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     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "Coefficients:  [ 72.88279832  -0.08844242]\n",
      "Intercept:  0.000210747768548\n",
      "P-Values:  [  0.00000000e+000   0.00000000e+000   5.96972978e-203]\n",
      "R-Squared:  0.656632624649\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "#from sklearn import datasets, linear_model\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# import the cleaned up dataset\n",
    "df = pd.read_csv('../datasets/loanf.csv')\n",
    "\n",
    "intrate = df['Interest.Rate']\n",
    "loanamt = df['Loan.Amount']\n",
    "fico = df['FICO.Score']\n",
    "\n",
    "# reshape the data from a pandas Series to columns \n",
    "# the dependent variable\n",
    "y = np.matrix(intrate).transpose()\n",
    "# the independent variables shaped as columns\n",
    "x1 = np.matrix(fico).transpose()\n",
    "x2 = np.matrix(loanamt).transpose()\n",
    "\n",
    "# put the two columns together to create an input matrix \n",
    "# if we had n independent variables we would have n columns here\n",
    "x = np.column_stack([x1,x2])\n",
    "\n",
    "# create a linear model and fit it to the data\n",
    "X = sm.add_constant(x)\n",
    "model = sm.OLS(y,X)\n",
    "f = model.fit()\n",
    "\n",
    "print 'Coefficients: ', f.params[0:2]\n",
    "print 'Intercept: ', f.params[2]\n",
    "print 'P-Values: ', f.pvalues\n",
    "print 'R-Squared: ', f.rsquared\n"
   ]
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