modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modeling distributions\n",
    "=================\n",
    "\n",
    "Copyright 2015 Allen Downey\n",
    "\n",
    "License: [Creative Commons Attribution 4.0 International](http://creativecommons.org/licenses/by/4.0/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "\n",
    "import analytic\n",
    "import brfss\n",
    "import nsfg\n",
    "\n",
    "import thinkstats2\n",
    "import thinkplot\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is about ways to model data using analytic distributions.  I start with the exponential distribution, which is often a good model of time between random arrivals.\n",
    "\n",
    "On December 18, 1997, 44 babies were born in a hospital in Brisbane, Australia.  The time of birth for all 44 babies was reported in the local paper; the complete dataset is in a file called `babyboom.dat`.\n",
    "\n",
    "If the times of birth are random, we expect the times between births to fit an exponential model.\n",
    "\n",
    "Here's what the exponential CDF looks like for a range of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YMjtRZTwdQVQ2NrGv2sr+agvHrPU9xhX4aTWMC3EKwaQwE+FqVHBGlDAMAV58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\nCaHEICIiIZQYREQkxP8B5ZOhO3oYARQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6e3e32c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.PrePlot(3)\n",
    "for lam in [2.0, 1, 0.5]:\n",
    "    xs, ps = thinkstats2.RenderExpoCdf(lam, 0, 3.0, 50)\n",
    "    label = r'$\\lambda=%g$' % lam\n",
    "    thinkplot.Plot(xs, ps, label=label)\n",
    "    \n",
    "thinkplot.Config(title='Exponential CDF',\n",
    "                 xlabel='x',\n",
    "                 ylabel='CDF',\n",
    "                 loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the babyboom data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>sex</th>\n",
       "      <th>weight_g</th>\n",
       "      <th>minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3837</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>3334</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>118</td>\n",
       "      <td>2</td>\n",
       "      <td>3554</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>155</td>\n",
       "      <td>2</td>\n",
       "      <td>3838</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>257</td>\n",
       "      <td>2</td>\n",
       "      <td>3625</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>405</td>\n",
       "      <td>1</td>\n",
       "      <td>2208</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>407</td>\n",
       "      <td>1</td>\n",
       "      <td>1745</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>422</td>\n",
       "      <td>2</td>\n",
       "      <td>2846</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>431</td>\n",
       "      <td>2</td>\n",
       "      <td>3166</td>\n",
       "      <td>271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>708</td>\n",
       "      <td>2</td>\n",
       "      <td>3520</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>735</td>\n",
       "      <td>2</td>\n",
       "      <td>3380</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>812</td>\n",
       "      <td>2</td>\n",
       "      <td>3294</td>\n",
       "      <td>492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>814</td>\n",
       "      <td>1</td>\n",
       "      <td>2576</td>\n",
       "      <td>494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>909</td>\n",
       "      <td>1</td>\n",
       "      <td>3208</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1035</td>\n",
       "      <td>2</td>\n",
       "      <td>3521</td>\n",
       "      <td>635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1049</td>\n",
       "      <td>1</td>\n",
       "      <td>3746</td>\n",
       "      <td>649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1053</td>\n",
       "      <td>1</td>\n",
       "      <td>3523</td>\n",
       "      <td>653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1133</td>\n",
       "      <td>2</td>\n",
       "      <td>2902</td>\n",
       "      <td>693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1209</td>\n",
       "      <td>2</td>\n",
       "      <td>2635</td>\n",
       "      <td>729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1256</td>\n",
       "      <td>2</td>\n",
       "      <td>3920</td>\n",
       "      <td>776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1305</td>\n",
       "      <td>2</td>\n",
       "      <td>3690</td>\n",
       "      <td>785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1406</td>\n",
       "      <td>1</td>\n",
       "      <td>3430</td>\n",
       "      <td>846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1407</td>\n",
       "      <td>1</td>\n",
       "      <td>3480</td>\n",
       "      <td>847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1433</td>\n",
       "      <td>1</td>\n",
       "      <td>3116</td>\n",
       "      <td>873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1446</td>\n",
       "      <td>1</td>\n",
       "      <td>3428</td>\n",
       "      <td>886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1514</td>\n",
       "      <td>2</td>\n",
       "      <td>3783</td>\n",
       "      <td>914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1631</td>\n",
       "      <td>2</td>\n",
       "      <td>3345</td>\n",
       "      <td>991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1657</td>\n",
       "      <td>2</td>\n",
       "      <td>3034</td>\n",
       "      <td>1017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1742</td>\n",
       "      <td>1</td>\n",
       "      <td>2184</td>\n",
       "      <td>1062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1807</td>\n",
       "      <td>2</td>\n",
       "      <td>3300</td>\n",
       "      <td>1087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1825</td>\n",
       "      <td>1</td>\n",
       "      <td>2383</td>\n",
       "      <td>1105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1854</td>\n",
       "      <td>2</td>\n",
       "      <td>3428</td>\n",
       "      <td>1134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1909</td>\n",
       "      <td>2</td>\n",
       "      <td>4162</td>\n",
       "      <td>1149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1947</td>\n",
       "      <td>2</td>\n",
       "      <td>3630</td>\n",
       "      <td>1187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1949</td>\n",
       "      <td>2</td>\n",
       "      <td>3406</td>\n",
       "      <td>1189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1951</td>\n",
       "      <td>2</td>\n",
       "      <td>3402</td>\n",
       "      <td>1191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>3500</td>\n",
       "      <td>1210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2037</td>\n",
       "      <td>2</td>\n",
       "      <td>3736</td>\n",
       "      <td>1237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2051</td>\n",
       "      <td>2</td>\n",
       "      <td>3370</td>\n",
       "      <td>1251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2104</td>\n",
       "      <td>2</td>\n",
       "      <td>2121</td>\n",
       "      <td>1264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2123</td>\n",
       "      <td>2</td>\n",
       "      <td>3150</td>\n",
       "      <td>1283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2217</td>\n",
       "      <td>1</td>\n",
       "      <td>3866</td>\n",
       "      <td>1337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2327</td>\n",
       "      <td>1</td>\n",
       "      <td>3542</td>\n",
       "      <td>1407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2355</td>\n",
       "      <td>1</td>\n",
       "      <td>3278</td>\n",
       "      <td>1435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    time  sex  weight_g  minutes\n",
       "0      5    1      3837        5\n",
       "1    104    1      3334       64\n",
       "2    118    2      3554       78\n",
       "3    155    2      3838      115\n",
       "4    257    2      3625      177\n",
       "5    405    1      2208      245\n",
       "6    407    1      1745      247\n",
       "7    422    2      2846      262\n",
       "8    431    2      3166      271\n",
       "9    708    2      3520      428\n",
       "10   735    2      3380      455\n",
       "11   812    2      3294      492\n",
       "12   814    1      2576      494\n",
       "13   909    1      3208      549\n",
       "14  1035    2      3521      635\n",
       "15  1049    1      3746      649\n",
       "16  1053    1      3523      653\n",
       "17  1133    2      2902      693\n",
       "18  1209    2      2635      729\n",
       "19  1256    2      3920      776\n",
       "20  1305    2      3690      785\n",
       "21  1406    1      3430      846\n",
       "22  1407    1      3480      847\n",
       "23  1433    1      3116      873\n",
       "24  1446    1      3428      886\n",
       "25  1514    2      3783      914\n",
       "26  1631    2      3345      991\n",
       "27  1657    2      3034     1017\n",
       "28  1742    1      2184     1062\n",
       "29  1807    2      3300     1087\n",
       "30  1825    1      2383     1105\n",
       "31  1854    2      3428     1134\n",
       "32  1909    2      4162     1149\n",
       "33  1947    2      3630     1187\n",
       "34  1949    2      3406     1189\n",
       "35  1951    2      3402     1191\n",
       "36  2010    1      3500     1210\n",
       "37  2037    2      3736     1237\n",
       "38  2051    2      3370     1251\n",
       "39  2104    2      2121     1264\n",
       "40  2123    2      3150     1283\n",
       "41  2217    1      3866     1337\n",
       "42  2327    1      3542     1407\n",
       "43  2355    1      3278     1435"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = analytic.ReadBabyBoom()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here's the CDF of interarrival times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PAdcAO0bavBr4PYCqugXYluTYg73w1VfsZMc5z512vYdtXn5hrHN65qFGsM5pm5c6D0eX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\nIElqMRwkSS2GgySpxXCQJLUYDpKklv8PpK0uRQAs7noAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff71e43c150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diffs = df.minutes.diff()\n",
    "cdf = thinkstats2.Cdf(diffs, label='actual')\n",
    "\n",
    "thinkplot.PrePlot(1)\n",
    "thinkplot.Cdf(cdf)\n",
    "thinkplot.Config(xlabel='minutes',\n",
    "                 ylabel='CDF',\n",
    "                 legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visually it looks like an exponential CDF, but there are other analytic distributions that also look like this.  A stronger test is to plot the complementary CDF, that is $1-CDF(x)$ on a log-y scale.\n",
    "\n",
    "If the data are from an exponential distribution, the result should approximate a straight line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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a3mD74BzrbHvsT2w/afvQOuvssTbu4vLv8yvlcobBaoyIxn1J2k/SDklHS9pf0j2STsyg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\nJPt/HHi8O8XcIbUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6e3b72250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.PrePlot(1)\n",
    "thinkplot.Cdf(cdf, complement=True)\n",
    "thinkplot.Config(xlabel='minutes',\n",
    "                 ylabel='CCDF',\n",
    "                 yscale='log',\n",
    "                 legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is not exactly straight, which indicates that the exponential distribution is not a perfect model for this data. Most likely the underlying assumption—that a birth is equally likely at any time of day—is not exactly true. Nevertheless, it might be reasonable to model this dataset with an exponential distribution.\n",
    "\n",
    "As George Box said, \"All models are wrong, but some are useful\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The normal distribution\n",
    "\n",
    "Many quantities in the natural world are well modeled by a normal distribution, also known as a Gaussian.\n",
    "\n",
    "Here is what the CDF of a normal distriubution looks like for a few different parameter values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AqW5QeOA4v3/0dat9240XcdHc2JwgBXhq7X6aWvwlE0YMSOPaebk2RxQ55U3N7KiottqX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\nUsrBNAkopZSD/X+aDPF9+48gqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff711e0e090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.PrePlot(3)\n",
    "\n",
    "mus = [1.0, 2.0, 3.0]\n",
    "sigmas = [0.5, 0.4, 0.3]\n",
    "for mu, sigma in zip(mus, sigmas):\n",
    "    xs, ps = thinkstats2.RenderNormalCdf(mu=mu, sigma=sigma, \n",
    "                                               low=-1.0, high=4.0)\n",
    "    label = r'$\\mu=%g$, $\\sigma=%g$' % (mu, sigma)\n",
    "    thinkplot.Plot(xs, ps, label=label)\n",
    "\n",
    "thinkplot.Config(title='Normal CDF',\n",
    "                 xlabel='x',\n",
    "                 ylabel='CDF',\n",
    "                 loc=2)\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We might expect the distribution of birth weights to be approximately normal.  I'll load data from the NSFG again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "preg = nsfg.ReadFemPreg()\n",
    "weights = preg.totalwgt_lb.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can estimate the parameters of the normal distribution, `mu` and `sigma`, then plot the data on top of the analytic model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean, Var 7.28088310002 1.54521257035\n",
      "Sigma 1.24306579486\n"
     ]
    },
    {
     "data": {
      "image/png": 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KMxiMSHSUEET2Z/sH1NTUB9uWx/ivjspsPCIRUUIQ2Z/11U3bsRLGnfuZjIUiEiUlBJH9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6e3e4bd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu, var = thinkstats2.TrimmedMeanVar(weights, p=0.01)\n",
    "print('Mean, Var', mu, var)\n",
    "    \n",
    "    # plot the model\n",
    "sigma = math.sqrt(var)\n",
    "print('Sigma', sigma)\n",
    "xs, ps = thinkstats2.RenderNormalCdf(mu, sigma, low=0, high=12.5)\n",
    "\n",
    "thinkplot.Plot(xs, ps, label='model', color='orange')\n",
    "\n",
    "    # plot the data\n",
    "cdf = thinkstats2.Cdf(weights, label='data')\n",
    "\n",
    "thinkplot.PrePlot(1)\n",
    "thinkplot.Cdf(cdf) \n",
    "thinkplot.Config(title='Birth weights',\n",
    "                   xlabel='birth weight (lbs)',\n",
    "                   ylabel='CDF',\n",
    "                   legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data fit the model well, but there are some deviations in the lower tail.\n",
    "\n",
    "To get a better view of the tails, we can use a normal probability plot, which plots the actual data versus sorted values from random normal values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def NormalProbability(ys, jitter=0.0):\n",
    "    \"\"\"Generates data for a normal probability plot.\n",
    "\n",
    "    ys: sequence of values\n",
    "    jitter: float magnitude of jitter added to the ys \n",
    "\n",
    "    returns: numpy arrays xs, ys\n",
    "    \"\"\"\n",
    "    n = len(ys)\n",
    "    xs = np.random.normal(0, 1, n)\n",
    "    xs.sort()\n",
    "    \n",
    "    if jitter:\n",
    "        ys = Jitter(ys, jitter)\n",
    "    else:\n",
    "        ys = np.array(ys)\n",
    "    ys.sort()\n",
    "\n",
    "    return xs, ys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the data are normal, the result should be a straight line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xwGJVPT1mQdmVvelhO33V3Pand1izvoyGhgb8/kCbNqedPIyDpxaTl5tBYWEh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\nb4wxScCSvTHGJIH/B6UU8Lf2Z8O+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6e388f3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean, var = thinkstats2.TrimmedMeanVar(weights, p=0.01)\n",
    "std = math.sqrt(var)\n",
    "\n",
    "xlim = [-4.5, 4.5]\n",
    "fxs, fys = thinkstats2.FitLine(xlim, mean, std)\n",
    "thinkplot.Plot(fxs, fys, linewidth=4, color='0.8')\n",
    "\n",
    "thinkplot.PrePlot(2) \n",
    "xs, ys = NormalProbability(weights)\n",
    "thinkplot.Plot(xs, ys, label='all live')\n",
    "\n",
    "thinkplot.Config(title='Normal probability plot',\n",
    "                 xlabel='Standard deviations from mean',\n",
    "                 ylabel='Birth weight (lbs)',\n",
    "                 legend=True, loc='lower right',\n",
    "                 xlim=xlim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normal probability plot shows that the lightest babies are lighter than expected, starting about two standard deviations below the mean.  Also, the heaviest babies are heavier than the model predicts.\n",
    "\n",
    "The NSFG data includes premature babies; if we select full-term babies, we might expect the normal model to be a better fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "full_term = preg[preg.prglngth >= 37]\n",
    "term_weights = full_term.totalwgt_lb.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Mp4+ewdQpE230SdPFUIP9h6q6MNo2EVmuqvMHWJkZwKvAfqraJiJ/Bv6hqn+M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\nDUhjnyRJUgP+BOUVQ7VuTUnGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6e37c61d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Plot(fxs, fys, linewidth=4, color='0.8')\n",
    "\n",
    "thinkplot.PrePlot(2) \n",
    "xs, ys = NormalProbability(weights)\n",
    "thinkplot.Plot(xs, ys, label='all live')\n",
    "\n",
    "xs, ys = NormalProbability(term_weights)\n",
    "thinkplot.Plot(xs, ys, label='full term')\n",
    "\n",
    "thinkplot.Config(title='Normal probability plot',\n",
    "                 xlabel='Standard deviations from mean',\n",
    "                 ylabel='Birth weight (lbs)',\n",
    "                 legend=True, loc='lower right',\n",
    "                 xlim=xlim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the normal model is a better fit for full-term babies at the low end of the distribution.\n",
    "\n",
    "But it turns out that the normal model does not do very well for adult weight.  I'll load data from the BRFSS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = brfss.ReadBrfss()\n",
    "weights = df.wtkg2.dropna()\n",
    "log_weights = np.log10(weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function generates normal probability plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def MakeNormalPlot(weights):\n",
    "    \"\"\"Generates a normal probability plot of birth weights.\n",
    "\n",
    "    weights: sequence\n",
    "    \"\"\"\n",
    "    mean, var = thinkstats2.TrimmedMeanVar(weights, p=0.01)\n",
    "    std = math.sqrt(var)\n",
    "\n",
    "    xs = [-5, 5]\n",
    "    xs, ys = thinkstats2.FitLine(xs, mean, std)\n",
    "    thinkplot.Plot(xs, ys, color='0.8', label='model')\n",
    "\n",
    "    xs, ys = thinkstats2.NormalProbability(weights)\n",
    "    thinkplot.Plot(xs, ys, label='weights')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normal distribution is a poor model for the distribution of adult weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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m+P3vnUgkQjgcJhyO0NVm9kGpiQKOPrqQc881iopg1121oEykK1lNCma2BggB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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6e35a7cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MakeNormalPlot(weights)\n",
    "thinkplot.Config(xlabel='z', ylabel='weights (kg)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But if we compute the log of adult weights, the normal distribution is much better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4WAcDjuxBXKs40lKSmHTNGFKSE0MUqTHRIVAPi7UDbga6ee8M+gCHq+rcAMVp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\neigvvfQSMTExbNiwofp8YWE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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6e24971d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MakeNormalPlot(log_weights)\n",
    "thinkplot.Config(xlabel='z', ylabel='weights (log10 kg)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Within 3 standard deviations of the mean, the normal model does quite well, although the heaviest and lightest people diverge from the model.\n",
    "\n",
    "If $\\log x$ has a normal distribution, $x$ has a lognormal distribution."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}