Statsmodels example: Interactions and ANOVA

example_interactions.ipynb

{
 "metadata": {
  "name": "example_interactions"
 },
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 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Interactions and ANOVA\n",
      "\n",
      "Note: This script is based heavily on Jonathan Taylor's class notes http://www.stanford.edu/class/stats191/interactions.html\n",
      "\n",
      "Download and format data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from urllib2 import urlopen\n",
      "import numpy as np\n",
      "np.set_printoptions(precision=4, suppress=True)\n",
      "import statsmodels.api as sm\n",
      "import pandas\n",
      "import matplotlib.pyplot as plt\n",
      "from statsmodels.formula.api import ols\n",
      "from statsmodels.graphics.api import interaction_plot, abline_plot\n",
      "from statsmodels.stats.anova import anova_lm\n",
      "\n",
      "try:\n",
      "    salary_table = pandas.read_csv('salary.table')\n",
      "except:  # recent pandas should be able to read URL\n",
      "    url = 'http://stats191.stanford.edu/data/salary.table'\n",
      "    fh = urlopen(url)\n",
      "    salary_table = pandas.read_table(fh)\n",
      "    salary_table.to_csv('salary.table')\n",
      "\n",
      "E = salary_table.E\n",
      "M = salary_table.M\n",
      "X = salary_table.X\n",
      "S = salary_table.S"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Take a look at the data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(6,6))\n",
      "symbols = ['D', '^']\n",
      "colors = ['r', 'g', 'blue']\n",
      "factor_groups = salary_table.groupby(['E','M'])\n",
      "for values, group in factor_groups:\n",
      "    i,j = values\n",
      "    plt.scatter(group['X'], group['S'], marker=symbols[j], color=colors[i-1],\n",
      "               s=144)\n",
      "plt.xlabel('Experience');\n",
      "plt.ylabel('Salary');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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keShSazW1E0sgWkp6OrzyCsyYYetqRSSIFCgCmO1kW/a2KvedVFTqLOXtr962dV/KzJlg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\nEVsoUERExBYKFBERsYUCRUREbPH/AbkNhO+a5TmjAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Fit a linear model:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "formula = 'S ~ C(E) + C(M) + X'\n",
      "lm = ols(formula, salary_table).fit()\n",
      "print lm.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:                      S   R-squared:                       0.957\n",
        "Model:                            OLS   Adj. R-squared:                  0.953\n",
        "Method:                 Least Squares   F-statistic:                     226.8\n",
        "Date:                Sun, 26 Aug 2012   Prob (F-statistic):           2.23e-27\n",
        "Time:                        20:52:27   Log-Likelihood:                -381.63\n",
        "No. Observations:                  46   AIC:                             773.3\n",
        "Df Residuals:                      41   BIC:                             782.4\n",
        "Df Model:                           4                                         \n",
        "==============================================================================\n",
        "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------\n",
        "Intercept   8035.5976    386.689     20.781      0.000      7254.663  8816.532\n",
        "C(E)[T.2]   3144.0352    361.968      8.686      0.000      2413.025  3875.045\n",
        "C(E)[T.3]   2996.2103    411.753      7.277      0.000      2164.659  3827.762\n",
        "C(M)[T.1]   6883.5310    313.919     21.928      0.000      6249.559  7517.503\n",
        "X            546.1840     30.519     17.896      0.000       484.549   607.819\n",
        "==============================================================================\n",
        "Omnibus:                        2.293   Durbin-Watson:                   2.237\n",
        "Prob(Omnibus):                  0.318   Jarque-Bera (JB):                1.362\n",
        "Skew:                          -0.077   Prob(JB):                        0.506\n",
        "Kurtosis:                       2.171   Cond. No.                         33.5\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Have a look at the created design matrix: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lm.model.exog[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "array([[ 1.,  0.,  0.,  1.,  1.],\n",
        "       [ 1.,  0.,  1.,  0.,  1.],\n",
        "       [ 1.,  0.,  1.,  1.,  1.],\n",
        "       [ 1.,  1.,  0.,  0.,  1.],\n",
        "       [ 1.,  0.,  1.,  0.,  1.]])"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Or since we initially passed in a DataFrame, we have a DataFrame available in"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lm.model._data._orig_exog[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>Intercept</th>\n",
        "      <th>C(E)[T.2]</th>\n",
        "      <th>C(E)[T.3]</th>\n",
        "      <th>C(M)[T.1]</th>\n",
        "      <th>X</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "   Intercept  C(E)[T.2]  C(E)[T.3]  C(M)[T.1]  X\n",
        "0          1          0          0          1  1\n",
        "1          1          0          1          0  1\n",
        "2          1          0          1          1  1\n",
        "3          1          1          0          0  1\n",
        "4          1          0          1          0  1"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We keep a reference to the original untouched data in"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lm.model._data.frame[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>Unnamed: 0</th>\n",
        "      <th>S</th>\n",
        "      <th>X</th>\n",
        "      <th>E</th>\n",
        "      <th>M</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td> 0</td>\n",
        "      <td> 13876</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 11608</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> 2</td>\n",
        "      <td> 18701</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 3</td>\n",
        "      <td> 11283</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 4</td>\n",
        "      <td> 11767</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "   Unnamed: 0      S  X  E  M\n",
        "0           0  13876  1  1  1\n",
        "1           1  11608  1  3  0\n",
        "2           2  18701  1  3  1\n",
        "3           3  11283  1  2  0\n",
        "4           4  11767  1  3  0"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Influence statistics"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "infl = lm.get_influence()\n",
      "print infl.summary_table()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "=============================================================================================================\n",
        "       obs      endog     fitted     Cook's   student.   hat diag    dffits   ext.stud.     dffits     dfbeta\n",
        "                           value          d   residual              internal   residual                 slope\n",
        "-------------------------------------------------------------------------------------------------------------\n",
        "         0  13876.000  15465.313      0.104     -1.683      0.155     -0.722     -1.723     -0.739      0.376\n",
        "         1  11608.000  11577.992      0.000      0.031      0.130      0.012      0.031      0.012      0.000\n",
        "         2  18701.000  18461.523      0.001      0.247      0.109      0.086      0.244      0.085      0.000\n",
        "         3  11283.000  11725.817      0.005     -0.458      0.113     -0.163     -0.453     -0.162     -0.074\n",
        "         4  11767.000  11577.992      0.001      0.197      0.130      0.076      0.195      0.075      0.001\n",
        "         5  20872.000  19155.532      0.092      1.787      0.126      0.678      1.838      0.698      0.297\n",
        "         6  11772.000  12272.001      0.006     -0.513      0.101     -0.172     -0.509     -0.170     -0.083\n",
        "         7  10535.000   9127.966      0.056      1.457      0.116      0.529      1.478      0.537     -0.312\n",
        "         8  12195.000  12124.176      0.000      0.074      0.123      0.028      0.073      0.027      0.000\n",
        "         9  12313.000  12818.185      0.005     -0.516      0.091     -0.163     -0.511     -0.161     -0.082\n",
        "        10  14975.000  16557.681      0.084     -1.655      0.134     -0.650     -1.692     -0.664      0.367\n",
        "        11  21371.000  19701.716      0.078      1.728      0.116      0.624      1.772      0.640      0.284\n",
        "        12  19800.000  19553.891      0.001      0.252      0.096      0.082      0.249      0.081      0.000\n",
        "        13  11417.000  10220.334      0.033      1.227      0.098      0.405      1.234      0.408     -0.259\n",
        "        14  20263.000  20100.075      0.001      0.166      0.093      0.053      0.165      0.053     -0.000\n",
        "        15  13231.000  13216.544      0.000      0.015      0.114      0.005      0.015      0.005      0.000\n",
        "        16  12884.000  13364.369      0.004     -0.488      0.082     -0.146     -0.483     -0.145     -0.077\n",
        "        17  13245.000  13910.553      0.007     -0.674      0.075     -0.192     -0.669     -0.191     -0.106\n",
        "        18  13677.000  13762.728      0.000     -0.089      0.113     -0.032     -0.087     -0.031     -0.000\n",
        "        19  15965.000  17650.049      0.082     -1.747      0.119     -0.642     -1.794     -0.659      0.388\n",
        "        20  12336.000  11312.702      0.021      1.043      0.087      0.323      1.044      0.323     -0.219\n",
        "        21  21352.000  21192.443      0.001      0.163      0.091      0.052      0.161      0.051     -0.000\n",
        "        22  13839.000  14456.737      0.006     -0.624      0.070     -0.171     -0.619     -0.170     -0.097\n",
        "        23  22884.000  21340.268      0.052      1.579      0.095      0.511      1.610      0.521      0.252\n",
        "        24  16978.000  18742.417      0.083     -1.822      0.111     -0.644     -1.877     -0.664      0.406\n",
        "        25  14803.000  15549.105      0.008     -0.751      0.065     -0.199     -0.747     -0.198     -0.116\n",
        "        26  17404.000  19288.601      0.093     -1.944      0.110     -0.684     -2.016     -0.709      0.437\n",
        "        27  22184.000  22284.811      0.000     -0.103      0.096     -0.034     -0.102     -0.033      0.000\n",
        "        28  13548.000  12405.070      0.025      1.162      0.083      0.350      1.167      0.352     -0.246\n",
        "        29  14467.000  13497.438      0.018      0.987      0.086      0.304      0.987      0.304     -0.209\n",
        "        30  15942.000  16641.473      0.007     -0.705      0.068     -0.190     -0.701     -0.189     -0.109\n",
        "        31  23174.000  23377.179      0.001     -0.209      0.108     -0.073     -0.207     -0.072      0.001\n",
        "        32  23780.000  23525.004      0.001      0.260      0.092      0.083      0.257      0.082      0.040\n",
        "        33  25410.000  24071.188      0.040      1.370      0.096      0.446      1.386      0.451      0.213\n",
        "        34  14861.000  14043.622      0.014      0.834      0.091      0.263      0.831      0.262     -0.177\n",
        "        35  16882.000  17733.841      0.012     -0.863      0.077     -0.249     -0.860     -0.249     -0.133\n",
        "        36  24170.000  24469.547      0.003     -0.312      0.127     -0.119     -0.309     -0.118      0.002\n",
        "        37  15990.000  15135.990      0.018      0.878      0.104      0.300      0.876      0.299     -0.189\n",
        "        38  26330.000  25163.556      0.035      1.202      0.109      0.420      1.209      0.422      0.186\n",
        "        39  17949.000  18826.209      0.017     -0.897      0.093     -0.288     -0.895     -0.287     -0.138\n",
        "        40  25685.000  26108.099      0.008     -0.452      0.169     -0.204     -0.447     -0.202      0.003\n",
        "        41  27837.000  26802.108      0.039      1.087      0.141      0.440      1.089      0.441      0.168\n",
        "        42  18838.000  19918.577      0.033     -1.119      0.117     -0.407     -1.123     -0.408     -0.175\n",
        "        43  17483.000  16774.542      0.018      0.743      0.138      0.297      0.739      0.295     -0.164\n",
        "        44  19207.000  20464.761      0.052     -1.313      0.131     -0.511     -1.325     -0.515     -0.207\n",
        "        45  19346.000  18959.278      0.009      0.423      0.208      0.216      0.419      0.214     -0.098\n",
        "=============================================================================================================\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "or get a dataframe"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_infl = infl.summary_frame()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_infl[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>dfb_Intercept</th>\n",
        "      <th>dfb_C(E)[T.2]</th>\n",
        "      <th>dfb_C(E)[T.3]</th>\n",
        "      <th>dfb_C(M)[T.1]</th>\n",
        "      <th>dfb_X</th>\n",
        "      <th>cooks_d</th>\n",
        "      <th>dffits</th>\n",
        "      <th>dffits_internal</th>\n",
        "      <th>hat_diag</th>\n",
        "      <th>standard_resid</th>\n",
        "      <th>student_resid</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td>-0.505123</td>\n",
        "      <td> 0.376134</td>\n",
        "      <td> 0.483977</td>\n",
        "      <td>-0.369677</td>\n",
        "      <td> 0.399111</td>\n",
        "      <td> 0.104186</td>\n",
        "      <td>-0.738880</td>\n",
        "      <td>-0.721753</td>\n",
        "      <td> 0.155327</td>\n",
        "      <td>-1.683099</td>\n",
        "      <td>-1.723037</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> 0.004663</td>\n",
        "      <td> 0.000145</td>\n",
        "      <td> 0.006733</td>\n",
        "      <td>-0.006220</td>\n",
        "      <td>-0.004449</td>\n",
        "      <td> 0.000029</td>\n",
        "      <td> 0.011972</td>\n",
        "      <td> 0.012120</td>\n",
        "      <td> 0.130266</td>\n",
        "      <td> 0.031318</td>\n",
        "      <td> 0.030934</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> 0.013627</td>\n",
        "      <td> 0.000367</td>\n",
        "      <td> 0.036876</td>\n",
        "      <td> 0.030514</td>\n",
        "      <td>-0.034970</td>\n",
        "      <td> 0.001492</td>\n",
        "      <td> 0.085380</td>\n",
        "      <td> 0.086377</td>\n",
        "      <td> 0.109021</td>\n",
        "      <td> 0.246931</td>\n",
        "      <td> 0.244082</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td>-0.083152</td>\n",
        "      <td>-0.074411</td>\n",
        "      <td> 0.009704</td>\n",
        "      <td> 0.053783</td>\n",
        "      <td> 0.105122</td>\n",
        "      <td> 0.005338</td>\n",
        "      <td>-0.161773</td>\n",
        "      <td>-0.163364</td>\n",
        "      <td> 0.113030</td>\n",
        "      <td>-0.457630</td>\n",
        "      <td>-0.453173</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 0.029382</td>\n",
        "      <td> 0.000917</td>\n",
        "      <td> 0.042425</td>\n",
        "      <td>-0.039198</td>\n",
        "      <td>-0.028036</td>\n",
        "      <td> 0.001166</td>\n",
        "      <td> 0.075439</td>\n",
        "      <td> 0.076340</td>\n",
        "      <td> 0.130266</td>\n",
        "      <td> 0.197257</td>\n",
        "      <td> 0.194929</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "   dfb_Intercept  dfb_C(E)[T.2]  dfb_C(E)[T.3]  dfb_C(M)[T.1]     dfb_X   cooks_d    dffits  dffits_internal  hat_diag  standard_resid  student_resid\n",
        "0      -0.505123       0.376134       0.483977      -0.369677  0.399111  0.104186 -0.738880        -0.721753  0.155327       -1.683099      -1.723037\n",
        "1       0.004663       0.000145       0.006733      -0.006220 -0.004449  0.000029  0.011972         0.012120  0.130266        0.031318       0.030934\n",
        "2       0.013627       0.000367       0.036876       0.030514 -0.034970  0.001492  0.085380         0.086377  0.109021        0.246931       0.244082\n",
        "3      -0.083152      -0.074411       0.009704       0.053783  0.105122  0.005338 -0.161773        -0.163364  0.113030       -0.457630      -0.453173\n",
        "4       0.029382       0.000917       0.042425      -0.039198 -0.028036  0.001166  0.075439         0.076340  0.130266        0.197257       0.194929"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now plot the reiduals within the groups separately:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "resid = lm.resid\n",
      "plt.figure(figsize=(6,6));\n",
      "for values, group in factor_groups:\n",
      "    i,j = values\n",
      "    group_num = i*2 + j - 1  # for plotting purposes\n",
      "    x = [group_num] * len(group)\n",
      "    plt.scatter(x, resid[group.index], marker=symbols[j], color=colors[i-1],\n",
      "            s=144, edgecolors='black')\n",
      "plt.xlabel('Group');\n",
      "plt.ylabel('Residuals');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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2wHUUICLuKTc3l5Ejx/LjjxcCJZw5POzsIbIQX9+/EBc3giVLXnf5aayWqMlG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\nzlRaWkqo2cyKo0cZWbnsK6B35Q/AQm9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vep0AAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we will test some interactions using anova or f_test"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "interX_lm = ols(\"S ~ C(E) * X + C(M)\", salary_table).fit()\n",
      "print interX_lm.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:                      S   R-squared:                       0.961\n",
        "Model:                            OLS   Adj. R-squared:                  0.955\n",
        "Method:                 Least Squares   F-statistic:                     158.6\n",
        "Date:                Sun, 26 Aug 2012   Prob (F-statistic):           8.23e-26\n",
        "Time:                        20:52:28   Log-Likelihood:                -379.47\n",
        "No. Observations:                  46   AIC:                             772.9\n",
        "Df Residuals:                      39   BIC:                             785.7\n",
        "Df Model:                           6                                         \n",
        "===============================================================================\n",
        "                  coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "-------------------------------------------------------------------------------\n",
        "Intercept    7256.2800    549.494     13.205      0.000      6144.824  8367.736\n",
        "C(E)[T.2]    4172.5045    674.966      6.182      0.000      2807.256  5537.753\n",
        "C(E)[T.3]    3946.3649    686.693      5.747      0.000      2557.396  5335.333\n",
        "C(M)[T.1]    7102.4539    333.442     21.300      0.000      6428.005  7776.903\n",
        "X             632.2878     53.185     11.888      0.000       524.710   739.865\n",
        "C(E)[T.2]:X  -125.5147     69.863     -1.797      0.080      -266.826    15.796\n",
        "C(E)[T.3]:X  -141.2741     89.281     -1.582      0.122      -321.861    39.313\n",
        "==============================================================================\n",
        "Omnibus:                        0.432   Durbin-Watson:                   2.179\n",
        "Prob(Omnibus):                  0.806   Jarque-Bera (JB):                0.590\n",
        "Skew:                           0.144   Prob(JB):                        0.744\n",
        "Kurtosis:                       2.526   Cond. No.                         69.7\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Do an ANOVA check"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from statsmodels.stats.api import anova_lm\n",
      "\n",
      "table1 = anova_lm(lm, interX_lm)\n",
      "print table1\n",
      "\n",
      "interM_lm = ols(\"S ~ X + C(E)*C(M)\", df=salary_table).fit()\n",
      "print interM_lm.summary()\n",
      "\n",
      "table2 = anova_lm(lm, interM_lm)\n",
      "print table2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   df_resid              ssr  df_diff         ss_diff         F    Pr(>F)\n",
        "0        41  43280719.492876        0             NaN       NaN       NaN\n",
        "1        39  39410679.807560        2  3870039.685316  1.914856  0.160964\n",
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:                      S   R-squared:                       0.999\n",
        "Model:                            OLS   Adj. R-squared:                  0.999\n",
        "Method:                 Least Squares   F-statistic:                     5517.\n",
        "Date:                Sun, 26 Aug 2012   Prob (F-statistic):           1.67e-55\n",
        "Time:                        20:52:28   Log-Likelihood:                -298.74\n",
        "No. Observations:                  46   AIC:                             611.5\n",
        "Df Residuals:                      39   BIC:                             624.3\n",
        "Df Model:                           6                                         \n",
        "=======================================================================================\n",
        "                          coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "---------------------------------------------------------------------------------------\n",
        "Intercept            9472.6854     80.344    117.902      0.000      9310.175  9635.196\n",
        "C(E)[T.2]            1381.6706     77.319     17.870      0.000      1225.279  1538.063\n",
        "C(E)[T.3]            1730.7483    105.334     16.431      0.000      1517.690  1943.806\n",
        "C(M)[T.1]            3981.3769    101.175     39.351      0.000      3776.732  4186.022\n",
        "C(E)[T.2]:C(M)[T.1]  4902.5231    131.359     37.322      0.000      4636.825  5168.222\n",
        "C(E)[T.3]:C(M)[T.1]  3066.0351    149.330     20.532      0.000      2763.986  3368.084\n",
        "X                     496.9870      5.566     89.283      0.000       485.728   508.246\n",
        "==============================================================================\n",
        "Omnibus:                       74.761   Durbin-Watson:                   2.244\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1037.873\n",
        "Skew:                          -4.103   Prob(JB):                    4.25e-226\n",
        "Kurtosis:                      24.776   Cond. No.                         79.0\n",
        "==============================================================================\n",
        "   df_resid              ssr  df_diff          ss_diff           F  Pr(>F)\n",
        "0        41  43280719.492876        0              NaN         NaN     NaN\n",
        "1        39   1178167.864864        2  42102551.628012  696.844466       0\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The design matrix as a DataFrame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "interM_lm.model._data._orig_exog[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>Intercept</th>\n",
        "      <th>C(E)[T.2]</th>\n",
        "      <th>C(E)[T.3]</th>\n",
        "      <th>C(M)[T.1]</th>\n",
        "      <th>C(E)[T.2]:C(M)[T.1]</th>\n",
        "      <th>C(E)[T.3]:C(M)[T.1]</th>\n",
        "      <th>X</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>3</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>4</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "   Intercept  C(E)[T.2]  C(E)[T.3]  C(M)[T.1]  C(E)[T.2]:C(M)[T.1]  C(E)[T.3]:C(M)[T.1]  X\n",
        "0          1          0          0          1                    0                    0  1\n",
        "1          1          0          1          0                    0                    0  1\n",
        "2          1          0          1          1                    0                    1  1\n",
        "3          1          1          0          0                    0                    0  1\n",
        "4          1          0          1          0                    0                    0  1"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The design matrix as an ndarray"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "interM_lm.model.exog\n",
      "interM_lm.model.exog_names"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "['Intercept',\n",
        " 'C(E)[T.2]',\n",
        " 'C(E)[T.3]',\n",
        " 'C(M)[T.1]',\n",
        " 'C(E)[T.2]:C(M)[T.1]',\n",
        " 'C(E)[T.3]:C(M)[T.1]',\n",
        " 'X']"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "infl = interM_lm.get_influence()\n",
      "resid = infl.resid_studentized_internal\n",
      "plt.figure(figsize=(6,6))\n",
      "for values, group in factor_groups:\n",
      "    i,j = values\n",
      "    idx = group.index\n",
      "    plt.scatter(X[idx], resid[idx], marker=symbols[j], color=colors[i-1],\n",
      "            s=144, edgecolors='black')\n",
      "plt.xlabel('X');\n",
      "plt.ylabel('standardized resids');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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LPc5lzR8AkgB85wM8WQSYH5CSAxhWGfDvO//ttOWgCxcuICMjw6GlK6WWLFmG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\n"
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looks like one observation is an outlier."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "drop_idx = abs(resid).argmax()\n",
      "print drop_idx  # zero-based index\n",
      "idx = salary_table.index.drop(drop_idx)\n",
      "\n",
      "lm32 = ols('S ~ C(E) + X + C(M)', df=salary_table, subset=idx).fit()\n",
      "\n",
      "print lm32.summary()\n",
      "\n",
      "interX_lm32 = ols('S ~ C(E) * X + C(M)', df=salary_table, subset=idx).fit()\n",
      "\n",
      "print interX_lm32.summary()\n",
      "\n",
      "table3 = anova_lm(lm32, interX_lm32)\n",
      "print table3\n",
      "\n",
      "interM_lm32 = ols('S ~ X + C(E) * C(M)', df=salary_table, subset=idx).fit()\n",
      "\n",
      "table4 = anova_lm(lm32, interM_lm32)\n",
      "print table4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "32\n",
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:                      S   R-squared:                       0.955\n",
        "Model:                            OLS   Adj. R-squared:                  0.950\n",
        "Method:                 Least Squares   F-statistic:                     211.7\n",
        "Date:                Sun, 26 Aug 2012   Prob (F-statistic):           2.45e-26\n",
        "Time:                        20:52:29   Log-Likelihood:                -373.79\n",
        "No. Observations:                  45   AIC:                             757.6\n",
        "Df Residuals:                      40   BIC:                             766.6\n",
        "Df Model:                           4                                         \n",
        "==============================================================================\n",
        "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------\n",
        "Intercept   8044.7518    392.781     20.482      0.000      7250.911  8838.592\n",
        "C(E)[T.2]   3129.5286    370.470      8.447      0.000      2380.780  3878.277\n",
        "C(E)[T.3]   2999.4451    416.712      7.198      0.000      2157.238  3841.652\n",
        "C(M)[T.1]   6866.9856    323.991     21.195      0.000      6212.175  7521.796\n",
        "X            545.7855     30.912     17.656      0.000       483.311   608.260\n",
        "==============================================================================\n",
        "Omnibus:                        2.511   Durbin-Watson:                   2.265\n",
        "Prob(Omnibus):                  0.285   Jarque-Bera (JB):                1.400\n",
        "Skew:                          -0.044   Prob(JB):                        0.496\n",
        "Kurtosis:                       2.140   Cond. No.                         33.1\n",
        "==============================================================================\n",
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:                      S   R-squared:                       0.959\n",
        "Model:                            OLS   Adj. R-squared:                  0.952\n",
        "Method:                 Least Squares   F-statistic:                     147.7\n",
        "Date:                Sun, 26 Aug 2012   Prob (F-statistic):           8.97e-25\n",
        "Time:                        20:52:29   Log-Likelihood:                -371.70\n",
        "No. Observations:                  45   AIC:                             757.4\n",
        "Df Residuals:                      38   BIC:                             770.0\n",
        "Df Model:                           6                                         \n",
        "===============================================================================\n",
        "                  coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "-------------------------------------------------------------------------------\n",
        "Intercept    7266.0887    558.872     13.001      0.000      6134.711  8397.466\n",
        "C(E)[T.2]    4162.0846    685.728      6.070      0.000      2773.900  5550.269\n",
        "C(E)[T.3]    3940.4359    696.067      5.661      0.000      2531.322  5349.549\n",
        "C(M)[T.1]    7088.6387    345.587     20.512      0.000      6389.035  7788.243\n",
        "X             631.6892     53.950     11.709      0.000       522.473   740.905\n",
        "C(E)[T.2]:X  -125.5009     70.744     -1.774      0.084      -268.714    17.712\n",
        "C(E)[T.3]:X  -139.8410     90.728     -1.541      0.132      -323.511    43.829\n",
        "==============================================================================\n",
        "Omnibus:                        0.617   Durbin-Watson:                   2.194\n",
        "Prob(Omnibus):                  0.734   Jarque-Bera (JB):                0.728\n",
        "Skew:                           0.162   Prob(JB):                        0.695\n",
        "Kurtosis:                       2.468   Cond. No.                         68.7\n",
        "=============================================================================="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   df_resid              ssr  df_diff         ss_diff         F    Pr(>F)\n",
        "0        40  43209096.482552        0             NaN       NaN       NaN\n",
        "1        38  39374237.269069        2  3834859.213482  1.850508  0.171042\n",
        "   df_resid              ssr  df_diff          ss_diff            F  Pr(>F)\n",
        "0        40  43209096.482552        0              NaN          NaN     NaN\n",
        "1        38    171188.119937        2  43037908.362615  4776.734853       0\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      " Replot the residuals"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "try:\n",
      "    resid = interM_lm32.get_influence().summary_frame()['standard_resid']\n",
      "except:\n",
      "    resid = interM_lm32.get_influence().summary_frame()['standard_resid']\n",
      "\n",
      "plt.figure(figsize=(6,6))\n",
      "for values, group in factor_groups:\n",
      "    i,j = values\n",
      "    idx = group.index\n",
      "    plt.scatter(X[idx], resid[idx], marker=symbols[j], color=colors[i-1],\n",
      "            s=144, edgecolors='black')\n",
      "plt.xlabel('X[~[32]]');\n",
      "plt.ylabel('standardized resids');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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z+t7TdHqkk4SF8HoBri7AmOK/eZd2xFBcXFzBn6Ojo4mOjnZgVaW7003sLGXEna6ia9eu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\n"
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      " Plot the fitted values"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lm_final = ols('S ~ X + C(E)*C(M)', df = salary_table.drop([drop_idx])).fit()\n",
      "mf = lm_final.model._data._orig_exog\n",
      "lstyle = ['-','--']\n",
      "\n",
      "plt.figure(figsize=(6,6))\n",
      "for values, group in factor_groups:\n",
      "    i,j = values\n",
      "    idx = group.index\n",
      "    plt.scatter(X[idx], S[idx], marker=symbols[j], color=colors[i-1],\n",
      "                s=144, edgecolors='black')\n",
      "    # drop NA because there is no idx 32 in the final model\n",
      "    plt.plot(mf.X[idx].dropna(), lm_final.fittedvalues[idx].dropna(),\n",
      "            ls=lstyle[j], color=colors[i-1])\n",
      "plt.xlabel('Experience');\n",
      "plt.ylabel('Salary');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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Fh4erGjVqKIPBoJRSqnnz5io0NFQppVTXrl3Vxo0blVJKfffdd2ro0KFKKaWC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\nREQCQkEREZGA+D9H9ZBbVRwusgAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "From our first look at the data, the difference between Master's and PhD in the management group is different than in the non-management group. This is an interaction between the two qualitative variables management,M and education,E. We can visualize this by first removing the effect of experience, then plotting the means within each of the 6 groups using interaction.plot."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "U = S - X * interX_lm32.params['X']\n",
      "\n",
      "plt.figure(figsize=(6,6))\n",
      "interaction_plot(E, M, U, colors=['red','blue'], markers=['^','D'],\n",
      "        markersize=10, ax=plt.gca())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "png": 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/YMECJk6cyIkTJxg5ciTDhw8HYMqUKYwbN46IiAg6dOhASkpK/V4BH3H0qLXk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\nfrxa60LEW61ZvhyWLiV95Ei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       "prompt_number": 21,
       "text": [
        "<matplotlib.figure.Figure at 0x5244450>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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/YMECJk6cyIkTJxg5ciTDhw8HYMqUKYwbN46IiAg6dOhASkpK/V4BH3H0qLXk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\nwBAREZf8f1UWn9gr1HMzAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Minority Employment Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "try:\n",
      "    minority_table = pandas.read_table('minority.table')\n",
      "except:  # don't have data already\n",
      "    url = 'http://stats191.stanford.edu/data/minority.table'\n",
      "    minority_table = pandas.read_table(url)\n",
      "\n",
      "factor_group = minority_table.groupby(['ETHN'])\n",
      "\n",
      "plt.figure(figsize=(6,6))\n",
      "colors = ['purple', 'green']\n",
      "markers = ['o', 'v']\n",
      "for factor, group in factor_group:\n",
      "    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
      "                marker=markers[factor], s=12**2)\n",
      "plt.xlabel('TEST');\n",
      "plt.ylabel('JPERF');\n",
      "\n",
      "min_lm = ols('JPERF ~ TEST', df=minority_table).fit()\n",
      "print min_lm.summary()\n",
      "\n",
      "plt.figure(figsize=(6,6));\n",
      "for factor, group in factor_group:\n",
      "    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
      "                marker=markers[factor], s=12**2)\n",
      "\n",
      "plt.xlabel('TEST')\n",
      "plt.ylabel('JPERF')\n",
      "abline_plot(model_results = min_lm, ax=plt.gca());\n",
      "\n",
      "min_lm2 = ols('JPERF ~ TEST + TEST:ETHN',\n",
      "        df=minority_table).fit()\n",
      "\n",
      "print min_lm2.summary()\n",
      "\n",
      "plt.figure(figsize=(6,6));\n",
      "for factor, group in factor_group:\n",
      "    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
      "                marker=markers[factor], s=12**2)\n",
      "\n",
      "abline_plot(intercept = min_lm2.params['Intercept'],\n",
      "                 slope = min_lm2.params['TEST'], ax=plt.gca(), color='purple');\n",
      "abline_plot(intercept = min_lm2.params['Intercept'],\n",
      "        slope = min_lm2.params['TEST'] + min_lm2.params['TEST:ETHN'],\n",
      "        ax=plt.gca(), color='green');\n",
      "\n",
      "\n",
      "min_lm3 = ols('JPERF ~ TEST + ETHN', df = minority_table).fit()\n",
      "print min_lm3.summary()\n",
      "\n",
      "plt.figure(figsize=(6,6));\n",
      "for factor, group in factor_group:\n",
      "    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
      "                marker=markers[factor], s=12**2)\n",
      "\n",
      "abline_plot(intercept = min_lm3.params['Intercept'],\n",
      "                 slope = min_lm3.params['TEST'], ax=plt.gca(), color='purple');\n",
      "abline_plot(intercept = min_lm3.params['Intercept'] + min_lm3.params['ETHN'],\n",
      "        slope = min_lm3.params['TEST'], ax=plt.gca(), color='green');\n",
      "\n",
      "\n",
      "min_lm4 = ols('JPERF ~ TEST * ETHN', df = minority_table).fit()\n",
      "print min_lm4.summary()\n",
      "\n",
      "plt.figure(figsize=(6,6));\n",
      "for factor, group in factor_group:\n",
      "    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
      "                marker=markers[factor], s=12**2)\n",
      "\n",
      "abline_plot(intercept = min_lm4.params['Intercept'],\n",
      "                 slope = min_lm4.params['TEST'], ax=plt.gca(), color='purple');\n",
      "abline_plot(intercept = min_lm4.params['Intercept'] + min_lm4.params['ETHN'],\n",
      "        slope = min_lm4.params['TEST'] + min_lm4.params['TEST:ETHN'],\n",
      "        ax=plt.gca(), color='green');\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:                  JPERF   R-squared:                       0.517\n",
        "Model:                            OLS   Adj. R-squared:                  0.490\n",
        "Method:                 Least Squares   F-statistic:                     19.25\n",
        "Date:                Sun, 26 Aug 2012   Prob (F-statistic):           0.000356\n",
        "Time:                        20:52:31   Log-Likelihood:                -36.614\n",
        "No. Observations:                  20   AIC:                             77.23\n",
        "Df Residuals:                      18   BIC:                             79.22\n",
        "Df Model:                           1                                         \n",
        "==============================================================================\n",
        "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------\n",
        "Intercept      1.0350      0.868      1.192      0.249        -0.789     2.859\n",
        "TEST           2.3605      0.538      4.387      0.000         1.230     3.491\n",
        "==============================================================================\n",
        "Omnibus:                        0.324   Durbin-Watson:                   2.896\n",
        "Prob(Omnibus):                  0.850   Jarque-Bera (JB):                0.483\n",
        "Skew:                          -0.186   Prob(JB):                        0.785\n",
        "Kurtosis:                       2.336   Cond. No.                         5.26\n",
        "==============================================================================\n",
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:                  JPERF   R-squared:                       0.632\n",
        "Model:                            OLS   Adj. R-squared:                  0.589\n",
        "Method:                 Least Squares   F-statistic:                     14.59\n",
        "Date:                Sun, 26 Aug 2012   Prob (F-statistic):           0.000204\n",
        "Time:                        20:52:31   Log-Likelihood:                -33.891\n",
        "No. Observations:                  20   AIC:                             73.78\n",
        "Df Residuals:                      17   BIC:                             76.77\n",
        "Df Model:                           2                                         \n",
        "==============================================================================\n",
        "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------\n",
        "Intercept      1.1211      0.780      1.437      0.169        -0.525     2.768\n",
        "TEST           1.8276      0.536      3.412      0.003         0.698     2.958\n",
        "TEST:ETHN      0.9161      0.397      2.306      0.034         0.078     1.754\n",
        "==============================================================================\n",
        "Omnibus:                        0.388   Durbin-Watson:                   3.008\n",
        "Prob(Omnibus):                  0.823   Jarque-Bera (JB):                0.514\n",
        "Skew:                           0.050   Prob(JB):                        0.773\n",
        "Kurtosis:                       2.221   Cond. No.                         5.96\n",
        "=============================================================================="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:                  JPERF   R-squared:                       0.572\n",
        "Model:                            OLS   Adj. R-squared:                  0.522\n",
        "Method:                 Least Squares   F-statistic:                     11.38\n",
        "Date:                Sun, 26 Aug 2012   Prob (F-statistic):           0.000731\n",
        "Time:                        20:52:31   Log-Likelihood:                -35.390\n",
        "No. Observations:                  20   AIC:                             76.78\n",
        "Df Residuals:                      17   BIC:                             79.77\n",
        "Df Model:                           2                                         \n",
        "==============================================================================\n",
        "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------\n",
        "Intercept      0.6120      0.887      0.690      0.500        -1.260     2.483\n",
        "TEST           2.2988      0.522      4.400      0.000         1.197     3.401\n",
        "ETHN           1.0276      0.691      1.487      0.155        -0.430     2.485\n",
        "==============================================================================\n",
        "Omnibus:                        0.251   Durbin-Watson:                   3.028\n",
        "Prob(Omnibus):                  0.882   Jarque-Bera (JB):                0.437\n",
        "Skew:                          -0.059   Prob(JB):                        0.804\n",
        "Kurtosis:                       2.286   Cond. No.                         5.72\n",
        "=============================================================================="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:                  JPERF   R-squared:                       0.664\n",
        "Model:                            OLS   Adj. R-squared:                  0.601\n",
        "Method:                 Least Squares   F-statistic:                     10.55\n",
        "Date:                Sun, 26 Aug 2012   Prob (F-statistic):           0.000451\n",
        "Time:                        20:52:31   Log-Likelihood:                -32.971\n",
        "No. Observations:                  20   AIC:                             73.94\n",
        "Df Residuals:                      16   BIC:                             77.92\n",
        "Df Model:                           3                                         \n",
        "==============================================================================\n",
        "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------\n",
        "Intercept      2.0103      1.050      1.914      0.074        -0.216     4.236\n",
        "TEST           1.3134      0.670      1.959      0.068        -0.108     2.735\n",
        "ETHN          -1.9132      1.540     -1.242      0.232        -5.179     1.352\n",
        "TEST:ETHN      1.9975      0.954      2.093      0.053        -0.026     4.021\n",
        "==============================================================================\n",
        "Omnibus:                        3.377   Durbin-Watson:                   3.015\n",
        "Prob(Omnibus):                  0.185   Jarque-Bera (JB):                1.330\n",
        "Skew:                           0.120   Prob(JB):                        0.514\n",
        "Kurtosis:                       1.760   Cond. No.                         13.8\n",
        "=============================================================================="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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      },
      {
       "output_type": "display_data",
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jS969pNIQtzvtpNVNo/dT2m5TgiM2/7VKUFTUy4/l3v28eXDRRfDmm+aiKivU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\n/q234OKLw12NiEQD9fCj0MS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      },
      {
       "output_type": "display_data",
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zOtFzEQ59cQi3Em5h1MZRaOfRrrGlkgagTptopeei5xgePRw9bXri92G/60xg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\nDn4o/QHpq9MBAPeM9/Dd698hel003rrxFsJXu74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      },
      {
       "output_type": "display_data",
       "png": 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lw59/MCUyXTwdq/augshU9b3PiXbQY+yEaJGSUSLuRByyL2ejMLgQ00ZPYzvS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\n3zTYudlhxRcrWHlKLf50PPjg4835b+r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      },
      {
       "output_type": "display_data",
       "png": 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KAX8NQJf6XfBj3x8r3CbjZQacf3VWaIrExtQGj798DGO+saqj6o28+3mIXhmN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\nCGEQGdpCCGEQGdpCCGGQfwE2MwDshNF7/gAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# is there any effect of ETHN on slope or intercept?\n",
      "table5 = anova_lm(min_lm, min_lm4)\n",
      "print table5\n",
      "\n",
      "# is there any effect of ETHN on intercept\n",
      "table6 = anova_lm(min_lm, min_lm3)\n",
      "print table6\n",
      "\n",
      "# is there any effect of ETHN on slope\n",
      "table7 = anova_lm(min_lm, min_lm2)\n",
      "print table7\n",
      "\n",
      "# is it just the slope or both?\n",
      "table8 = anova_lm(min_lm2, min_lm4)\n",
      "print table8"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   df_resid        ssr  df_diff    ss_diff         F    Pr(>F)\n",
        "0        18  45.568297        0        NaN       NaN       NaN\n",
        "1        16  31.655473        2  13.912824  3.516061  0.054236\n",
        "   df_resid        ssr  df_diff   ss_diff         F    Pr(>F)\n",
        "0        18  45.568297        0       NaN       NaN       NaN\n",
        "1        17  40.321546        1  5.246751  2.212087  0.155246\n",
        "   df_resid        ssr  df_diff    ss_diff         F    Pr(>F)\n",
        "0        18  45.568297        0        NaN       NaN       NaN\n",
        "1        17  34.707653        1  10.860644  5.319603  0.033949\n",
        "   df_resid        ssr  df_diff  ss_diff         F    Pr(>F)\n",
        "0        17  34.707653        0      NaN       NaN       NaN\n",
        "1        16  31.655473        1  3.05218  1.542699  0.232115\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## One-way ANOVA"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "try:\n",
      "    rehab_table = pandas.read_csv('rehab.table')\n",
      "except:\n",
      "    url = 'http://stats191.stanford.edu/data/rehab.csv'\n",
      "    rehab_table = pandas.read_table(url, delimiter=\",\")\n",
      "    rehab_table.to_csv('rehab.table')\n",
      "\n",
      "plt.figure(figsize=(6,6))\n",
      "rehab_table.boxplot('Time', 'Fitness', ax=plt.gca())\n",
      "\n",
      "rehab_lm = ols('Time ~ C(Fitness)', df=rehab_table).fit()\n",
      "table9 = anova_lm(rehab_lm)\n",
      "print table9\n",
      "\n",
      "print rehab_lm.model._data._orig_exog\n",
      "\n",
      "print rehab_lm.summary()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "            df  sum_sq     mean_sq          F    PR(>F)\n",
        "C(Fitness)   2     672  336.000000  16.961538  0.000041\n",
        "Residual    21     416   19.809524        NaN       NaN\n",
        "    Intercept  C(Fitness)[T.2]  C(Fitness)[T.3]\n",
        "0           1                0                0\n",
        "1           1                0                0\n",
        "2           1                0                0\n",
        "3           1                0                0\n",
        "4           1                0                0\n",
        "5           1                0                0\n",
        "6           1                0                0\n",
        "7           1                0                0\n",
        "8           1                1                0\n",
        "9           1                1                0\n",
        "10          1                1                0\n",
        "11          1                1                0\n",
        "12          1                1                0\n",
        "13          1                1                0\n",
        "14          1                1                0\n",
        "15          1                1                0\n",
        "16          1                1                0\n",
        "17          1                1                0\n",
        "18          1                0                1\n",
        "19          1                0                1\n",
        "20          1                0                1\n",
        "21          1                0                1\n",
        "22          1                0                1\n",
        "23          1                0                1\n",
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:                   Time   R-squared:                       0.618\n",
        "Model:                            OLS   Adj. R-squared:                  0.581\n",
        "Method:                 Least Squares   F-statistic:                     16.96\n",
        "Date:                Sun, 26 Aug 2012   Prob (F-statistic):           4.13e-05\n",
        "Time:                        20:52:32   Log-Likelihood:                -68.286\n",
        "No. Observations:                  24   AIC:                             142.6\n",
        "Df Residuals:                      21   BIC:                             146.1\n",
        "Df Model:                           2                                         \n",
        "===================================================================================\n",
        "                      coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "-----------------------------------------------------------------------------------\n",
        "Intercept          38.0000      1.574     24.149      0.000        34.728    41.272\n",
        "C(Fitness)[T.2]    -6.0000      2.111     -2.842      0.010       -10.390    -1.610\n",
        "C(Fitness)[T.3]   -14.0000      2.404     -5.824      0.000       -18.999    -9.001\n",
        "==============================================================================\n",
        "Omnibus:                        0.163   Durbin-Watson:                   2.209\n",
        "Prob(Omnibus):                  0.922   Jarque-Bera (JB):                0.211\n",
        "Skew:                          -0.163   Prob(JB):                        0.900\n",
        "Kurtosis:                       2.675   Cond. No.                         3.80\n",
        "==============================================================================\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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AN/yUc/uplpqQuQNAAmK4u0CuZxf6aQ96yXAHACuRuQPwDT/l3H6qpSZk7gCQ\ngBjuLpDr2YV+2oNeMtwBwEpk7gB8w085t59qqQmZOwAkIIa7C+R6dqGf3jNyqg6ZXV5CMdiHkeP1\nr8MVhjsA33BkqrIQt5fiYtf7cOTjTKYeyNwB+Iafcm4/1VITMncASEAMdxfIaO1CP+1BLxnuAGAl\nMncAvuGnnNtPtdSEzB0AEhDD3QVyPbvQT3vQS4Y7AFiJzB2Ab/gp5/ZTLTUhcweABMRwd4Fczy70\n0x70kuEOAFYicwfgG37Kuf1US03I3AEgATHcXSDXswv9tAe9lJp6XQAAHMjxyWdktGvndQXukLkD\nsI7f8/JYIHMHgATEcHeBXM8u9NMmIa8L8BzDHQAsROYOwDpk7hy5A7DQpEleV+A9hrsLZLR2oZ/2\nyMoKeV2C5xjuAGAhMncAiENk7gCQgBjuLpDR2oV+2oNeMtxdKS0t9boExBD9tEdhIb2sdbivX79e\ngwcPVvfu3dWjRw/NmDFDklRZWans7Gx17txZOTk52rJlS6MU6zeJer9tRT/t8Z//0Mtah3uzZs00\nbdo0/fe//9U777yjRx55RJ988okKCwuVnZ2tNWvWaOjQoSosLGysegEA9VDrcO/QoYMCgYAkqXXr\n1uratau++uorLViwQHl5eZKkvLw8zZ8/v+Er9aFwOOx1CYgh+mmTsNcFeK7ep0KGw2ENGjRIH3/8\nsTp16qTNmzdLkowxSklJiW5Hd+yXRZkBwFK1je96fVjHjh07dPHFF2v69Olq06ZNte85jlPjIOcc\ndwDwTp1ny+zZs0cXX3yxRo0apeHDh0uS0tLSVFFRIUkqLy9Xampqw1YJADgitQ53Y4yuvvpqdevW\nTTfeeGP06xdccIGKiookSUVFRdGhDwDwh1oz9+XLl+vss89Wz549o9HLPffco379+mnEiBFat26d\n0tPTNXfuXCUnJzda0QCA2jXY2jK2u+qqq/Tyyy8rNTVVH330kdflwIX169dr9OjR+vbbb+U4jq65\n5hrdcMMNXpeFo/DDDz9o0KBB+r//+z/t3r1bv/nNb3TPPfd4XZYnGO5H6c0331Tr1q01evRohnuc\nq6ioUEVFhQKBgHbs2KE+ffpo/vz56tq1q9el4Sh8//33atWqlfbu3asBAwbogQce0IABA7wuq9Gx\n/MBRGjhwoNq1a+d1GYiBmt7P8fXXX3tcFY5Wq1atJEm7d+/Wvn37lJKS4nFF3mC4AwcIh8MqKSlR\n//79vS7O/UAoAAAEI0lEQVQFRykSiSgQCCgtLU2DBw9Wt27dvC7JEwx34Ec7duzQJZdcounTp6t1\n69Zel4Oj1KRJE5WWlmrDhg164403EnaFSIY7oJ/ez3HFFVdwaq8l2rZtq2HDhun999/3uhRPMNyR\n8A73fg7En40bN0ZX99y1a5eWLFmiYDDocVXeYLgfpZEjR+rMM8/UmjVrdOKJJ+qJJ57wuiQcpbfe\nektPPfWUiouLFQwGFQwGtXjxYq/LwlEoLy/XkCFDFAgE1L9/f+Xm5mro0KFel+UJToUEAAtx5A4A\nFmK4A4CFGO4AYCGGOwBYiOEOABZiuCOuhMNhtWzZUr1795YkpaenV/v6/lMZe/furfXr1+vSSy+V\nJH3wwQdatGhRo9Q4e/ZsTZ48WZI0bdo0nXTSSbr++usb5baB/er1MXuAn5x66qlavXq1pOqf1Xvq\nqaeqpKSk2nVfeOEFSVJJSYlWrVql8847r8HrO7Cm8ePHKyUlJWHfJQnvcOSOuFbbRzyGw2FlZmZq\nz549uuOOO/T8888rGAxq7ty5Kigo0FVXXaXBgwfrlFNO0cyZM6M/99RTT6l///4KBoO69tprFYlE\ntG/fPuXn5yszM1M9e/bU9OnTJUkzZsxQ9+7d1atXL11++eWSpJYtW1b7rGHeSgIvcOSOuPbuu+9G\n/15WVhZ9q/mAAQP05z//WZLUrFkzTZkyRatWrdKMGTMkSQUFBVqzZo2Ki4u1bds2denSRX/84x+1\nZs0azZ07VytWrFBSUpLGjRunp59+Wt27d9fXX38dXbt/27ZtkqR7771X4XBYzZo1i35txIgR1Wqs\n6QPkgYbGcIc1TjnllGqxTDgcjv7dGFPtCNpxHA0bNkzNmjXTz3/+c6WmpqqiokKvv/66Vq1apb59\n+0qqWp8kLS1Nubm5+uKLL3TDDTdo2LBhysnJkST17NlTl19+uYYPH86CY/AVYhkkrObNm0f/npSU\npL1790qS8vLyVFJSopKSEn366ae64447lJycrA8//FBZWVn629/+pjFjxkiSXn75ZY0bN06rV6/W\naaedpn379nlyX4CDMdyREI499lht37691us4jqOhQ4fqxRdf1HfffSdJqqys1Lp167Rp0ybt3btX\nF110kaZMmaLVq1fLGKN169YpKytLhYWF2rp1q3bu3NkYdweoE7EMrFFTtr3/a4MHD1ZhYaGCwaAm\nTpx42Ot37dpVd911l3JychSJRNSsWTM9+uijatGiha688kpFIhFJUmFhofbt26dRo0Zp69atMsbo\nT3/6k4499tgGvIdA/bEqJOJKOBxWbm5uXH0o+ezZs7Vq1apqZ+QADY1YBnGladOm2rp1a/RNTH43\nbdo0FRYWqm3btl6XggTDkTsAWIgjdwCwEMMdACzEcAcACzHcAcBCDHcAsND/A43KLs+/+vqDAAAA\nAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Two-way ANOVA"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "try:\n",
      "    kidney_table = pandas.read_table('./kidney.table')\n",
      "except:\n",
      "    url = 'http://stats191.stanford.edu/data/kidney.table'\n",
      "    kidney_table = pandas.read_table(url, delimiter=\" *\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Explore the dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kidney_table.groupby(['Weight', 'Duration']).size()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "Weight  Duration\n",
        "1       1           10\n",
        "        2           10\n",
        "2       1           10\n",
        "        2           10\n",
        "3       1           10\n",
        "        2           10"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Balanced panel"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kt = kidney_table\n",
      "plt.figure(figsize=(6,6))\n",
      "interaction_plot(kt['Weight'], kt['Duration'], np.log(kt['Days']+1),\n",
      "        colors=['red', 'blue'], markers=['D','^'], ms=10, ax=plt.gca())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "png": 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06NGDY8eOAeYAismTJ3u8MJECe+UVs/B76VIoWdLuauQCGRlmI9akSfDNNxAe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\njg9+9VWzFPOjj6BFC7urEvF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       "prompt_number": 27,
       "text": [
        "<matplotlib.figure.Figure at 0x5375e90>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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06NGDY8eOAeYAismTJ3u8MJECe+UVs/B76VIoWdLuauQCGRlmI9akSfDNNxAe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\nxqBChPp6AAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You have things available in the calling namespace available in the formula evaluation namespace"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kidney_lm = ols('np.log(Days+1) ~ C(Duration) * C(Weight)', df=kt).fit()\n",
      "\n",
      "table10 = anova_lm(kidney_lm)\n",
      "\n",
      "print anova_lm(ols('np.log(Days+1) ~ C(Duration) + C(Weight)',\n",
      "                df=kt).fit(), kidney_lm)\n",
      "print anova_lm(ols('np.log(Days+1) ~ C(Duration)', df=kt).fit(),\n",
      "               ols('np.log(Days+1) ~ C(Duration) + C(Weight, Sum)',\n",
      "                   df=kt).fit())\n",
      "print anova_lm(ols('np.log(Days+1) ~ C(Weight)', df=kt).fit(),\n",
      "               ols('np.log(Days+1) ~ C(Duration) + C(Weight, Sum)',\n",
      "                   df=kt).fit())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   df_resid        ssr  df_diff   ss_diff        F    Pr(>F)\n",
        "0        56  29.624856        0       NaN      NaN       NaN\n",
        "1        54  28.989198        2  0.635658  0.59204  0.556748\n",
        "   df_resid        ssr  df_diff    ss_diff          F    Pr(>F)\n",
        "0        58  46.596147        0        NaN        NaN       NaN\n",
        "1        56  29.624856        2  16.971291  16.040454  0.000003\n",
        "   df_resid        ssr  df_diff   ss_diff         F   Pr(>F)\n",
        "0        57  31.964549        0       NaN       NaN      NaN\n",
        "1        56  29.624856        1  2.339693  4.422732  0.03997\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Sum of squares\n",
      "\n",
      " Illustrates the use of different types of sums of squares (I,II,II)\n",
      " and how the Sum contrast can be used to produce the same output between\n",
      " the 3.\n",
      "\n",
      " Types I and II are equivalent under a balanced design.\n",
      "\n",
      " Don't use Type III with non-orthogonal contrast - ie., Treatment"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sum_lm = ols('np.log(Days+1) ~ C(Duration, Sum) * C(Weight, Sum)',\n",
      "            df=kt).fit()\n",
      "\n",
      "print anova_lm(sum_lm)\n",
      "print anova_lm(sum_lm, typ=2)\n",
      "print anova_lm(sum_lm, typ=3)\n",
      "\n",
      "nosum_lm = ols('np.log(Days+1) ~ C(Duration, Treatment) * C(Weight, Treatment)',\n",
      "            df=kt).fit()\n",
      "print anova_lm(nosum_lm)\n",
      "print anova_lm(nosum_lm, typ=2)\n",
      "print anova_lm(nosum_lm, typ=3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                                 df     sum_sq   mean_sq          F    PR(>F)\n",
        "C(Duration, Sum)                  1   2.339693  2.339693   4.358293  0.041562\n",
        "C(Weight, Sum)                    2  16.971291  8.485645  15.806745  0.000004\n",
        "C(Duration, Sum):C(Weight, Sum)   2   0.635658  0.317829   0.592040  0.556748\n",
        "Residual                         54  28.989198  0.536837        NaN       NaN\n",
        "                                    sum_sq  df          F    PR(>F)\n",
        "C(Duration, Sum)                  2.339693   1   4.358293  0.041562\n",
        "C(Weight, Sum)                   16.971291   2  15.806745  0.000004\n",
        "C(Duration, Sum):C(Weight, Sum)   0.635658   2   0.592040  0.556748\n",
        "Residual                         28.989198  54        NaN       NaN\n",
        "                                     sum_sq  df           F    PR(>F)\n",
        "Intercept                        156.301830   1  291.153237  0.000000\n",
        "C(Duration, Sum)                   2.339693   1    4.358293  0.041562\n",
        "C(Weight, Sum)                    16.971291   2   15.806745  0.000004\n",
        "C(Duration, Sum):C(Weight, Sum)    0.635658   2    0.592040  0.556748\n",
        "Residual                          28.989198  54         NaN       NaN\n",
        "                                             df     sum_sq   mean_sq          F    PR(>F)\n",
        "C(Duration, Treatment)                        1   2.339693  2.339693   4.358293  0.041562\n",
        "C(Weight, Treatment)                          2  16.971291  8.485645  15.806745  0.000004\n",
        "C(Duration, Treatment):C(Weight, Treatment)   2   0.635658  0.317829   0.592040  0.556748\n",
        "Residual                                     54  28.989198  0.536837        NaN       NaN\n",
        "                                                sum_sq  df          F    PR(>F)\n",
        "C(Duration, Treatment)                        2.339693   1   4.358293  0.041562\n",
        "C(Weight, Treatment)                         16.971291   2  15.806745  0.000004\n",
        "C(Duration, Treatment):C(Weight, Treatment)   0.635658   2   0.592040  0.556748\n",
        "Residual                                     28.989198  54        NaN       NaN\n",
        "                                                sum_sq  df          F    PR(>F)\n",
        "Intercept                                    10.427596   1  19.424139  0.000050\n",
        "C(Duration, Treatment)                        0.054293   1   0.101134  0.751699\n",
        "C(Weight, Treatment)                         11.703387   2  10.900317  0.000106\n",
        "C(Duration, Treatment):C(Weight, Treatment)   0.635658   2   0.592040  0.556748\n",
        "Residual                                     28.989198  54        NaN       NaN"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 29
    }
   ],
   "metadata": {}
  }
 ]
}