Demo2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datascience import *\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plots\n",
    "plots.style.use('fivethirtyeight')\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are data on 0/1 smoking and continuous weight in two waves (waves 8 and 9) of a panel survey. Your first exercise is to compute the average weight of a smoker in wave 8 and the average weight of a non-smoke in wave 8. After doing that, compute the difference between the two averages. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make some predictions regarding the results; which average weight do you think will be higher, the weight of smokers or the weight of non-smokers? Why is that?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>hhidpn</th> <th>ragender</th> <th>r8agey_m</th> <th>r8weight</th> <th>r9weight</th> <th>r8smoken</th> <th>r9smoken</th>\n",
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       "        </tr>\n",
       "    </tbody>\n",
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       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (16009 rows omitted)</p"
      ],
      "text/plain": [
       "hhidpn   | ragender | r8agey_m | r8weight | r9weight | r8smoken | r9smoken\n",
       "3010     | 1        | 70       | 71.6672  | 65.317   | 0        | 0\n",
       "3020     | 2        | 67       | 65.317   | 68.0385  | 0        | 0\n",
       "10001010 | 1        | 66       | 72.5744  | 72.5744  | 0        | 0\n",
       "10003030 | 2        | 50       | 58.9667  | 72.5744  | 0        | 0\n",
       "10004010 | 1        | 66       | 102.511  | 100.697  | 0        | 0\n",
       "10004040 | 2        | 60       | 77.1103  | 74.8423  | 0        | 0\n",
       "10013010 | 1        | 68       | 108.862  | 99.7898  | 0        | 0\n",
       "10013040 | 2        | 58       | 64.4098  | 63.5026  | 1        | 1\n",
       "10038010 | 1        | 70       | 74.8423  | 73.4816  | 0        | 0\n",
       "10038040 | 2        | 63       | 64.4098  | 63.5026  | 0        | 0\n",
       "... (16009 rows omitted)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load file\n",
    "smoker_ratio = Table.read_table('c08_r89smokeweight.csv')\n",
    "smoker_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>hhidpn</th> <th>ragender</th> <th>r8agey_m</th> <th>r8weight</th> <th>r9weight</th> <th>r8smoken</th> <th>r9smoken</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>10013040</td> <td>2       </td> <td>58      </td> <td>64.4098 </td> <td>63.5026 </td> <td>1       </td> <td>1       </td>\n",
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       "<p>... (2212 rows omitted)</p"
      ],
      "text/plain": [
       "hhidpn   | ragender | r8agey_m | r8weight | r9weight | r8smoken | r9smoken\n",
       "10013040 | 2        | 58       | 64.4098  | 63.5026  | 1        | 1\n",
       "10059020 | 2        | 70       | 57.6059  | 57.1523  | 1        | 1\n",
       "10083010 | 1        | 67       | 122.469  | 95.7075  | 1        | 0\n",
       "10196010 | 2        | 71       | 58.9667  | 52.1628  | 1        | 0\n",
       "10433010 | 2        | 71       | 125.644  | 120.201  | 1        | 0\n",
       "10482010 | 2        | 65       | 61.2347  | 65.7706  | 1        | 1\n",
       "10577011 | 1        | 56       | 105.233  | 105.686  | 1        | 1\n",
       "11071010 | 2        | 73       | 81.6462  | 77.1103  | 1        | 1\n",
       "11230010 | 2        | 70       | 63.5026  | 65.7706  | 1        | 1\n",
       "11345010 | 1        | 66       | 86.1821  | 86.1821  | 1        | 1\n",
       "... (2212 rows omitted)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter the table by only including rows where r8smoken=1\n",
    "smoker8_weight_filter = smoker_ratio.where(smoker_ratio['r8smoken']== 1)\n",
    "smoker8_weight_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  64.40978,   57.60593,  122.4693 , ...,   90.718  ,   65.77055,\n",
       "         56.69875])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select the column of smoker weights in wave 8\n",
    "smoker8_weight = smoker8_weight_filter['r8weight']\n",
    "smoker8_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76.134530202520253"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compute the average weight of smokers in wave 8\n",
    "smoker8_avg= smoker8_weight.mean()\n",
    "smoker8_avg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you have calculated the average weight of smokers in wave 8, lets now calculate the average weight of non-smokers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>hhidpn</th> <th>ragender</th> <th>r8agey_m</th> <th>r8weight</th> <th>r9weight</th> <th>r8smoken</th> <th>r9smoken</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>3010    </td> <td>1       </td> <td>70      </td> <td>71.6672 </td> <td>65.317  </td> <td>0       </td> <td>0       </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>3020    </td> <td>2       </td> <td>67      </td> <td>65.317  </td> <td>68.0385 </td> <td>0       </td> <td>0       </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
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       "            <td>10001010</td> <td>1       </td> <td>66      </td> <td>72.5744 </td> <td>72.5744 </td> <td>0       </td> <td>0       </td>\n",
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       "    </tbody>\n",
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       "            <td>10003030</td> <td>2       </td> <td>50      </td> <td>58.9667 </td> <td>72.5744 </td> <td>0       </td> <td>0       </td>\n",
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       "            <td>10004010</td> <td>1       </td> <td>66      </td> <td>102.511 </td> <td>100.697 </td> <td>0       </td> <td>0       </td>\n",
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       "    </tbody>\n",
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       "            <td>10004040</td> <td>2       </td> <td>60      </td> <td>77.1103 </td> <td>74.8423 </td> <td>0       </td> <td>0       </td>\n",
       "        </tr>\n",
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       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>10038010</td> <td>1       </td> <td>70      </td> <td>74.8423 </td> <td>73.4816 </td> <td>0       </td> <td>0       </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>10038040</td> <td>2       </td> <td>63      </td> <td>64.4098 </td> <td>63.5026 </td> <td>0       </td> <td>0       </td>\n",
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       "            <td>10050010</td> <td>2       </td> <td>64      </td> <td>68.0385 </td> <td>68.0385 </td> <td>0       </td> <td>0       </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (13787 rows omitted)</p"
      ],
      "text/plain": [
       "hhidpn   | ragender | r8agey_m | r8weight | r9weight | r8smoken | r9smoken\n",
       "3010     | 1        | 70       | 71.6672  | 65.317   | 0        | 0\n",
       "3020     | 2        | 67       | 65.317   | 68.0385  | 0        | 0\n",
       "10001010 | 1        | 66       | 72.5744  | 72.5744  | 0        | 0\n",
       "10003030 | 2        | 50       | 58.9667  | 72.5744  | 0        | 0\n",
       "10004010 | 1        | 66       | 102.511  | 100.697  | 0        | 0\n",
       "10004040 | 2        | 60       | 77.1103  | 74.8423  | 0        | 0\n",
       "10013010 | 1        | 68       | 108.862  | 99.7898  | 0        | 0\n",
       "10038010 | 1        | 70       | 74.8423  | 73.4816  | 0        | 0\n",
       "10038040 | 2        | 63       | 64.4098  | 63.5026  | 0        | 0\n",
       "10050010 | 2        | 64       | 68.0385  | 68.0385  | 0        | 0\n",
       "... (13787 rows omitted)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter the table by only including rows where r8smoken=0\n",
    "non8_weight_filter = smoker_ratio.where(smoker_ratio['r8smoken']== 0)\n",
    "non8_weight_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  71.66722,   65.31696,   72.5744 , ...,  102.9649 ,   56.69875,\n",
       "         81.6462 ])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select the column of non-smoker weights in wave 8\n",
    "non8_weight = non8_weight_filter['r8weight']\n",
    "non8_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.945557732115674"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compute the average weight of non-smokers in wave 8\n",
    "non8_avg= non8_weight.mean()\n",
    "non8_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now, calculate the difference between the two averages\n",
    "wave8_diff = non8_avg - smoker8_avg\n",
    "wave8_diff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observe the differences between the two averages: which number is higher? Do the results match the predications you made at the beginning of this exercise?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's calculate the average weight of a smoker for wave 9. Try to fill in the blanks, and once again make some predictions regarding the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Filter the table by only including rows where r9smoken=0\n",
    "smoker9_weight_filter = smoker_ratio.......\n",
    "smoker9_weight_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Select the column of smoker weights in wave 9\n",
    "smoker9_weight = ........\n",
    "smoker9_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Compute the average weight of smokers in wave 9\n",
    "smoker9_avg= ........\n",
    "smoker9_avg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Now that you have successfully calculated the average weight of smokers in wave 9, do the results match your predictions? Are the numbers similar? Which one is closer to the true effect of smoking on weight? Should we also look at the change of weight among people who are nonsmokers in both waves?"
   ]
  }
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