assignment1/assignment1_clean.ipynb

{
 "metadata": {
  "name": "assignment1_clean"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Data Analysis Assignment"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "from statsmodels.formula.api import ols\n",
      "from statsmodels.graphics.gofplots import qqplot"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 71
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load data and perform some cleanings. Of the 2,500 observations, we identified two that contain missing values, which were simply removed, because we expect that such few occurrences will not affect the analysis results significantly. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loansData = pd.read_csv('https://spark-public.s3.amazonaws.com/dataanalysis/loansData.csv')\n",
      "\n",
      "loansData = loansData.reset_index(drop=True)\n",
      "\n",
      "loansData.columns = map(lambda x: x.replace('.', '_').lower(), loansData.columns)\n",
      "\n",
      "loansData['amount_requested'] = loansData['amount_requested'].astype(float)\n",
      "loansData['interest_rate'] = loansData['interest_rate'].apply(lambda x: float(x.strip('%')))\n",
      "loansData['debt_to_income_ratio'] = loansData['debt_to_income_ratio'].apply(lambda x: float(x.strip('%')) / 100)\n",
      "loansData['loan_length'] = loansData['loan_length'].apply(lambda x: float(x.strip('months').strip()))\n",
      "\n",
      "loansData = loansData.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loansData['interest_rate'].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 94,
       "text": [
        "count    2498.000000\n",
        "mean       13.070368\n",
        "std         4.178007\n",
        "min         5.420000\n",
        "25%        10.160000\n",
        "50%        13.110000\n",
        "75%        15.800000\n",
        "max        24.890000"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The interest rates are evenly distributed, with near-bell-shape distribution centered at around 13%. Based on this observation we identified that no transformation is required on the interest rate."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loansData.interest_rate.plot(kind='kde');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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++SZXX321Yz9hHaVkDoUuRo2ScQrhPaYViuDgYDIzM0lJSSE2Npabb76ZmJgY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\nVHL6yKYoVwAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We first check the correlation between all numerical variables in the data. We exclude the amount of granted loan from further analysis because it is highly correlated with the amount of requested loan, which, common sense says, might affect the interest rate of the loan. Our correlation table shows that it is indeed the case. We further identified other variables that could be useful for further analysis based on their relative magnitudes of correlation with respect to the interest rate."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loansData.corr()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>amount_requested</th>\n",
        "      <th>amount_funded_by_investors</th>\n",
        "      <th>interest_rate</th>\n",
        "      <th>loan_length</th>\n",
        "      <th>debt_to_income_ratio</th>\n",
        "      <th>monthly_income</th>\n",
        "      <th>open_credit_lines</th>\n",
        "      <th>revolving_credit_balance</th>\n",
        "      <th>inquiries_in_the_last_6_months</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>amount_requested</strong></td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.969841</td>\n",
        "      <td> 0.331831</td>\n",
        "      <td> 0.412298</td>\n",
        "      <td> 0.081288</td>\n",
        "      <td> 0.391179</td>\n",
        "      <td> 0.195942</td>\n",
        "      <td> 0.293365</td>\n",
        "      <td>-0.029561</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>amount_funded_by_investors</strong></td>\n",
        "      <td> 0.969841</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.336830</td>\n",
        "      <td> 0.406218</td>\n",
        "      <td> 0.093679</td>\n",
        "      <td> 0.374377</td>\n",
        "      <td> 0.185826</td>\n",
        "      <td> 0.261892</td>\n",
        "      <td>-0.067757</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>interest_rate</strong></td>\n",
        "      <td> 0.331831</td>\n",
        "      <td> 0.336830</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.423506</td>\n",
        "      <td> 0.172203</td>\n",
        "      <td> 0.012921</td>\n",
        "      <td> 0.090307</td>\n",
        "      <td> 0.061109</td>\n",
        "      <td> 0.164646</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>loan_length</strong></td>\n",
        "      <td> 0.412298</td>\n",
        "      <td> 0.406218</td>\n",
        "      <td> 0.423506</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.024991</td>\n",
        "      <td> 0.074545</td>\n",
        "      <td> 0.040890</td>\n",
        "      <td> 0.055436</td>\n",
        "      <td> 0.023842</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>debt_to_income_ratio</strong></td>\n",
        "      <td> 0.081288</td>\n",
        "      <td> 0.093679</td>\n",
        "      <td> 0.172203</td>\n",
        "      <td> 0.024991</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.162345</td>\n",
        "      <td> 0.370850</td>\n",
        "      <td> 0.189221</td>\n",
        "      <td> 0.011979</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>monthly_income</strong></td>\n",
        "      <td> 0.391179</td>\n",
        "      <td> 0.374377</td>\n",
        "      <td> 0.012921</td>\n",
        "      <td> 0.074545</td>\n",
        "      <td>-0.162345</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.171396</td>\n",
        "      <td> 0.359684</td>\n",
        "      <td> 0.033948</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>open_credit_lines</strong></td>\n",
        "      <td> 0.195942</td>\n",
        "      <td> 0.185826</td>\n",
        "      <td> 0.090307</td>\n",
        "      <td> 0.040890</td>\n",
        "      <td> 0.370850</td>\n",
        "      <td> 0.171396</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.290085</td>\n",
        "      <td> 0.110736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>revolving_credit_balance</strong></td>\n",
        "      <td> 0.293365</td>\n",
        "      <td> 0.261892</td>\n",
        "      <td> 0.061109</td>\n",
        "      <td> 0.055436</td>\n",
        "      <td> 0.189221</td>\n",
        "      <td> 0.359684</td>\n",
        "      <td> 0.290085</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.012186</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>inquiries_in_the_last_6_months</strong></td>\n",
        "      <td>-0.029561</td>\n",
        "      <td>-0.067757</td>\n",
        "      <td> 0.164646</td>\n",
        "      <td> 0.023842</td>\n",
        "      <td> 0.011979</td>\n",
        "      <td> 0.033948</td>\n",
        "      <td> 0.110736</td>\n",
        "      <td> 0.012186</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 95,
       "text": [
        "                                amount_requested  amount_funded_by_investors  \\\n",
        "amount_requested                        1.000000                    0.969841   \n",
        "amount_funded_by_investors              0.969841                    1.000000   \n",
        "interest_rate                           0.331831                    0.336830   \n",
        "loan_length                             0.412298                    0.406218   \n",
        "debt_to_income_ratio                    0.081288                    0.093679   \n",
        "monthly_income                          0.391179                    0.374377   \n",
        "open_credit_lines                       0.195942                    0.185826   \n",
        "revolving_credit_balance                0.293365                    0.261892   \n",
        "inquiries_in_the_last_6_months         -0.029561                   -0.067757   \n",
        "\n",
        "                                interest_rate  loan_length  debt_to_income_ratio  \\\n",
        "amount_requested                     0.331831     0.412298              0.081288   \n",
        "amount_funded_by_investors           0.336830     0.406218              0.093679   \n",
        "interest_rate                        1.000000     0.423506              0.172203   \n",
        "loan_length                          0.423506     1.000000              0.024991   \n",
        "debt_to_income_ratio                 0.172203     0.024991              1.000000   \n",
        "monthly_income                       0.012921     0.074545             -0.162345   \n",
        "open_credit_lines                    0.090307     0.040890              0.370850   \n",
        "revolving_credit_balance             0.061109     0.055436              0.189221   \n",
        "inquiries_in_the_last_6_months       0.164646     0.023842              0.011979   \n",
        "\n",
        "                                monthly_income  open_credit_lines  \\\n",
        "amount_requested                      0.391179           0.195942   \n",
        "amount_funded_by_investors            0.374377           0.185826   \n",
        "interest_rate                         0.012921           0.090307   \n",
        "loan_length                           0.074545           0.040890   \n",
        "debt_to_income_ratio                 -0.162345           0.370850   \n",
        "monthly_income                        1.000000           0.171396   \n",
        "open_credit_lines                     0.171396           1.000000   \n",
        "revolving_credit_balance              0.359684           0.290085   \n",
        "inquiries_in_the_last_6_months        0.033948           0.110736   \n",
        "\n",
        "                                revolving_credit_balance  \\\n",
        "amount_requested                                0.293365   \n",
        "amount_funded_by_investors                      0.261892   \n",
        "interest_rate                                   0.061109   \n",
        "loan_length                                     0.055436   \n",
        "debt_to_income_ratio                            0.189221   \n",
        "monthly_income                                  0.359684   \n",
        "open_credit_lines                               0.290085   \n",
        "revolving_credit_balance                        1.000000   \n",
        "inquiries_in_the_last_6_months                  0.012186   \n",
        "\n",
        "                                inquiries_in_the_last_6_months  \n",
        "amount_requested                                     -0.029561  \n",
        "amount_funded_by_investors                           -0.067757  \n",
        "interest_rate                                         0.164646  \n",
        "loan_length                                           0.023842  \n",
        "debt_to_income_ratio                                  0.011979  \n",
        "monthly_income                                        0.033948  \n",
        "open_credit_lines                                     0.110736  \n",
        "revolving_credit_balance                              0.012186  \n",
        "inquiries_in_the_last_6_months                        1.000000  "
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some exploratory analyses were needed to determine the significance of the categorical variables. We identified that the loan length has a stronger effect on the interest rate compared to other categorical variables. We can verify this by looking at boxplots of the interest rate grouped by the loan lengths. A scatter plot between the FICO range and interest rate, grouped by loan lengths, also shows some clustering, although there are plenty of overlaps."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loansData.boxplot(column='interest_rate');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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MMeFw2Kxfvz72Wk1Njdm9e7cxxpjff//dTJ8+fVDfIyARTPmLtFVSUqK7775bklRYWKho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\n"
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "props = loansData.loan_length.value_counts().astype(float)/len(loansData.loan_length)\n",
      "loansData.boxplot(column='interest_rate', by='loan_length', widths=props);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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+WHMnIrNizd08WHMnIlVxbRl1sOeuMQaDAXq9Xu0wiKAoSpOejzmjYdhzJyKz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\n"
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grouped = loansData.groupby('loan_length')\n",
      "fr = pd.Categorical.from_array(loansData.fico_range)\n",
      "\n",
      "cols = ['Yellow', 'MediumOrchid']\n",
      "\n",
      "i = 0\n",
      "for length, group in grouped:\n",
      "    scatter(pd.Categorical.from_array(group.fico_range).labels, group.interest_rate, c=cols[i].strip().strip('\\n'), label=str(int(length)) + ' months', s=30)\n",
      "    i += 1\n",
      "    \n",
      "a = gca()\n",
      "a.set_xticks(np.unique(fr.labels))\n",
      "a.set_xticklabels(fr.levels, rotation='30')\n",
      "a.set_xlabel('FICO range', fontsize=12)\n",
      "a.set_ylabel('Interest rate', fontsize=12)\n",
      "a.legend()\n",
      "\n",
      "f = gcf()\n",
      "f.set_size_inches(12,7)\n",
      "f.suptitle('Interest rate as a function of FICO range and loan length', fontsize=15)\n",
      "f.savefig('interest.png');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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X11fWJhQKERwcXK2pGpVxc3PD+fPn5X6srKyUxlR3ikZ1zglVrKys4OHhUaF94cKFcHNz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\nSddoNOJlV3zn183WE6NZPr9Xr9fzOD6aSZMmEXHiOKVqluT2gkiLcm1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6RzqW6YNOJ99s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LSaMkrKU52iOVNQUKBxNZT3AV7d4gNEoVDAxMQElStXxoEDBzBt2jQMGDAAQ4cOxbZt25CV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\nERFx8x80OEdPnNRYbgAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We fit a regression model relating interest rate to loan length and FICO range. Although we could still improve the fit by including the numerical variables, we choose not to do it to make a simple and tractable model. We also don\u2019t consider interaction between loan length and FICO range. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lm = ols('interest_rate ~ C(loan_length) + fico_range', loansData).fit()\n",
      "lm.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table class=\"simpletable\">\n",
        "<caption>OLS Regression Results</caption>\n",
        "<tr>\n",
        "  <th>Dep. Variable:</th>      <td>interest_rate</td>  <th>  R-squared:         </th> <td>   0.731</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.727</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   175.7</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Date:</th>             <td>Mon, 18 Feb 2013</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Time:</th>                 <td>01:16:09</td>     <th>  Log-Likelihood:    </th> <td> -5476.3</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>  2498</td>      <th>  AIC:               </th> <td>1.103e+04</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Df Residuals:</th>          <td>  2459</td>      <th>  BIC:               </th> <td>1.126e+04</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Df Model:</th>              <td>    38</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>   15.2120</td> <td>    0.977</td> <td>   15.574</td> <td> 0.000</td> <td>   13.297    17.127</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(loan_length)[T.60.0]</th> <td>    4.3797</td> <td>    0.106</td> <td>   41.193</td> <td> 0.000</td> <td>    4.171     4.588</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.645-649]</th>  <td>   -0.3287</td> <td>    1.595</td> <td>   -0.206</td> <td> 0.837</td> <td>   -3.456     2.799</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.650-654]</th>  <td>   -0.0820</td> <td>    2.393</td> <td>   -0.034</td> <td> 0.973</td> <td>   -4.774     4.610</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.655-659]</th>  <td>   -0.2820</td> <td>    1.465</td> <td>   -0.192</td> <td> 0.847</td> <td>   -3.155     2.591</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.660-664]</th>  <td>    2.1943</td> <td>    0.996</td> <td>    2.202</td> <td> 0.028</td> <td>    0.240     4.148</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.665-669]</th>  <td>    1.3299</td> <td>    0.994</td> <td>    1.338</td> <td> 0.181</td> <td>   -0.619     3.279</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.670-674]</th>  <td>    0.3449</td> <td>    0.991</td> <td>    0.348</td> <td> 0.728</td> <td>   -1.599     2.288</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.675-679]</th>  <td>   -0.2280</td> <td>    0.992</td> <td>   -0.230</td> <td> 0.818</td> <td>   -2.172     1.716</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.680-684]</th>  <td>   -1.0059</td> <td>    0.992</td> <td>   -1.014</td> <td> 0.311</td> <td>   -2.952     0.940</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.685-689]</th>  <td>   -1.4203</td> <td>    0.995</td> <td>   -1.428</td> <td> 0.153</td> <td>   -3.371     0.530</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.690-694]</th>  <td>   -1.5749</td> <td>    0.994</td> <td>   -1.584</td> <td> 0.113</td> <td>   -3.525     0.375</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.695-699]</th>  <td>   -2.1815</td> <td>    0.993</td> <td>   -2.197</td> <td> 0.028</td> <td>   -4.129    -0.234</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.700-704]</th>  <td>   -2.9584</td> <td>    0.996</td> <td>   -2.971</td> <td> 0.003</td> <td>   -4.911    -1.006</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.705-709]</th>  <td>   -3.5981</td> <td>    0.995</td> <td>   -3.616</td> <td> 0.000</td> <td>   -5.549    -1.647</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.710-714]</th>  <td>   -3.7565</td> <td>    0.999</td> <td>   -3.762</td> <td> 0.000</td> <td>   -5.715    -1.798</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.715-719]</th>  <td>   -4.9249</td> <td>    1.003</td> <td>   -4.910</td> <td> 0.000</td> <td>   -6.892    -2.958</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.720-724]</th>  <td>   -5.1367</td> <td>    0.998</td> <td>   -5.146</td> <td> 0.000</td> <td>   -7.094    -3.179</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.725-729]</th>  <td>   -5.4909</td> <td>    1.003</td> <td>   -5.476</td> <td> 0.000</td> <td>   -7.457    -3.525</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.730-734]</th>  <td>   -6.2809</td> <td>    1.003</td> <td>   -6.264</td> <td> 0.000</td> <td>   -8.247    -4.315</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.735-739]</th>  <td>   -6.5318</td> <td>    1.014</td> <td>   -6.442</td> <td> 0.000</td> <td>   -8.520    -4.544</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.740-744]</th>  <td>   -6.6940</td> <td>    1.022</td> <td>   -6.549</td> <td> 0.000</td> <td>   -8.698    -4.690</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.745-749]</th>  <td>   -6.8514</td> <td>    1.022</td> <td>   -6.706</td> <td> 0.000</td> <td>   -8.855    -4.848</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.750-754]</th>  <td>   -7.4623</td> <td>    1.016</td> <td>   -7.344</td> <td> 0.000</td> <td>   -9.455    -5.470</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.755-759]</th>  <td>   -7.4537</td> <td>    1.029</td> <td>   -7.244</td> <td> 0.000</td> <td>   -9.471    -5.436</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.760-764]</th>  <td>   -7.3461</td> <td>    1.029</td> <td>   -7.141</td> <td> 0.000</td> <td>   -9.363    -5.329</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.765-769]</th>  <td>   -8.0370</td> <td>    1.043</td> <td>   -7.709</td> <td> 0.000</td> <td>  -10.081    -5.993</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.770-774]</th>  <td>   -8.7167</td> <td>    1.111</td> <td>   -7.844</td> <td> 0.000</td> <td>  -10.896    -6.538</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.775-779]</th>  <td>   -7.4633</td> <td>    1.082</td> <td>   -6.895</td> <td> 0.000</td> <td>   -9.586    -5.341</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.780-784]</th>  <td>   -8.7177</td> <td>    1.061</td> <td>   -8.218</td> <td> 0.000</td> <td>  -10.798    -6.638</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.785-789]</th>  <td>   -8.0972</td> <td>    1.098</td> <td>   -7.372</td> <td> 0.000</td> <td>  -10.251    -5.943</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.790-794]</th>  <td>   -8.3085</td> <td>    1.092</td> <td>   -7.607</td> <td> 0.000</td> <td>  -10.450    -6.167</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.795-799]</th>  <td>   -8.8419</td> <td>    1.150</td> <td>   -7.686</td> <td> 0.000</td> <td>  -11.098    -6.586</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.800-804]</th>  <td>   -8.6511</td> <td>    1.163</td> <td>   -7.439</td> <td> 0.000</td> <td>  -10.931    -6.371</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.805-809]</th>  <td>   -8.5820</td> <td>    1.178</td> <td>   -7.284</td> <td> 0.000</td> <td>  -10.892    -6.272</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.810-814]</th>  <td>   -6.9183</td> <td>    1.245</td> <td>   -5.556</td> <td> 0.000</td> <td>   -9.360    -4.477</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.815-819]</th>  <td>   -9.0170</td> <td>    1.323</td> <td>   -6.817</td> <td> 0.000</td> <td>  -11.611    -6.423</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.820-824]</th>  <td>   -7.3120</td> <td>    2.393</td> <td>   -3.056</td> <td> 0.002</td> <td>  -12.004    -2.620</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>fico_range[T.830-834]</th>  <td>   -7.5920</td> <td>    2.393</td> <td>   -3.173</td> <td> 0.002</td> <td>  -12.284    -2.900</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
        "  <th>Omnibus:</th>       <td>130.177</td> <th>  Durbin-Watson:     </th> <td>   1.922</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 181.842</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Skew:</th>          <td> 0.477</td>  <th>  Prob(JB):          </th> <td>3.26e-40</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Kurtosis:</th>      <td> 3.916</td>  <th>  Cond. No.          </th> <td>    146.</td>\n",
        "</tr>\n",
        "</table>"
       ],
       "output_type": "pyout",
       "prompt_number": 100,
       "text": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:          interest_rate   R-squared:                       0.731\n",
        "Model:                            OLS   Adj. R-squared:                  0.727\n",
        "Method:                 Least Squares   F-statistic:                     175.7\n",
        "Date:                Mon, 18 Feb 2013   Prob (F-statistic):               0.00\n",
        "Time:                        01:16:09   Log-Likelihood:                -5476.3\n",
        "No. Observations:                2498   AIC:                         1.103e+04\n",
        "Df Residuals:                    2459   BIC:                         1.126e+04\n",
        "Df Model:                          38                                         \n",
        "==========================================================================================\n",
        "                             coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------------------\n",
        "Intercept                 15.2120      0.977     15.574      0.000        13.297    17.127\n",
        "C(loan_length)[T.60.0]     4.3797      0.106     41.193      0.000         4.171     4.588\n",
        "fico_range[T.645-649]     -0.3287      1.595     -0.206      0.837        -3.456     2.799\n",
        "fico_range[T.650-654]     -0.0820      2.393     -0.034      0.973        -4.774     4.610\n",
        "fico_range[T.655-659]     -0.2820      1.465     -0.192      0.847        -3.155     2.591\n",
        "fico_range[T.660-664]      2.1943      0.996      2.202      0.028         0.240     4.148\n",
        "fico_range[T.665-669]      1.3299      0.994      1.338      0.181        -0.619     3.279\n",
        "fico_range[T.670-674]      0.3449      0.991      0.348      0.728        -1.599     2.288\n",
        "fico_range[T.675-679]     -0.2280      0.992     -0.230      0.818        -2.172     1.716\n",
        "fico_range[T.680-684]     -1.0059      0.992     -1.014      0.311        -2.952     0.940\n",
        "fico_range[T.685-689]     -1.4203      0.995     -1.428      0.153        -3.371     0.530\n",
        "fico_range[T.690-694]     -1.5749      0.994     -1.584      0.113        -3.525     0.375\n",
        "fico_range[T.695-699]     -2.1815      0.993     -2.197      0.028        -4.129    -0.234\n",
        "fico_range[T.700-704]     -2.9584      0.996     -2.971      0.003        -4.911    -1.006\n",
        "fico_range[T.705-709]     -3.5981      0.995     -3.616      0.000        -5.549    -1.647\n",
        "fico_range[T.710-714]     -3.7565      0.999     -3.762      0.000        -5.715    -1.798\n",
        "fico_range[T.715-719]     -4.9249      1.003     -4.910      0.000        -6.892    -2.958\n",
        "fico_range[T.720-724]     -5.1367      0.998     -5.146      0.000        -7.094    -3.179\n",
        "fico_range[T.725-729]     -5.4909      1.003     -5.476      0.000        -7.457    -3.525\n",
        "fico_range[T.730-734]     -6.2809      1.003     -6.264      0.000        -8.247    -4.315\n",
        "fico_range[T.735-739]     -6.5318      1.014     -6.442      0.000        -8.520    -4.544\n",
        "fico_range[T.740-744]     -6.6940      1.022     -6.549      0.000        -8.698    -4.690\n",
        "fico_range[T.745-749]     -6.8514      1.022     -6.706      0.000        -8.855    -4.848\n",
        "fico_range[T.750-754]     -7.4623      1.016     -7.344      0.000        -9.455    -5.470\n",
        "fico_range[T.755-759]     -7.4537      1.029     -7.244      0.000        -9.471    -5.436\n",
        "fico_range[T.760-764]     -7.3461      1.029     -7.141      0.000        -9.363    -5.329\n",
        "fico_range[T.765-769]     -8.0370      1.043     -7.709      0.000       -10.081    -5.993\n",
        "fico_range[T.770-774]     -8.7167      1.111     -7.844      0.000       -10.896    -6.538\n",
        "fico_range[T.775-779]     -7.4633      1.082     -6.895      0.000        -9.586    -5.341\n",
        "fico_range[T.780-784]     -8.7177      1.061     -8.218      0.000       -10.798    -6.638\n",
        "fico_range[T.785-789]     -8.0972      1.098     -7.372      0.000       -10.251    -5.943\n",
        "fico_range[T.790-794]     -8.3085      1.092     -7.607      0.000       -10.450    -6.167\n",
        "fico_range[T.795-799]     -8.8419      1.150     -7.686      0.000       -11.098    -6.586\n",
        "fico_range[T.800-804]     -8.6511      1.163     -7.439      0.000       -10.931    -6.371\n",
        "fico_range[T.805-809]     -8.5820      1.178     -7.284      0.000       -10.892    -6.272\n",
        "fico_range[T.810-814]     -6.9183      1.245     -5.556      0.000        -9.360    -4.477\n",
        "fico_range[T.815-819]     -9.0170      1.323     -6.817      0.000       -11.611    -6.423\n",
        "fico_range[T.820-824]     -7.3120      2.393     -3.056      0.002       -12.004    -2.620\n",
        "fico_range[T.830-834]     -7.5920      2.393     -3.173      0.002       -12.284    -2.900\n",
        "==============================================================================\n",
        "Omnibus:                      130.177   Durbin-Watson:                   1.922\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              181.842\n",
        "Skew:                           0.477   Prob(JB):                     3.26e-40\n",
        "Kurtosis:                       3.916   Cond. No.                         146.\n",
        "==============================================================================\n",
        "\"\"\""
       ]
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lm.pvalues"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 101,
       "text": [
        "Intercept                  3.159062e-52\n",
        "C(loan_length)[T.60.0]    1.557353e-282\n",
        "fico_range[T.645-649]      8.367674e-01\n",
        "fico_range[T.650-654]      9.726628e-01\n",
        "fico_range[T.655-659]      8.473899e-01\n",
        "fico_range[T.660-664]      2.775211e-02\n",
        "fico_range[T.665-669]      1.809146e-01\n",
        "fico_range[T.670-674]      7.278396e-01\n",
        "fico_range[T.675-679]      8.181856e-01\n",
        "fico_range[T.680-684]      3.109028e-01\n",
        "fico_range[T.685-689]      1.534355e-01\n",
        "fico_range[T.690-694]      1.133903e-01\n",
        "fico_range[T.695-699]      2.811550e-02\n",
        "fico_range[T.700-704]      2.992288e-03\n",
        "fico_range[T.705-709]      3.057081e-04\n",
        "fico_range[T.710-714]      1.727181e-04\n",
        "fico_range[T.715-719]      9.680745e-07\n",
        "fico_range[T.720-724]      2.875148e-07\n",
        "fico_range[T.725-729]      4.785347e-08\n",
        "fico_range[T.730-734]      4.420753e-10\n",
        "fico_range[T.735-739]      1.411781e-10\n",
        "fico_range[T.740-744]      7.028897e-11\n",
        "fico_range[T.745-749]      2.473532e-11\n",
        "fico_range[T.750-754]      2.813765e-13\n",
        "fico_range[T.755-759]      5.797948e-13\n",
        "fico_range[T.760-764]      1.211201e-12\n",
        "fico_range[T.765-769]      1.823367e-14\n",
        "fico_range[T.770-774]      6.428102e-15\n",
        "fico_range[T.775-779]      6.805091e-12\n",
        "fico_range[T.780-784]      3.294661e-16\n",
        "fico_range[T.785-789]      2.278067e-13\n",
        "fico_range[T.790-794]      3.962034e-14\n",
        "fico_range[T.795-799]      2.181115e-14\n",
        "fico_range[T.800-804]      1.391379e-13\n",
        "fico_range[T.805-809]      4.343024e-13\n",
        "fico_range[T.810-814]      3.054587e-08\n",
        "fico_range[T.815-819]      1.163922e-11\n",
        "fico_range[T.820-824]      2.266550e-03\n",
        "fico_range[T.830-834]      1.526738e-03"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally we check that the residual error is normally distributed, i.e. no non-random patterns of variation in the residuals."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "qqplot(lm.resid, line='s');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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3vMD4CSPr/RyaEqeMMUyePJkNGzYQEhKCpdLIv8Vi4Ztvvrn6Sq+AgkHEtZo3H0h+fiFm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\n6WPMnz/f1SVVq7i42AgPDzc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      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}