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  "name": "WA3. Linear Regression - Analysis Worksheet"
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     "input": [
      "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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       "stream": "stdout",
       "text": [
        "Coefficients:  [-0.08844242  0.00021075]\n",
        "Intercept:  72.8827983168\n",
        "P-Values:  [  0.00000000e+000   5.96972978e-203   0.00000000e+000]\n",
        "R-Squared:  0.656632624649\n"
       ]
      }
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        "    \n",
        "    .warning{\n",
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