    0.6763</td> <td>    0.449</td> <td>    1.505</td> <td> 0.132</td> <td>   -0.205     1.557</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a4</th>        <td>    1.2339</td> <td>    0.449</td> <td>    2.746</td> <td> 0.006</td> <td>    0.353     2.115</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a5</th>        <td>    1.4565</td> <td>    0.449</td> <td>    3.242</td> <td> 0.001</td> <td>    0.575     2.338</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b12</th>       <td>    1.2609</td> <td>    0.636</td> <td>    1.981</td> <td> 0.048</td> <td>    0.013     2.509</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b13</th>       <td>    1.2252</td> <td>    0.619</td> <td>    1.981</td> <td> 0.048</td> <td>    0.012     2.438</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b14</th>       <td>    0.2458</td> <td>    0.619</td> <td>    0.397</td> <td> 0.691</td> <td>   -0.967     1.459</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b15</th>       <td>    0.5812</td> <td>    0.619</td> <td>    0.939</td> <td> 0.348</td> <td>   -0.632     1.794</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b23</th>       <td>    0.5467</td> <td>    0.635</td> <td>    0.860</td> <td> 0.390</td> <td>   -0.699     1.793</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b24</th>       <td>   -0.5779</td> <td>    0.635</td> <td>   -0.910</td> <td> 0.363</td> <td>   -1.824     0.668</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b25</th>       <td>    0.7384</td> <td>    0.635</td> <td>    1.162</td> <td> 0.245</td> <td>   -0.508     1.984</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b34</th>       <td>   -1.4007</td> <td>    0.617</td> <td>   -2.268</td> <td> 0.023</td> <td>   -2.612    -0.190</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b35</th>       <td>   -1.4379</td> <td>    0.617</td> <td>   -2.329</td> <td> 0.020</td> <td>   -2.649    -0.227</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b45</th>       <td>   -1.4129</td> <td>    0.617</td> <td>   -2.288</td> <td> 0.022</td> <td>   -2.624    -0.202</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c123</th>      <td>   -1.8029</td> <td>    0.874</td> <td>   -2.063</td> <td> 0.039</td> <td>   -3.517    -0.089</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c124</th>      <td>    0.5771</td> <td>    0.874</td> <td>    0.660</td> <td> 0.509</td> <td>   -1.137     2.291</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c125</th>      <td>   -1.7199</td> <td>    0.874</td> <td>   -1.968</td> <td> 0.049</td> <td>   -3.434    -0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c134</th>      <td>   -0.2180</td> <td>    0.861</td> <td>   -0.253</td> <td> 0.800</td> <td>   -1.907     1.471</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c135</th>      <td>   -1.5618</td> <td>    0.861</td> <td>   -1.814</td> <td> 0.070</td> <td>   -3.251     0.127</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c145</th>      <td>   -0.3455</td> <td>    0.861</td> <td>   -0.401</td> <td> 0.688</td> <td>   -2.034     1.343</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c234</th>      <td>    1.1538</td> <td>    0.873</td> <td>    1.321</td> <td> 0.187</td> <td>   -0.559     2.866</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c235</th>      <td>   -0.4012</td> <td>    0.873</td> <td>   -0.459</td> <td> 0.646</td> <td>   -2.114     1.311</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c245</th>      <td>    0.5115</td> <td>    0.873</td> <td>    0.586</td> <td> 0.558</td> <td>   -1.201     2.224</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c345</th>      <td>    1.6105</td> <td>    0.860</td> <td>    1.872</td> <td> 0.061</td> <td>   -0.077     3.298</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d1234</th>     <td>   -0.6647</td> <td>    1.217</td> <td>   -0.546</td> <td> 0.585</td> <td>   -3.052     1.722</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d1235</th>     <td>    2.6005</td> <td>    1.217</td> <td>    2.136</td> <td> 0.033</td> <td>    0.213     4.988</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d1245</th>     <td>    0.4978</td> <td>    1.217</td> <td>    0.409</td> <td> 0.683</td> <td>   -1.889     2.885</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d1345</th>     <td>    0.7939</td> <td>    1.208</td> <td>    0.657</td> <td> 0.511</td> <td>   -1.575     3.163</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d2345</th>     <td>   -1.0023</td> <td>    1.217</td> <td>   -0.824</td> <td> 0.410</td> <td>   -3.388     1.384</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>e12345</th>    <td>   -1.0212</td> <td>    1.708</td> <td>   -0.598</td> <td> 0.550</td> <td>   -4.371     2.328</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>92.409</td> <th>  Durbin-Watson:     </th> <td>   0.737</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  65.910</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.333</td> <th>  Prob(JB):          </th> <td>4.87e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.430</td> <th>  Cond. No.          </th> <td>2.62e+14</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:         DailyFecundity   R-squared:                       0.053\n",
       "Model:                            OLS   Adj. R-squared:                  0.038\n",
       "Method:                 Least Squares   F-statistic:                     3.622\n",
       "Date:                Thu, 03 May 2018   Prob (F-statistic):           8.80e-11\n",
       "Time:                        11:11:36   Log-Likelihood:                -4709.2\n",
       "No. Observations:                2054   AIC:                             9482.\n",
       "Df Residuals:                    2022   BIC:                             9662.\n",
       "Df Model:                          31                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept   8.971e+12   4.56e+12      1.969      0.049      3.55e+10  1.79e+13\n",
       "a          -8.971e+12   4.56e+12     -1.969      0.049     -1.79e+13 -3.55e+10\n",
       "a1            -0.7626      0.451     -1.692      0.091        -1.647     0.122\n",
       "a2            -0.9675      0.474     -2.043      0.041        -1.896    -0.039\n",
       "a3             0.6763      0.449      1.505      0.132        -0.205     1.557\n",
       "a4             1.2339      0.449      2.746      0.006         0.353     2.115\n",
       "a5             1.4565      0.449      3.242      0.001         0.575     2.338\n",
       "b12            1.2609      0.636      1.981      0.048         0.013     2.509\n",
       "b13            1.2252      0.619      1.981      0.048         0.012     2.438\n",
       "b14            0.2458      0.619      0.397      0.691        -0.967     1.459\n",
       "b15            0.5812      0.619      0.939      0.348        -0.632     1.794\n",
       "b23            0.5467      0.635      0.860      0.390        -0.699     1.793\n",
       "b24           -0.5779      0.635     -0.910      0.363        -1.824     0.668\n",
       "b25            0.7384      0.635      1.162      0.245        -0.508     1.984\n",
       "b34           -1.4007      0.617     -2.268      0.023        -2.612    -0.190\n",
       "b35           -1.4379      0.617     -2.329      0.020        -2.649    -0.227\n",
       "b45           -1.4129      0.617     -2.288      0.022        -2.624    -0.202\n",
       "c123          -1.8029      0.874     -2.063      0.039        -3.517    -0.089\n",
       "c124           0.5771      0.874      0.660      0.509        -1.137     2.291\n",
       "c125          -1.7199      0.874     -1.968      0.049        -3.434    -0.006\n",
       "c134          -0.2180      0.861     -0.253      0.800        -1.907     1.471\n",
       "c135          -1.5618      0.861     -1.814      0.070        -3.251     0.127\n",
       "c145          -0.3455      0.861     -0.401      0.688        -2.034     1.343\n",
       "c234           1.1538      0.873      1.321      0.187        -0.559     2.866\n",
       "c235          -0.4012      0.873     -0.459      0.646        -2.114     1.311\n",
       "c245           0.5115      0.873      0.586      0.558        -1.201     2.224\n",
       "c345           1.6105      0.860      1.872      0.061        -0.077     3.298\n",
       "d1234         -0.6647      1.217     -0.546      0.585        -3.052     1.722\n",
       "d1235          2.6005      1.217      2.136      0.033         0.213     4.988\n",
       "d1245          0.4978      1.217      0.409      0.683        -1.889     2.885\n",
       "d1345          0.7939      1.208      0.657      0.511        -1.575     3.163\n",
       "d2345         -1.0023      1.217     -0.824      0.410        -3.388     1.384\n",
       "e12345        -1.0212      1.708     -0.598      0.550        -4.371     2.328\n",
       "==============================================================================\n",
       "Omnibus:                       92.409   Durbin-Watson:                   0.737\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               65.910\n",
       "Skew:                           0.333   Prob(JB):                     4.87e-15\n",
       "Kurtosis:                       2.430   Cond. No.                     2.62e+14\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 1.4e-25. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_DailyFecundity.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Plot inferred coefficients with confidence intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0ffc1f9390>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAAAAAAXQMNwBAAAABmrFup6sWNOVJKlftSrd3d1peVtDWpp87g2b871SbEog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\n5YST9+h3PKkMfaBh1t/rUdT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8I3+w45Z7k9wLUFWPTvJtYLeOGxZyTFc7qqrTgN8F9gQu\nqqrjph5+XVcdNG9UCjgKOAd4NXAV8GvA+7qKqKpvzPm4DviVyf2OMv56qudkmmNyDfB84L0dNQB8\nhObsuD2B7wCXV9Uu7WNLO+x4H83XxjHAl4BTk+xCM0h4eocdf0Ez2PEimhceLwI2m7rdlek3+n8I\nnJBkJ5pj1Nn3Cj4v04bynAzhWMBwjscQOj4FbAvsRPN98sttyxKan7GdqKoVVXVn+7GiqlYAu0y2\nd9VBczwm3gk8BXgPsAnw0a4iPB6Da/A5sWNNrqZ5n/ie9uN04HE0r4m7fL0x/b7ovTS/cx9L8zO9\ns5/ndgy2o/PXHDMzJ1CSDwAfqKrjkpzRc873q2ormlG6C6vqduDmrna+hl9oS2h+sHblYOAZSe6v\nqlOAz1TVzkn+Kw+dqdWFZyfZs/0rw63AE5LcV1V/RjNg2JWbgDtpzmr4Ec0xuIzmOHVl+rgfTnNs\n7q6qz9DtsdgiyQXt7dOr6hrggqo6iuav2l1ZmuQ6gKr65ySXAyS5tqq6/F55Fs0LoKuSfKTtOSDJ\nKztsmOuJSb4CkOTKjo+Hz8v8+nxOhnYsoN/jMYSOXZNUe7brD4DnJVlVVZcD/9BRA8Anga2ANyX5\nIUBV/e/2BWqXpn+/HQTsm2RlVV2Kx6Ov4zGEBvA5sWNhy4DjgXdPfo63XxvP7Wj/89ktSbW3P19V\nb7HDjimdvOaYmUGgKR+vqjcAv0rzZvIymkt+7u0qIMlh7c1Tqupi4DHABWv4lPXtDpofpD+c+0BV\nfa/DjkcluR8gyR1VdTBwZlWdS3NZR1cmDSur6qok97X376+qzi4FS3JIVR0GnAmcnuT8qlqZpLMB\nQmCTqnoGzVl+S5Pc3batrKqfdNhBVT0myf9r939xVb0E+CzNCHtXps92/G9zHuvsazTJVVX1fOC4\n9mfGiXQ7GDaxc1WdT/PCbPuq2nTqdOwuz9DyeXnIIJ6TgRwLGMjxGFAH7cDPl9Neztve7+y5SXJ8\nVe0D/FlVnUfzV/U+vjYe0/6O3QDYJMnKts/j0d/xGEKDz4kdC0ry2ar6KvD7VXU08Eb6+drYvqr+\niOZ3yjZVtXRyPOj2d4odw+zo/DXHLF0ONnEW8FSa6+M/2N4+p6+YJJckOX8y8NCRs4EdF3jsMx12\nfLeqnjO5k+QnSX4TuJHmsr2u3FpVm7cNL5hsrKrtgC6fF5J8Hvj3wAFV9QW6HQyD5q/Fk1Ncb6uq\nJwBU1eNoB8s68i7g305vSHNd+EFAJ3OLtH6vmnk1SHLeZGN7adrZHXaQ5IH2jMaXAb/d5b6nHMpD\np0O/hPZ3QFVtS7envQ7xeXk5/TwvQ3lO/BodXsfVU7/bjp5sbL9PVnTUQLv/a4DntXcvYWpl1A5d\nAhxCc3ni37bPxeR3/W1r+sT1zeMxqAbA58SOhSW5q71K4TSa95FbdLXvKW+imZ7hauAkYHN48Fic\nb8foOzp/zTEzE0NPVNX1SfZ4pG1jUT0uVT85PS3Jj+bp2CHJp7vomNM03bE5sFmSc3vq2BV4Bs3k\nYp09L3MaJsfiUcCjJ6cXSpJmV1UtSUcT/c+z7yfQXAr+5T72PzQej+HxOdFC2strt0jS5VxR0uDM\n4uVg11bV/kmuAKiq/WhG70anmqXqT6BZnevvgf2Br9OsjrXokvzoETo6HQRaQ0eng0B9Py+P0ND7\nIFBVnZnkWDvG21FVG9JMCr09zeqO/33qsQdXSBhDxxAa2n3NXYnqCJq/RnW6EtU8HWNfEWtL4PFJ\nvjvnoT2BrhYcWK0jyQ9ozjpdbfWfrjvmbO+6YzuAJLfSnGW7cVU9Ncm3umqY21FVjweeDdzYdcec\nptOSnNTDfn1O1tzVy/PSd0ctsIJwVXW6gvBCHYxwJWM7fuqORX3NMYuDQPvQnFJ4S3v/ScCN1azE\ntCrJXv2lde4EHlqq/rnVLlVvhx19N1TVQvP+LKG5XM6OEXcAfwxsClwJ/FFVXZLkDe1jh9NMrj6W\njiE0QLOKy/doFhf4EnADzQoVh9CcinzUmDqqWV1oFatPbLrpZHuSLTtoKOD9wD9Vs/DBK5Jc1T78\nKWDu0sd2dNPxKuDNwJKqehfwCpolr99RVe9O8idj6Wjn0pi2BDhq6jLG4xe7oe3o/VgMrGMoz8sQ\nOj4E/ALN9AyHAo+mucznN2hWdj6hg4bBdNTDVyteAuw62d7V+2g7HtbR+WuOWZwT6AU0y6U+p/3Y\nqd02WdZ2TIayVL0dw+vou+Gfac7Qu2bq4+r24xfsGH3HM5McmeT9wH7A5lX1uap6NN2uLDiEjiE0\nQLMS1RuB36KZa++4JJcBvwM8bYQdn6RZAfQpSbZIsgVwS3t70QeAWicB+yR5OvBK4JxqJlmFbr82\n7Fjd62i+NvehGaA8tJ0PcX/guJF1HEaz2MPk98rVwEoe+v3SlSEciyF1DOV5GULHs5O8lObM1hcC\nL2RkYGgAAAqYSURBVEtyDs08gF2uEDaUjptoziItmvfNBwP/NHXbjn46On/NMXODQGlWWtoBOLC9\nfTewQZKb0+0qTEMwd6n6L9DhUvV2DLqj74b/BRyQZKepj53TLNf6sFXt7Bhdx4MTpie5v70M7R+A\nr9FOyjeijiE0PCjNPDOrrURFDyup9N3R/oX6AzSrDR1fVRt0uf/Whu2lVyS5kuaNwslVdXzHLXas\nbmWSe5L8C/Dd9vIjktw+wo49aCb4fQFwYZKzgBVJzmpvd2UIx2JIHUN5XobQ8eAKwsBqKwgDXV6C\nNYiOJIfQrNR7JvC0JDfRfN12+j7ajod1dP6aY+YGgarqrTRL106WFd4I+NP+ivqT5LAkdyQ5Bfg9\n4E+AF9thxwAa3g9svcBj77Zj9B1XV9ULpjckOZXmLyFPHlnHEBomHUNYiWooHXNXG1pO96sNrWj/\nf096fgAcQHMpwVPt6K1jVXs5GjSXcgBQVRvT7evq3juSrEjyeppVbT5dVb/d1b7n6P1YDKljKM/L\nQDqGsoLwUDomKxm/kP5WMrZj/o5OVzicxTmBDqNZcelagCT/t6r6WOpvUJJc0ncD2DHXEDr6aEjy\nIXjwhc9rgV+lGdG+nA6XebZjsB0vX0NHV5faDKJjCA1txzFr6HjeGj7157JjykbAUuBHNBPNvp5m\nEs97O9j3a5hzmVOSFe2gYXWwfzvmdxjtX2iTfH9q++OAN46wgyTXVNWBNN+zl3e579bkssC+j8VQ\nOiYNfT8vvXckeeECD62gmUpkVB0TSe4G3lBVTwP+Xdf7t2PBjgdo5oc8l2a8Y9HM4hLxVyZ5ZlVd\nm2TvqtoM+HrGNSG0NBOqKjS/4CZn6x0JPCZJly/Y7bBj8B1DaLBjwY47eWi1yyOBrZIs67JDkrT2\nqmppeynW9LZtktxmRz8d7SVPJHmgqjYCfgm4Kcm/dtUwsI5OVxacxTOBUlV/DGxVVf8FOBr4WM9N\nkub3S0n2mLp/cVVdb4cddgyywY7hdqymqq5Lsqcddgy1o8uGqtqBZiLmJwJfAf5w8ga3qs5L0snl\n8HY8rGN34H00c94cTzM9wYuB7wD/OckNHTQ8FziH5kzOa4Fj23lfAP6a7lYVtGP1jhfTrI76QFW9\nmmbi/7uA3arqNUm+OLKOzlcWnLlBoCSnV9Xzaf4ytxvwliQX9pwlaX7XVtX+Sa4AqKr9aFaHsMMO\nO4bXYMeAOqrq8AUeWgJs10WDHXYMvaH1CZrJXa8AfhO4pKoOTjNB84529NZxJs1g1OY0ix2cSLOy\n34uADwIHddDwbuDXk3yrql5Ks1jKUe3P9C5XFbRjdW+lWfFzE5rFMPZNcmNV7UjztdvJ4MuAOiYr\nC25Cs5DPU9ozgrYGLqaZ23W9mrlBoKraCbhsMvBTVZtU1ZOnRjEl9ayqrqOZq2Ap8LdVdUt7f0fg\n23bYYcdwGuwYbMdf0FyGNt91+11OUm2HHUNuAHh8ko+2t4+rqpcDl1bVIXS7Kpcdq9ticiZFVf1+\nkj9vt3+xqk7tqGGjyeU0Sf6yqm4APldVJ9LtsbBjjrSr51XVLUlubLfdPLk8a2QdK5PcA9xTVaut\nLFhVi/K8zNwgEHAu8Kyp+z9pt+3bT46keXQ+yd0C7FidHasbQscQGsCOuYbQ8Q3g9CTfnPtAVXU5\nSbYddgy5AWBpVW08mbA9yZ9W1a3AV4HN7OitY8Op2++d81hXKzCtrKrtpt5Uf6uqDgL+CthlzZ9q\nx2Kqqg3aiZCnVwLdkI5X5xpIx6p6aJ6mTlYWnMVBoEcleXApvST3VTOJk6SBSHJz3w1gx1x2rG4I\nHUNoADvmGkjH62kufZ/PYQtst8OOLjuG0ADwcWA/mmWVAUjyN1W1jObyFzv66fhQVW2e5K4kH55s\nrKqnAH/TUcObgW2BWycbkny/qp5DcwlOV+xY3bE0gyz3JrlyavsOwDtH2NH5yoKzuDrYhcAZSc5v\n7x8KHJ+ki+tKJUmSOlNVZwEnJLmjvb818J4kR6/5M+2wYzwNdtgh6afX6TV368mrgZOq6pb2Gv0T\naUbxJEmSft7sNXnzBM0cAcAz7LBjQB1DaLDDjp9JVX2l7wawY64uO6pqy6p6R1WdU1VHznnswwt9\n3iJ07FBVf15Vl1XVSVW1dOqx8xZjnzN1OVg7QdM+Sfavqs0BktzVc5YkSdJi2aCqtm7fOFFVj6Wf\n12922DHkBjvseJiqWmjJ8yXA07tosGO4HcAngX+kWQns6Kp6CXBkkh8D+3fY0fmKfjM1CJTkgar6\nneamgz+SJOnn3nuAr1fVue39ZcAf2GHHgDqG0GCHHfO5imZupPmWP9+qowY7htuxS5KXtLfPq6rf\nBb7WrqTXpc5X9JvFOYHeCdxGsyzl3ZPtSf61tyhJkqRFUlV7AAe2d7+W5Ho77BhSxxAa7LBjnv1+\nEzgsyT/O89j3kuxgx6g7bgCe2q4ONtn2CuBNwOZJFuUsnHk6vkVztdO9U9ueB3wU2CzJE9b3Pmfq\nTKDWEe1/f2tq2ypg5x5aJEmSFlX7hqmXN2922DErDXbYMY9TWHgO3OPsGH3HF2kGJx9crS7Jp6rq\nVuCMDjs6X9Fv5gaBkuzUd4MkSZIkadB2BJ5UVb9hhx3z+CGwV1XtNc9jH+kqIsn7Ftj+d8DzF2Of\nMzcIVFWbAm8AnpTk2Kr6RWC3JH/Vc5okSZIkaRg2b/+7G7AvcH57/2DgSjvsGEjHgqrqLUnetr7/\n3ZkbBKKZxfsa4Fnt/f8DnAs4CCRJkiRJIsmpAFV1KbB3khXt/VOAL9lhxxA6HsExgINANLN4H1FV\n/xEgyT1VNd/M4pIkSZKkcdsWuG/q/n3tNjvs6L2jqu5c4KElwCaLsc9ZHAS6r6o2oV0urap2AX7c\nb5IkSZIkaYDOBq6sqs+3918MfMoOOwbScQewb5Ifzn2gqr63GDtcaFbuITsFuADYoao+DVwEnNhr\nkSRJkiRpcJL8AfBK4Pb245VJ3mGHHQPpOJtmsuz5fGYxdrhk1apVi/HvLqqqehywP80pUlckua3n\nJEmSJEmSpJ9ZO8XNy4Cdk7ytqp4EbJdkvU9SPXOXg1XVRUkOYmqypqltkiRJkiRJs+TDwAPAgTST\nQa8APkuzctl6NTODQFW1MbApsE1VbU1zFhDAlsATewuTJEmSJElae/sl2buq/g4gye1VtdFi7Ghm\nBoGAVwGvB/4NzRLxk0GgO4EP9hUlSZIkSZK0DlZW1YY8tADW42nODFrvZm5OoKo6LskZfXdIkiRJ\nkiStq6p6GXAEsDdwFvBS4OQk567vfc3cIBBAVT0LeDJTZzIlObu3IEmSJEmSpLVUVbsDB9Fc9XRR\nkhsWYz8zNwhUVecAuwB/D/yk3bwqyfH9VUmSJEmSJA3bLM0JNPHLwB5JZmv0SpIkSZIkqUcb9B2w\nFr4JbNd3hCRJkiRJ0iyZxTOBtgGur6orgR9PNiY5pL8kSZIkSZKkYZvFQaBT+g6QJEmSJEmaNTM3\nMbQkSZIkSZJ+djNzJlBVrQDmG7FaQrM62JYdJ0mSJEmSJM0MzwSSJEmSJEkagVlcHUySJEmSJEk/\nIweBJEmSJEmSRsBBIEmSJEmSpBFwEEiSJEmSJGkEHASSJEmSJEkagf8PAxDr+x+UCnoAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ffc1f3b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_int_DailyFecundity = pd.DataFrame(lm_DailyFecundity.conf_int())\n",
    "conf_int_DailyFecundity[2] = [lm_DailyFecundity.params.a,\n",
    "                            lm_DailyFecundity.params.a,\n",
    "                            lm_DailyFecundity.params.a1,\n",
    "                            lm_DailyFecundity.params.a2,\n",
    "                            lm_DailyFecundity.params.a3,\n",
    "                            lm_DailyFecundity.params.a4,\n",
    "                            lm_DailyFecundity.params.a5,\n",
    "                            lm_DailyFecundity.params.b12,\n",
    "                            lm_DailyFecundity.params.b13,\n",
    "                            lm_DailyFecundity.params.b14,\n",
    "                            lm_DailyFecundity.params.b15,\n",
    "                            lm_DailyFecundity.params.b23,\n",
    "                            lm_DailyFecundity.params.b24,\n",
    "                            lm_DailyFecundity.params.b25,\n",
    "                            lm_DailyFecundity.params.b34,\n",
    "                            lm_DailyFecundity.params.b35,\n",
    "                            lm_DailyFecundity.params.b45,\n",
    "                            lm_DailyFecundity.params.c123,\n",
    "                            lm_DailyFecundity.params.c124,\n",
    "                            lm_DailyFecundity.params.c125,\n",
    "                            lm_DailyFecundity.params.c134,\n",
    "                            lm_DailyFecundity.params.c135,\n",
    "                            lm_DailyFecundity.params.c145,\n",
    "                            lm_DailyFecundity.params.c234,\n",
    "                            lm_DailyFecundity.params.c235,\n",
    "                            lm_DailyFecundity.params.c245,\n",
    "                            lm_DailyFecundity.params.c345,\n",
    "                            lm_DailyFecundity.params.d1234,\n",
    "                            lm_DailyFecundity.params.d1235,\n",
    "                            lm_DailyFecundity.params.d1245,\n",
    "                            lm_DailyFecundity.params.d1345,\n",
    "                            lm_DailyFecundity.params.d2345,\n",
    "                            lm_DailyFecundity.params.e12345]\n",
    "conf_int_DailyFecundity.columns = [\"95% conf. int. bottom\", \"95% conf. int. top\", \"coef\"]\n",
    "# Set Intercept and a to 0, as otherwise the rest of the plot vanishes.\n",
    "conf_int_DailyFecundity[\"coef\"].Intercept = 0\n",
    "conf_int_DailyFecundity[\"95% conf. int. bottom\"].Intercept = 0\n",
    "conf_int_DailyFecundity[\"95% conf. int. top\"].Intercept = 0\n",
    "conf_int_DailyFecundity[\"coef\"].a = 0\n",
    "conf_int_DailyFecundity[\"95% conf. int. bottom\"].a = 0\n",
    "conf_int_DailyFecundity[\"95% conf. int. top\"].a = 0\n",
    "conf_int_DailyFecundity.plot.bar(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Development:\n",
    "- Read the data and fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_Development = pd.read_csv(\"DevelopmentData_processed.csv\")\n",
    "\n",
    "lm_Development = smf.ols(formula=\"Development ~ a + a1 + a2 + a3 + a4 + a5 +\"\n",
    "                     \"b12 + b13 + b14 + b15 + b23 + b24 + b25 + b34 + b35 + b45 +\"\n",
    "                     \"c123 + c124 + c125 + c134 + c135 + c145 + c234 + c235 + c245 + c345 +\"\n",
    "                     \"d1234 + d1235 + d1245 + d1345 + d2345 + e12345\", data=data_Development).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Output summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Development</td>   <th>  R-squared:         </th> <td>   0.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   6.265</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 03 May 2018</td> <th>  Prob (F-statistic):</th> <td>3.94e-22</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:11:37</td>     <th>  Log-Likelihood:    </th> <td> -719.38</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   766</td>      <th>  AIC:               </th> <td>   1503.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   734</td>      <th>  BIC:               </th> <td>   1651.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    31</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>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    5.2273</td> <td>    0.067</td> <td>   77.558</td> <td> 0.000</td> <td>    5.095     5.360</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a</th>         <td>    5.2273</td> <td>    0.067</td> <td>   77.558</td> <td> 0.000</td> <td>    5.095     5.360</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a1</th>        <td>    0.7121</td> <td>    0.187</td> <td>    3.816</td> <td> 0.000</td> <td>    0.346     1.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a2</th>        <td>   -0.3295</td> <td>    0.187</td> <td>   -1.766</td> <td> 0.078</td> <td>   -0.696     0.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a3</th>        <td>    0.0871</td> <td>    0.187</td> <td>    0.467</td> <td> 0.641</td> <td>   -0.279     0.453</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a4</th>        <td>    0.0038</td> <td>    0.187</td> <td>    0.020</td> <td> 0.984</td> <td>   -0.363     0.370</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a5</th>        <td>   -0.5795</td> <td>    0.187</td> <td>   -3.106</td> <td> 0.002</td> <td>   -0.946    -0.213</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b12</th>       <td>   -0.6705</td> <td>    0.261</td> <td>   -2.568</td> <td> 0.010</td> <td>   -1.183    -0.158</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b13</th>       <td>   -0.7121</td> <td>    0.261</td> <td>   -2.728</td> <td> 0.007</td> <td>   -1.225    -0.200</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b14</th>       <td>   -0.7955</td> <td>    0.261</td> <td>   -3.047</td> <td> 0.002</td> <td>   -1.308    -0.283</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b15</th>       <td>   -0.7538</td> <td>    0.261</td> <td>   -2.888</td> <td> 0.004</td> <td>   -1.266    -0.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b23</th>       <td>   -0.3371</td> <td>    0.261</td> <td>   -1.291</td> <td> 0.197</td> <td>   -0.850     0.175</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b24</th>       <td>    0.1629</td> <td>    0.261</td> <td>    0.624</td> <td> 0.533</td> <td>   -0.350     0.675</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b25</th>       <td>    0.0795</td> <td>    0.261</td> <td>    0.305</td> <td> 0.761</td> <td>   -0.433     0.592</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b34</th>       <td>   -0.4205</td> <td>    0.261</td> <td>   -1.611</td> <td> 0.108</td> <td>   -0.933     0.092</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b35</th>       <td>   -0.0038</td> <td>    0.261</td> <td>   -0.015</td> <td> 0.988</td> <td>   -0.516     0.509</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b45</th>       <td>   -0.0871</td> <td>    0.261</td> <td>   -0.334</td> <td> 0.739</td> <td>   -0.600     0.425</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c123</th>      <td>    0.6288</td> <td>    0.367</td> <td>    1.713</td> <td> 0.087</td> <td>   -0.092     1.349</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c124</th>      <td>    0.6288</td> <td>    0.367</td> <td>    1.713</td> <td> 0.087</td> <td>   -0.092     1.349</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c125</th>      <td>    0.9621</td> <td>    0.367</td> <td>    2.621</td> <td> 0.009</td> <td>    0.241     1.683</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c134</th>      <td>    0.9205</td> <td>    0.367</td> <td>    2.507</td> <td> 0.012</td> <td>    0.200     1.641</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c135</th>      <td>    0.9205</td> <td>    0.367</td> <td>    2.507</td> <td> 0.012</td> <td>    0.200     1.641</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c145</th>      <td>    0.8788</td> <td>    0.367</td> <td>    2.394</td> <td> 0.017</td> <td>    0.158     1.599</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c234</th>      <td>    0.2955</td> <td>    0.367</td> <td>    0.805</td> <td> 0.421</td> <td>   -0.425     1.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c235</th>      <td>    0.2121</td> <td>    0.367</td> <td>    0.578</td> <td> 0.564</td> <td>   -0.509     0.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c245</th>      <td>    0.2538</td> <td>    0.367</td> <td>    0.691</td> <td> 0.490</td> <td>   -0.467     0.974</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c345</th>      <td>    0.5871</td> <td>    0.367</td> <td>    1.599</td> <td> 0.110</td> <td>   -0.134     1.308</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d1234</th>     <td>   -0.6288</td> <td>    0.518</td> <td>   -1.215</td> <td> 0.225</td> <td>   -1.645     0.388</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d1235</th>     <td>   -0.9621</td> <td>    0.518</td> <td>   -1.858</td> <td> 0.064</td> <td>   -1.978     0.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d1245</th>     <td>   -1.1288</td> <td>    0.518</td> <td>   -2.180</td> <td> 0.030</td> <td>   -2.145    -0.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d1345</th>     <td>   -1.3371</td> <td>    0.518</td> <td>   -2.583</td> <td> 0.010</td> <td>   -2.353    -0.321</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d2345</th>     <td>   -0.3371</td> <td>    0.518</td> <td>   -0.651</td> <td> 0.515</td> <td>   -1.353     0.679</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>e12345</th>    <td>    1.4205</td> <td>    0.731</td> <td>    1.943</td> <td> 0.052</td> <td>   -0.015     2.856</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>11.991</td> <th>  Durbin-Watson:     </th> <td>   1.750</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.002</td> <th>  Jarque-Bera (JB):  </th> <td>  12.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.293</td> <th>  Prob(JB):          </th> <td> 0.00236</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.190</td> <th>  Cond. No.          </th> <td>1.49e+15</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:            Development   R-squared:                       0.209\n",
       "Model:                            OLS   Adj. R-squared:                  0.176\n",
       "Method:                 Least Squares   F-statistic:                     6.265\n",
       "Date:                Thu, 03 May 2018   Prob (F-statistic):           3.94e-22\n",
       "Time:                        11:11:37   Log-Likelihood:                -719.38\n",
       "No. Observations:                 766   AIC:                             1503.\n",
       "Df Residuals:                     734   BIC:                             1651.\n",
       "Df Model:                          31                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      5.2273      0.067     77.558      0.000         5.095     5.360\n",
       "a              5.2273      0.067     77.558      0.000         5.095     5.360\n",
       "a1             0.7121      0.187      3.816      0.000         0.346     1.078\n",
       "a2            -0.3295      0.187     -1.766      0.078        -0.696     0.037\n",
       "a3             0.0871      0.187      0.467      0.641        -0.279     0.453\n",
       "a4             0.0038      0.187      0.020      0.984        -0.363     0.370\n",
       "a5            -0.5795      0.187     -3.106      0.002        -0.946    -0.213\n",
       "b12           -0.6705      0.261     -2.568      0.010        -1.183    -0.158\n",
       "b13           -0.7121      0.261     -2.728      0.007        -1.225    -0.200\n",
       "b14           -0.7955      0.261     -3.047      0.002        -1.308    -0.283\n",
       "b15           -0.7538      0.261     -2.888      0.004        -1.266    -0.241\n",
       "b23           -0.3371      0.261     -1.291      0.197        -0.850     0.175\n",
       "b24            0.1629      0.261      0.624      0.533        -0.350     0.675\n",
       "b25            0.0795      0.261      0.305      0.761        -0.433     0.592\n",
       "b34           -0.4205      0.261     -1.611      0.108        -0.933     0.092\n",
       "b35           -0.0038      0.261     -0.015      0.988        -0.516     0.509\n",
       "b45           -0.0871      0.261     -0.334      0.739        -0.600     0.425\n",
       "c123           0.6288      0.367      1.713      0.087        -0.092     1.349\n",
       "c124           0.6288      0.367      1.713      0.087        -0.092     1.349\n",
       "c125           0.9621      0.367      2.621      0.009         0.241     1.683\n",
       "c134           0.9205      0.367      2.507      0.012         0.200     1.641\n",
       "c135           0.9205      0.367      2.507      0.012         0.200     1.641\n",
       "c145           0.8788      0.367      2.394      0.017         0.158     1.599\n",
       "c234           0.2955      0.367      0.805      0.421        -0.425     1.016\n",
       "c235           0.2121      0.367      0.578      0.564        -0.509     0.933\n",
       "c245           0.2538      0.367      0.691      0.490        -0.467     0.974\n",
       "c345           0.5871      0.367      1.599      0.110        -0.134     1.308\n",
       "d1234         -0.6288      0.518     -1.215      0.225        -1.645     0.388\n",
       "d1235         -0.9621      0.518     -1.858      0.064        -1.978     0.054\n",
       "d1245         -1.1288      0.518     -2.180      0.030        -2.145    -0.112\n",
       "d1345         -1.3371      0.518     -2.583      0.010        -2.353    -0.321\n",
       "d2345         -0.3371      0.518     -0.651      0.515        -1.353     0.679\n",
       "e12345         1.4205      0.731      1.943      0.052        -0.015     2.856\n",
       "==============================================================================\n",
       "Omnibus:                       11.991   Durbin-Watson:                   1.750\n",
       "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               12.099\n",
       "Skew:                           0.293   Prob(JB):                      0.00236\n",
       "Kurtosis:                       3.190   Cond. No.                     1.49e+15\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 1.6e-27. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_Development.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Plot inferred coefficients with confidence intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0ffc91b7b8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAKAAlEAAAAAABaAEAgAAACgAJRAAAABAASiBAAAAAApACQQAAABQAEogAAAA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\nSZIkSZIkjYCDQJIkSZIkSSPw/wGmFiaZ7RWAiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ffc91ff60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_int_Development = pd.DataFrame(lm_Development.conf_int())\n",
    "conf_int_Development[2] = [lm_Development.params.a,\n",
    "                            lm_Development.params.a,\n",
    "                            lm_Development.params.a1,\n",
    "                            lm_Development.params.a2,\n",
    "                            lm_Development.params.a3,\n",
    "                            lm_Development.params.a4,\n",
    "                            lm_Development.params.a5,\n",
    "                            lm_Development.params.b12,\n",
    "                            lm_Development.params.b13,\n",
    "                            lm_Development.params.b14,\n",
    "                            lm_Development.params.b15,\n",
    "                            lm_Development.params.b23,\n",
    "                            lm_Development.params.b24,\n",
    "                            lm_Development.params.b25,\n",
    "                            lm_Development.params.b34,\n",
    "                            lm_Development.params.b35,\n",
    "                            lm_Development.params.b45,\n",
    "                            lm_Development.params.c123,\n",
    "                            lm_Development.params.c124,\n",
    "                            lm_Development.params.c125,\n",
    "                            lm_Development.params.c134,\n",
    "                            lm_Development.params.c135,\n",
    "                            lm_Development.params.c145,\n",
    "                            lm_Development.params.c234,\n",
    "                            lm_Development.params.c235,\n",
    "                            lm_Development.params.c245,\n",
    "                            lm_Development.params.c345,\n",
    "                            lm_Development.params.d1234,\n",
    "                            lm_Development.params.d1235,\n",
    "                            lm_Development.params.d1245,\n",
    "                            lm_Development.params.d1345,\n",
    "                            lm_Development.params.d2345,\n",
    "                            lm_Development.params.e12345]\n",
    "conf_int_Development.columns = [\"95% conf. int. bottom\", \"95% conf. int. top\", \"coef\"]\n",
    "# Comment all the following lines out to plot the Intercept and a.\n",
    "conf_int_Development[\"coef\"].Intercept = 0\n",
    "conf_int_Development[\"95% conf. int. bottom\"].Intercept = 0\n",
    "conf_int_Development[\"95% conf. int. top\"].Intercept = 0\n",
    "conf_int_Development[\"coef\"].a = 0\n",
    "conf_int_Development[\"95% conf. int. bottom\"].a = 0\n",
    "conf_int_Development[\"95% conf. int. top\"].a = 0\n",
    "conf_int_Development.plot.bar(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Survival:\n",
    "- Read the data and fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_Survival = pd.read_csv(\"SurvivalData_processed.csv\")\n",
    "\n",
    "lm_Survival = smf.ols(formula=\"Survival ~ a + a1 + a2 + a3 + a4 + a5 +\"\n",
    "                     \"b12 + b13 + b14 + b15 + b23 + b24 + b25 + b34 + b35 + b45 +\"\n",
    "                     \"c123 + c124 + c125 + c134 + c135 + c145 + c234 + c235 + c245 + c345 +\"\n",
    "                     \"d1234 + d1235 + d1245 + d1345 + d2345 + e12345\", data=data_Survival).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Output summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>Survival</td>     <th>  R-squared:         </th> <td>   0.065</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.056</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   7.049</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 03 May 2018</td> <th>  Prob (F-statistic):</th> <td>6.81e-29</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:11:38</td>     <th>  Log-Likelihood:    </th> <td> -12325.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  3163</td>      <th>  AIC:               </th> <td>2.471e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  3131</td>      <th>  BIC:               </th> <td>2.491e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    31</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>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   26.6250</td> <td>    0.669</td> <td>   39.771</td> <td> 0.000</td> <td>   25.312    27.938</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a</th>         <td>   26.6250</td> <td>    0.669</td> <td>   39.771</td> <td> 0.000</td> <td>   25.312    27.938</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a1</th>        <td>   -6.6000</td> <td>    1.796</td> <td>   -3.674</td> <td> 0.000</td> <td>  -10.122    -3.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a2</th>        <td>   -1.0750</td> <td>    1.894</td> <td>   -0.568</td> <td> 0.570</td> <td>   -4.788     2.638</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a3</th>        <td>   -0.7206</td> <td>    1.789</td> <td>   -0.403</td> <td> 0.687</td> <td>   -4.227     2.786</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a4</th>        <td>   -4.9500</td> <td>    1.796</td> <td>   -2.756</td> <td> 0.006</td> <td>   -8.472    -1.428</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a5</th>        <td>  -10.0900</td> <td>    1.796</td> <td>   -5.617</td> <td> 0.000</td> <td>  -13.612    -6.568</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b12</th>       <td>    5.2350</td> <td>    2.540</td> <td>    2.061</td> <td> 0.039</td> <td>    0.254    10.216</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b13</th>       <td>    3.0506</td> <td>    2.463</td> <td>    1.238</td> <td> 0.216</td> <td>   -1.779     7.880</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b14</th>       <td>    6.0900</td> <td>    2.469</td> <td>    2.467</td> <td> 0.014</td> <td>    1.249    10.931</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b15</th>       <td>    6.9200</td> <td>    2.469</td> <td>    2.803</td> <td> 0.005</td> <td>    2.079    11.761</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b23</th>       <td>   -1.2244</td> <td>    2.535</td> <td>   -0.483</td> <td> 0.629</td> <td>   -6.195     3.746</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b24</th>       <td>   -0.4350</td> <td>    2.540</td> <td>   -0.171</td> <td> 0.864</td> <td>   -5.416     4.546</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b25</th>       <td>    4.7050</td> <td>    2.540</td> <td>    1.852</td> <td> 0.064</td> <td>   -0.276     9.686</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b34</th>       <td>    2.4106</td> <td>    2.463</td> <td>    0.979</td> <td> 0.328</td> <td>   -2.419     7.240</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b35</th>       <td>    7.6706</td> <td>    2.463</td> <td>    3.114</td> <td> 0.002</td> <td>    2.841    12.500</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b45</th>       <td>    5.3200</td> <td>    2.469</td> <td>    2.155</td> <td> 0.031</td> <td>    0.479    10.161</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c123</th>      <td>   -2.5656</td> <td>    3.487</td> <td>   -0.736</td> <td> 0.462</td> <td>   -9.404     4.272</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c124</th>      <td>   -8.1450</td> <td>    3.491</td> <td>   -2.333</td> <td> 0.020</td> <td>  -14.991    -1.299</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c125</th>      <td>   -8.8995</td> <td>    3.489</td> <td>   -2.550</td> <td> 0.011</td> <td>  -15.741    -2.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c134</th>      <td>   -4.6406</td> <td>    3.436</td> <td>   -1.351</td> <td> 0.177</td> <td>  -11.377     2.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c135</th>      <td>   -7.1506</td> <td>    3.436</td> <td>   -2.081</td> <td> 0.037</td> <td>  -13.887    -0.414</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c145</th>      <td>   -9.2300</td> <td>    3.440</td> <td>   -2.683</td> <td> 0.007</td> <td>  -15.974    -2.486</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c234</th>      <td>   -1.7256</td> <td>    3.487</td> <td>   -0.495</td> <td> 0.621</td> <td>   -8.564     5.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c235</th>      <td>   -6.4656</td> <td>    3.487</td> <td>   -1.854</td> <td> 0.064</td> <td>  -13.304     0.372</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c245</th>      <td>   -1.7550</td> <td>    3.491</td> <td>   -0.503</td> <td> 0.615</td> <td>   -8.601     5.091</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c345</th>      <td>   -3.1706</td> <td>    3.436</td> <td>   -0.923</td> <td> 0.356</td> <td>   -9.907     3.566</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d1234</th>     <td>    7.5156</td> <td>    4.862</td> <td>    1.546</td> <td> 0.122</td> <td>   -2.017    17.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d1235</th>     <td>   12.2300</td> <td>    4.860</td> <td>    2.516</td> <td> 0.012</td> <td>    2.701    21.760</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d1245</th>     <td>   11.9395</td> <td>    4.863</td> <td>    2.455</td> <td> 0.014</td> <td>    2.404    21.475</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d1345</th>     <td>    7.9906</td> <td>    4.825</td> <td>    1.656</td> <td> 0.098</td> <td>   -1.469    17.450</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d2345</th>     <td>    1.3956</td> <td>    4.862</td> <td>    0.287</td> <td> 0.774</td> <td>   -8.137    10.928</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>e12345</th>    <td>  -13.4300</td> <td>    6.824</td> <td>   -1.968</td> <td> 0.049</td> <td>  -26.810    -0.050</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>204.087</td> <th>  Durbin-Watson:     </th> <td>   0.761</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 262.223</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.597</td>  <th>  Prob(JB):          </th> <td>1.15e-57</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.751</td>  <th>  Cond. No.          </th> <td>4.38e+15</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               Survival   R-squared:                       0.065\n",
       "Model:                            OLS   Adj. R-squared:                  0.056\n",
       "Method:                 Least Squares   F-statistic:                     7.049\n",
       "Date:                Thu, 03 May 2018   Prob (F-statistic):           6.81e-29\n",
       "Time:                        11:11:38   Log-Likelihood:                -12325.\n",
       "No. Observations:                3163   AIC:                         2.471e+04\n",
       "Df Residuals:                    3131   BIC:                         2.491e+04\n",
       "Df Model:                          31                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     26.6250      0.669     39.771      0.000        25.312    27.938\n",
       "a             26.6250      0.669     39.771      0.000        25.312    27.938\n",
       "a1            -6.6000      1.796     -3.674      0.000       -10.122    -3.078\n",
       "a2            -1.0750      1.894     -0.568      0.570        -4.788     2.638\n",
       "a3            -0.7206      1.789     -0.403      0.687        -4.227     2.786\n",
       "a4            -4.9500      1.796     -2.756      0.006        -8.472    -1.428\n",
       "a5           -10.0900      1.796     -5.617      0.000       -13.612    -6.568\n",
       "b12            5.2350      2.540      2.061      0.039         0.254    10.216\n",
       "b13            3.0506      2.463      1.238      0.216        -1.779     7.880\n",
       "b14            6.0900      2.469      2.467      0.014         1.249    10.931\n",
       "b15            6.9200      2.469      2.803      0.005         2.079    11.761\n",
       "b23           -1.2244      2.535     -0.483      0.629        -6.195     3.746\n",
       "b24           -0.4350      2.540     -0.171      0.864        -5.416     4.546\n",
       "b25            4.7050      2.540      1.852      0.064        -0.276     9.686\n",
       "b34            2.4106      2.463      0.979      0.328        -2.419     7.240\n",
       "b35            7.6706      2.463      3.114      0.002         2.841    12.500\n",
       "b45            5.3200      2.469      2.155      0.031         0.479    10.161\n",
       "c123          -2.5656      3.487     -0.736      0.462        -9.404     4.272\n",
       "c124          -8.1450      3.491     -2.333      0.020       -14.991    -1.299\n",
       "c125          -8.8995      3.489     -2.550      0.011       -15.741    -2.058\n",
       "c134          -4.6406      3.436     -1.351      0.177       -11.377     2.096\n",
       "c135          -7.1506      3.436     -2.081      0.037       -13.887    -0.414\n",
       "c145          -9.2300      3.440     -2.683      0.007       -15.974    -2.486\n",
       "c234          -1.7256      3.487     -0.495      0.621        -8.564     5.112\n",
       "c235          -6.4656      3.487     -1.854      0.064       -13.304     0.372\n",
       "c245          -1.7550      3.491     -0.503      0.615        -8.601     5.091\n",
       "c345          -3.1706      3.436     -0.923      0.356        -9.907     3.566\n",
       "d1234          7.5156      4.862      1.546      0.122        -2.017    17.048\n",
       "d1235         12.2300      4.860      2.516      0.012         2.701    21.760\n",
       "d1245         11.9395      4.863      2.455      0.014         2.404    21.475\n",
       "d1345          7.9906      4.825      1.656      0.098        -1.469    17.450\n",
       "d2345          1.3956      4.862      0.287      0.774        -8.137    10.928\n",
       "e12345       -13.4300      6.824     -1.968      0.049       -26.810    -0.050\n",
       "==============================================================================\n",
       "Omnibus:                      204.087   Durbin-Watson:                   0.761\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              262.223\n",
       "Skew:                          -0.597   Prob(JB):                     1.15e-57\n",
       "Kurtosis:                       3.751   Cond. No.                     4.38e+15\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 7.75e-28. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_Survival.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Plot inferred coefficients with confidence intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0ffc6d48d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LygAAAKikrevbWNuGqlMOAQAAUEn11rextg1VpA4FAAAAqDDlEAAAAECFKYcA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\nkiRJkiRJA/b/AYFp/Ieb+GYjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ffc6d40b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_int_Survival = pd.DataFrame(lm_Survival.conf_int())\n",
    "conf_int_Survival[2] = [lm_Survival.params.a,\n",
    "                            lm_Survival.params.a,\n",
    "                            lm_Survival.params.a1,\n",
    "                            lm_Survival.params.a2,\n",
    "                            lm_Survival.params.a3,\n",
    "                            lm_Survival.params.a4,\n",
    "                            lm_Survival.params.a5,\n",
    "                            lm_Survival.params.b12,\n",
    "                            lm_Survival.params.b13,\n",
    "                            lm_Survival.params.b14,\n",
    "                            lm_Survival.params.b15,\n",
    "                            lm_Survival.params.b23,\n",
    "                            lm_Survival.params.b24,\n",
    "                            lm_Survival.params.b25,\n",
    "                            lm_Survival.params.b34,\n",
    "                            lm_Survival.params.b35,\n",
    "                            lm_Survival.params.b45,\n",
    "                            lm_Survival.params.c123,\n",
    "                            lm_Survival.params.c124,\n",
    "                            lm_Survival.params.c125,\n",
    "                            lm_Survival.params.c134,\n",
    "                            lm_Survival.params.c135,\n",
    "                            lm_Survival.params.c145,\n",
    "                            lm_Survival.params.c234,\n",
    "                            lm_Survival.params.c235,\n",
    "                            lm_Survival.params.c245,\n",
    "                            lm_Survival.params.c345,\n",
    "                            lm_Survival.params.d1234,\n",
    "                            lm_Survival.params.d1235,\n",
    "                            lm_Survival.params.d1245,\n",
    "                            lm_Survival.params.d1345,\n",
    "                            lm_Survival.params.d2345,\n",
    "                            lm_Survival.params.e12345]\n",
    "conf_int_Survival.columns = [\"95% conf. int. bottom\", \"95% conf. int. top\", \"coef\"]\n",
    "# Comment all the following lines out to plot the Intercept and a.\n",
    "conf_int_Survival[\"coef\"].Intercept = 0\n",
    "conf_int_Survival[\"95% conf. int. bottom\"].Intercept = 0\n",
    "conf_int_Survival[\"95% conf. int. top\"].Intercept = 0\n",
    "conf_int_Survival[\"coef\"].a = 0\n",
    "conf_int_Survival[\"95% conf. int. bottom\"].a = 0\n",
    "conf_int_Survival[\"95% conf. int. top\"].a = 0\n",
    "conf_int_Survival.plot.bar(figsize=(20,10))"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}