{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# HIDDEN\n", "from datascience import *\n", "%matplotlib inline\n", "path_data = '../../../data/'\n", "import matplotlib.pyplot as plots\n", "plots.style.use('fivethirtyeight')\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confidence Intervals ###\n", "We have developed a method for estimating a parameter by using random sampling and the bootstrap. Our method produces an interval of estimates, to account for chance variability in the random sample. By providing an interval of estimates instead of just one estimate, we give ourselves some wiggle room.\n", "\n", "In the previous example we saw that our process of estimation produced a good interval about 95% of the time, a \"good\" interval being one that contains the parameter. We say that we are *95% confident* that the process results in a good interval. Our interval of estimates is called a *95% confidence interval* for the parameter, and 95% is called the *confidence level* of the interval.\n", "\n", "The situation in the previous example was a bit unusual. Because we happened to know value of the parameter, we were able to check whether an interval was good or a dud, and this in turn helped us to see that our process of estimation captured the parameter about 95 out of every 100 times we used it.\n", "\n", "But usually, data scientists don't know the value of the parameter. That is the reason they want to estimate it in the first place. In such situations, they provide an interval of estimates for the unknown parameter by using methods like the one we have developed. Because of statistical theory and demonstrations like the one we have seen, data scientists can be confident that their process of generating the interval results in a good interval a known percent of the time." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Confidence Interval for a Population Median: Bootstrap Percentile Method ###\n", "\n", "We will now use the bootstrap method to estimate an unknown population median. The data come from a sample of newborns in a large hospital system; we will treat it as if it were a simple random sample though the sampling was done in multiple stages. [Stat Labs](https://www.stat.berkeley.edu/~statlabs/) by Deborah Nolan and Terry Speed has details about a larger dataset from which this set is drawn. \n", "\n", "The table `baby` contains the following variables for mother-baby pairs: the baby's birth weight in ounces, the number of gestational days, the mother's age in completed years, the mother's height in inches, pregnancy weight in pounds, and whether or not the mother smoked during pregnancy." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "baby = Table.read_table(path_data + 'baby.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
Birth Weight | Gestational Days | Maternal Age | Maternal Height | Maternal Pregnancy Weight | Maternal Smoker | \n", "
---|---|---|---|---|---|
120 | 284 | 27 | 62 | 100 | False | \n", "
113 | 282 | 33 | 64 | 135 | False | \n", "
128 | 279 | 28 | 64 | 115 | True | \n", "
108 | 282 | 23 | 67 | 125 | True | \n", "
136 | 286 | 25 | 62 | 93 | False | \n", "
138 | 244 | 33 | 62 | 178 | False | \n", "
132 | 245 | 23 | 65 | 140 | False | \n", "
120 | 289 | 25 | 62 | 125 | False | \n", "
143 | 299 | 30 | 66 | 136 | True | \n", "
140 | 351 | 27 | 68 | 120 | False | \n", "
... (1164 rows omitted)
" ], "text/plain": [ "Birth Weight | Gestational Days | Maternal Age | Maternal Height | Maternal Pregnancy Weight | Maternal Smoker\n", "120 | 284 | 27 | 62 | 100 | False\n", "113 | 282 | 33 | 64 | 135 | False\n", "128 | 279 | 28 | 64 | 115 | True\n", "108 | 282 | 23 | 67 | 125 | True\n", "136 | 286 | 25 | 62 | 93 | False\n", "138 | 244 | 33 | 62 | 178 | False\n", "132 | 245 | 23 | 65 | 140 | False\n", "120 | 289 | 25 | 62 | 125 | False\n", "143 | 299 | 30 | 66 | 136 | True\n", "140 | 351 | 27 | 68 | 120 | False\n", "... (1164 rows omitted)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "baby" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Birth weight is an important factor in the health of a newborn infant – smaller babies tend to need more medical care in their first days than larger newborns. It is therefore helpful to have an estimate of birth weight before the baby is born. One way to do this is to examine the relationship between birth weight and the number of gestational days. \n", "\n", "A simple measure of this relationship is the ratio of birth weight to the number of gestational days. The table `ratios` contains the first two columns of `baby`, as well as a column of the ratios. The first entry in that column was calcualted as follows:\n", "\n", "$$\n", "\\frac{120~\\mbox{ounces}}{284~\\mbox{days}} ~\\approx ~ 0.4225~ \\mbox{ounces per day}\n", "$$" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ratios = baby.select('Birth Weight', 'Gestational Days').with_column(\n", " 'Ratio BW/GD', baby.column('Birth Weight')/baby.column('Gestational Days')\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "Birth Weight | Gestational Days | Ratio BW/GD | \n", "
---|---|---|
120 | 284 | 0.422535 | \n", "
113 | 282 | 0.400709 | \n", "
128 | 279 | 0.458781 | \n", "
108 | 282 | 0.382979 | \n", "
136 | 286 | 0.475524 | \n", "
138 | 244 | 0.565574 | \n", "
132 | 245 | 0.538776 | \n", "
120 | 289 | 0.415225 | \n", "
143 | 299 | 0.478261 | \n", "
140 | 351 | 0.39886 | \n", "
... (1164 rows omitted)
" ], "text/plain": [ "Birth Weight | Gestational Days | Ratio BW/GD\n", "120 | 284 | 0.422535\n", "113 | 282 | 0.400709\n", "128 | 279 | 0.458781\n", "108 | 282 | 0.382979\n", "136 | 286 | 0.475524\n", "138 | 244 | 0.565574\n", "132 | 245 | 0.538776\n", "120 | 289 | 0.415225\n", "143 | 299 | 0.478261\n", "140 | 351 | 0.39886\n", "... (1164 rows omitted)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ratios" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is a histogram of the ratios." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/choldgraf/anaconda/envs/textbook/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "Birth Weight | Gestational Days | Ratio BW/GD | \n", "
---|---|---|
116 | 148 | 0.783784 | \n", "