{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# HIDDEN\n", "\n", "from datascience import *\n", "import numpy as np\n", "from scipy import stats\n", "\n", "import matplotlib\n", "matplotlib.use('Agg', warn=False)\n", "%matplotlib inline\n", "import matplotlib.pyplot as plots\n", "plots.style.use('fivethirtyeight')\n", "import warnings\n", "warnings.simplefilter(action=\"ignore\", category=FutureWarning)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# HIDDEN \n", "\n", "galton = Table.read_table('galton.csv')\n", "heights = galton.select('midparentHeight', 'childHeight')\n", "heights = heights.relabel(0, 'MidParent').relabel(1, 'Child')\n", "hybrid = Table.read_table('hybrid.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# HIDDEN\n", "\n", "def standard_units(x):\n", " return (x - np.mean(x))/np.std(x)\n", "\n", "def correlation(table, x, y):\n", " x_in_standard_units = standard_units(table.column(x))\n", " y_in_standard_units = standard_units(table.column(y))\n", " return np.mean(x_in_standard_units * y_in_standard_units)\n", "\n", "def slope(table, x, y):\n", " r = correlation(table, x, y)\n", " return r * np.std(table.column(y))/np.std(table.column(x))\n", "\n", "def intercept(table, x, y):\n", " a = slope(table, x, y)\n", " return np.mean(table.column(y)) - a * np.mean(table.column(x))\n", "\n", "def fit(table, x, y):\n", " a = slope(table, x, y)\n", " b = intercept(table, x, y)\n", " return a * table.column(x) + b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visual Diagnostics ###\n", "Suppose a data scientist has decided to use linear regression to estimate values of a response variable based on a predictor. To see how well this method of estmation performs, the data scientist must how far off the estimates are from the actual values. These differences are called *residuals*.\n", "\n", "$$\n", "\\mbox{residual} ~=~ \\mbox{observed value} ~-~ \\mbox{regression estimate}\n", "$$\n", "\n", "A residual is what's left over – the residue – after estimation. \n", "\n", "Residuals are the vertical distances of the points from the regression line. There is one residual for each point in the scatter plot. The residual is the difference between the observed value of $y$ and the fitted value of $y$, so fr the point $(x, y)$,\n", "\n", "$$\n", "\\mbox{residual} ~~ = ~~ y ~-~\n", "\\mbox{fitted value of }y\n", "~~ = ~~ y ~-~\n", "\\mbox{height of regression line at }x\n", "$$\n", "\n", "The function `residual` calculates the residuals. The calculation assumes all the relevant functions we have already defined: `standard_units`, `correlation`, `slope`, `intercept`, and `fit`." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def residual(table, x, y):\n", " return table.column(y) - fit(table, x, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Continuing our example of using Galton's data to estimate the heights of adult children (the response) based on the midparent height (the predictor), let us calculate the fitted values and the residuals." ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
MidParent | Child | Fitted Value | Residual | \n", "
---|---|---|---|
75.43 | 73.2 | 70.7124 | 2.48763 | \n", "
75.43 | 69.2 | 70.7124 | -1.51237 | \n", "
75.43 | 69 | 70.7124 | -1.71237 | \n", "
75.43 | 69 | 70.7124 | -1.71237 | \n", "
73.66 | 73.5 | 69.5842 | 3.91576 | \n", "
73.66 | 72.5 | 69.5842 | 2.91576 | \n", "
73.66 | 65.5 | 69.5842 | -4.08424 | \n", "
73.66 | 65.5 | 69.5842 | -4.08424 | \n", "
72.06 | 71 | 68.5645 | 2.43553 | \n", "
72.06 | 68 | 68.5645 | -0.564467 | \n", "
... (924 rows omitted)
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "scatter_fit(heights, 'MidParent', 'Child')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A *residual plot* can be drawn by plotting the residuals against the predictor variable. The function `residual_plot` does just that. " ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def residual_plot(table, x, y):\n", " x_array = table.column(x)\n", " t = Table().with_columns(\n", " x, x_array,\n", " 'residuals', residual(table, x, y)\n", " )\n", " t.scatter(x, 'residuals', color='r')\n", " xlims = make_array(min(x_array), max(x_array))\n", " plots.plot(xlims, make_array(0, 0), color='darkblue', lw=4)\n", " plots.title('Residual Plot')" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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CwYMwrlrlcVxcJTIAQDhyhF2P5wGjsVXj4gt/hx6Jt9/OxPmsVkg6HcQQLxJJ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"text/plain": [ "Length | Age | \n", "
---|---|
1.8 | 1 | \n", "
1.85 | 1.5 | \n", "
1.87 | 1.5 | \n", "
1.77 | 1.5 | \n", "
2.02 | 2.5 | \n", "
2.27 | 4 | \n", "
2.15 | 5 | \n", "
2.26 | 5 | \n", "
2.35 | 7 | \n", "
2.47 | 8 | \n", "
... (17 rows omitted)
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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LBweHUrfT5QcppaSkJLNpC8D2mAJTaZOtm5u6BwGlEkonJ9G6TaU9hmYSF6lzc3Nx+fJl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\nEsWAICIiUf8PXVfCvXHmy5YAAAAASUVORK5CYII=\n", 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