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    "#Linear_Regression_Plot_Channing7June2015\n",
    "\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "from sklearn import linear_model\n",
    "\n",
    "#Load data\n",
    "BSA=[[50,1.22],[25,0.958],[12.5,0.792],[6.25,0.758]]\n",
    "BSA=np.array(BSA)\n",
    "\n",
    "#Create linear regression object\n",
    "regr=linear_model.LinearRegression()\n",
    "\n",
    "#Define X and Y, use only one feature\n",
    "X=BSA[:,[0]]\n",
    "Y=BSA[:,[1]]\n",
    "\n",
    "#Fit\n",
    "regr.fit(X,Y)\n",
    "\n",
    "#Plotting\n",
    "pl.scatter(X,Y,color='black')\n",
    "pl.plot(X,regr.predict(X),color='red',linewidth=1)\n",
    "\n",
    "#Fomula\n",
    "k=regr.coef_\n",
    "b=regr.intercept_\n",
    "R_square=1-np.mean((regr.predict(X)-Y)**2)\n",
    "\n",
    "pl.title(\"y=%.4lf x + %.4lf \\n R^2=%.4lf\" %(k,b,R_square))\n",
    "\n",
    "pl.show()\n",
    "\n"
   ]
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