solutions/ch08q01.ipynb

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    {
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     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('..')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import re\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from chapters.chapter8 import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
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    "collapsed": true
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   "outputs": [],
   "source": [
    "# numpy assumes columns are observations, so we have to transpose our data\n",
    "corr = lambda df: np.corrcoef(df.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lsat</th>\n",
       "      <th>gpa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>576</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>635</td>\n",
       "      <td>3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>558</td>\n",
       "      <td>2.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>578</td>\n",
       "      <td>3.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666</td>\n",
       "      <td>3.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   lsat   gpa\n",
       "0   576  3.39\n",
       "1   635  3.30\n",
       "2   558  2.81\n",
       "3   578  3.03\n",
       "4   666  3.44"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/lsat_efron.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.54591892],\n",
       "       [ 0.54591892,  1.        ]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = corr(df)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  9.41793757e-17,   1.96841299e-01],\n",
       "       [  1.96841299e-01,   8.26799418e-17]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "se = np.sqrt(bootstrap_variance(df, corr, 10000))\n",
    "se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CI(lower=array([[ 1.        ,  0.16371497],\n",
       "       [ 0.16371497,  1.        ]]), upper=array([[ 1.        ,  0.92812286],\n",
       "       [ 0.92812286,  1.        ]]))"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bootstrap_ci_normal(df, corr, 10000, 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CI(lower=array([[ 1.        ,  0.09183783],\n",
       "       [ 0.09183783,  1.        ]]), upper=array([[ 1.72633836,  0.8181762 ],\n",
       "       [ 0.8181762 ,  1.72633836]]))"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bootstrap_ci_pivot(df, corr, 10000, 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CI(lower=array([[ 1.        ,  0.20647611],\n",
       "       [ 0.20647611,  1.        ]]), upper=array([[ 1.        ,  0.93652281],\n",
       "       [ 0.93652281,  1.        ]]))"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bootstrap_ci_percentile(df, corr, 10000, 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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