From 95410ca1575be29e009cb1bc329fca9a7b4a8a75 Mon Sep 17 00:00:00 2001 From: Neil Smith Date: Fri, 16 Feb 2018 11:45:18 +0000 Subject: [PATCH] Initial commit --- .../accidents-regression-checkpoint.ipynb | 6 + .../section5.1-checkpoint.ipynb | 705 ++++++++ .../section5.1solutions-checkpoint.ipynb | 1557 +++++++++++++++++ .ipynb_checkpoints/unit10-checkpoint.ipynb | 39 + .ipynb_checkpoints/unit3-checkpoint.ipynb | 6 + accidents-regression.ipynb | 1554 ++++++++++++++++ anaerob.csv | 54 + cemheat.csv | 14 + rubber.csv | 31 + section5.1.ipynb | 840 +++++++++ section5.1solutions.ipynb | 896 ++++++++++ unit10.ipynb | 39 + unit3.ipynb | 39 + 13 files changed, 5780 insertions(+) create mode 100644 .ipynb_checkpoints/accidents-regression-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/section5.1-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/section5.1solutions-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/unit10-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/unit3-checkpoint.ipynb create mode 100644 accidents-regression.ipynb create mode 100644 anaerob.csv create mode 100644 cemheat.csv create mode 100644 rubber.csv create mode 100644 section5.1.ipynb create mode 100644 section5.1solutions.ipynb create mode 100644 unit10.ipynb create mode 100644 unit3.ipynb diff --git a/.ipynb_checkpoints/accidents-regression-checkpoint.ipynb b/.ipynb_checkpoints/accidents-regression-checkpoint.ipynb new file mode 100644 index 0000000..2fd6442 --- /dev/null +++ b/.ipynb_checkpoints/accidents-regression-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.ipynb_checkpoints/section5.1-checkpoint.ipynb b/.ipynb_checkpoints/section5.1-checkpoint.ipynb new file mode 100644 index 0000000..dca6b94 --- /dev/null +++ b/.ipynb_checkpoints/section5.1-checkpoint.ipynb @@ -0,0 +1,705 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Section 5.1: Using the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports and defintions" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "── Attaching packages ─────────────────────────────────────── tidyverse 1.2.1 ──\n", + "✔ ggplot2 2.2.1 ✔ purrr 0.2.4\n", + "✔ tibble 1.4.2 ✔ dplyr 0.7.4\n", + "✔ tidyr 0.8.0 ✔ stringr 1.2.0\n", + "✔ readr 1.1.1 ✔ forcats 0.2.0\n", + "── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n", + "✖ dplyr::filter() masks stats::filter()\n", + "✖ dplyr::lag() masks stats::lag()\n" + ] + } + ], + "source": [ + "library(tidyverse)\n", + "# library(cowplot)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Multiple plot function\n", + "#\n", + "# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)\n", + "# - cols: Number of columns in layout\n", + "# - layout: A matrix specifying the layout. If present, 'cols' is ignored.\n", + "#\n", + "# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),\n", + "# then plot 1 will go in the upper left, 2 will go in the upper right, and\n", + "# 3 will go all the way across the bottom.\n", + "#\n", + "multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {\n", + " library(grid)\n", + "\n", + " # Make a list from the ... arguments and plotlist\n", + " plots <- c(list(...), plotlist)\n", + "\n", + " numPlots = length(plots)\n", + "\n", + " # If layout is NULL, then use 'cols' to determine layout\n", + " if (is.null(layout)) {\n", + " # Make the panel\n", + " # ncol: Number of columns of plots\n", + " # nrow: Number of rows needed, calculated from # of cols\n", + " layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),\n", + " ncol = cols, nrow = ceiling(numPlots/cols))\n", + " }\n", + "\n", + " if (numPlots==1) {\n", + " print(plots[[1]])\n", + "\n", + " } else {\n", + " # Set up the page\n", + " grid.newpage()\n", + " pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))\n", + "\n", + " # Make each plot, in the correct location\n", + " for (i in 1:numPlots) {\n", + " # Get the i,j matrix positions of the regions that contain this subplot\n", + " matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))\n", + "\n", + " print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,\n", + " layout.pos.col = matchidx$col))\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modelling abrasion loss\n", + "\n", + "This example concerns the dataset `rubber`, which you first met in Exercise 3.1. The experiment introduced in that exercise concerned an investigation of the resistance of rubber to abrasion (the abrasion loss) and how that resistance depended on various attributes of the rubber. In Exercise 3.1, the only explanatory variable we analysed was hardness; but in Exercise 3.19, a second explanatory variable, tensile strength, was taken into account too. There are 30 datapoints." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
losshardnessstrength
37245 162
20655 233
17561 232
15466 231
13671 231
11271 237
5581 224
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16660 189
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34059 146
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\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lll}\n", + " loss & hardness & strength\\\\\n", + "\\hline\n", + "\t 372 & 45 & 162\\\\\n", + "\t 206 & 55 & 233\\\\\n", + "\t 175 & 61 & 232\\\\\n", + "\t 154 & 66 & 231\\\\\n", + "\t 136 & 71 & 231\\\\\n", + "\t 112 & 71 & 237\\\\\n", + "\t 55 & 81 & 224\\\\\n", + "\t 45 & 86 & 219\\\\\n", + "\t 221 & 53 & 203\\\\\n", + "\t 166 & 60 & 189\\\\\n", + "\t 164 & 64 & 210\\\\\n", + "\t 113 & 68 & 210\\\\\n", + "\t 82 & 79 & 196\\\\\n", + "\t 32 & 81 & 180\\\\\n", + "\t 228 & 56 & 200\\\\\n", + "\t 196 & 68 & 173\\\\\n", + "\t 128 & 75 & 188\\\\\n", + "\t 97 & 83 & 161\\\\\n", + "\t 64 & 88 & 119\\\\\n", + "\t 249 & 59 & 161\\\\\n", + "\t 219 & 71 & 151\\\\\n", + "\t 186 & 80 & 165\\\\\n", + "\t 155 & 82 & 151\\\\\n", + "\t 114 & 89 & 128\\\\\n", + "\t 341 & 51 & 161\\\\\n", + "\t 340 & 59 & 146\\\\\n", + "\t 283 & 65 & 148\\\\\n", + "\t 267 & 74 & 144\\\\\n", + "\t 215 & 81 & 134\\\\\n", + "\t 148 & 86 & 127\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "loss | hardness | strength | \n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 372 | 45 | 162 | \n", + "| 206 | 55 | 233 | \n", + "| 175 | 61 | 232 | \n", + "| 154 | 66 | 231 | \n", + "| 136 | 71 | 231 | \n", + "| 112 | 71 | 237 | \n", + "| 55 | 81 | 224 | \n", + "| 45 | 86 | 219 | \n", + "| 221 | 53 | 203 | \n", + "| 166 | 60 | 189 | \n", + "| 164 | 64 | 210 | \n", + "| 113 | 68 | 210 | \n", + "| 82 | 79 | 196 | \n", + "| 32 | 81 | 180 | \n", + "| 228 | 56 | 200 | \n", + "| 196 | 68 | 173 | \n", + "| 128 | 75 | 188 | \n", + "| 97 | 83 | 161 | \n", + "| 64 | 88 | 119 | \n", + "| 249 | 59 | 161 | \n", + "| 219 | 71 | 151 | \n", + "| 186 | 80 | 165 | \n", + "| 155 | 82 | 151 | \n", + "| 114 | 89 | 128 | \n", + "| 341 | 51 | 161 | \n", + "| 340 | 59 | 146 | \n", + "| 283 | 65 | 148 | \n", + "| 267 | 74 | 144 | \n", + "| 215 | 81 | 134 | \n", + "| 148 | 86 | 127 | \n", + "\n", + "\n" + ], + "text/plain": [ + " loss hardness strength\n", + "1 372 45 162 \n", + "2 206 55 233 \n", + "3 175 61 232 \n", + "4 154 66 231 \n", + "5 136 71 231 \n", + "6 112 71 237 \n", + "7 55 81 224 \n", + "8 45 86 219 \n", + "9 221 53 203 \n", + "10 166 60 189 \n", + "11 164 64 210 \n", + "12 113 68 210 \n", + "13 82 79 196 \n", + "14 32 81 180 \n", + "15 228 56 200 \n", + "16 196 68 173 \n", + "17 128 75 188 \n", + "18 97 83 161 \n", + "19 64 88 119 \n", + "20 249 59 161 \n", + "21 219 71 151 \n", + "22 186 80 165 \n", + "23 155 82 151 \n", + "24 114 89 128 \n", + "25 341 51 161 \n", + "26 340 59 146 \n", + "27 283 65 148 \n", + "28 267 74 144 \n", + "29 215 81 134 \n", + "30 148 86 127 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the file.\n", + "rubber <- read.csv('rubber.csv')\n", + "rubber" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The figures below show scatterplots of abrasion loss against hardness and of abrasion loss against strength. (A scatterplot of abrasion loss against hardness also appeared in Solution 3.1.) Figure (a) suggests a strong decreasing linear relationship of abrasion loss with hardness. Figure (b) is much less indicative of any strong dependence of abrasion loss on tensile strength." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# ggplot(rubber, aes(x=hardness, y=loss)) + geom_point()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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JDEA6QjGSBJBEhHssrCymQvgVNNYWmyl8CrtLAm2UvgFPb0WUu4Z63/ICHkwQAJ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\nB1LuL6KOTt0hNBeQAElqcHAghZaeZbx438y6m9a++Ld/HbQ1F5A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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hardloss <- ggplot(rubber, aes(x=hardness, y=loss)) + geom_point()\n", + "strloss <- ggplot(rubber, aes(x=strength, y=loss)) + geom_point()\n", + "\n", + "# plot_grid(hardloss, strloss, labels = \"AUTO\")\n", + "\n", + "multiplot(hardloss, strloss)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In exercise 3.5(a) you obtained the following output for the regression of abrasion loss on hardness." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = loss ~ hardness, data = rubber)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-86.15 -46.77 -19.49 54.27 111.49 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 550.4151 65.7867 8.367 4.22e-09 ***\n", + "hardness -5.3366 0.9229 -5.782 3.29e-06 ***\n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 60.52 on 28 degrees of freedom\n", + "Multiple R-squared: 0.5442,\tAdjusted R-squared: 0.5279 \n", + "F-statistic: 33.43 on 1 and 28 DF, p-value: 3.294e-06\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
DfSum SqMean SqF valuePr(>F)
hardness 1 122455.0 122455.037 33.43276 3.294489e-06
Residuals28 102556.3 3662.726 NA NA
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllll}\n", + " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", + "\\hline\n", + "\thardness & 1 & 122455.0 & 122455.037 & 33.43276 & 3.294489e-06\\\\\n", + "\tResiduals & 28 & 102556.3 & 3662.726 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n", + "|---|---|\n", + "| hardness | 1 | 122455.0 | 122455.037 | 33.43276 | 3.294489e-06 | \n", + "| Residuals | 28 | 102556.3 | 3662.726 | NA | NA | \n", + "\n", + "\n" + ], + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "hardness 1 122455.0 122455.037 33.43276 3.294489e-06\n", + "Residuals 28 102556.3 3662.726 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Df Sum Sq Mean Sq F value Pr(>F) PctExp\n", + "hardness 1 122455.0 122455.037 33.43276 3.294489e-06 54.42171\n", + "Residuals 28 102556.3 3662.726 NA NA 45.57829\n" + ] + } + ], + "source": [ + "fit <- lm(loss ~ hardness, data = rubber)\n", + "summary(fit)\n", + "anova(fit)\n", + "af <- anova(fit)\n", + "afss <- af$\"Sum Sq\"\n", + "print(cbind(af,PctExp=afss/sum(afss)*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "solution": "shown", + "solution2": "hidden", + "solution2_first": true, + "solution_first": true + }, + "source": [ + "## Exercise\n", + "Now repeat the for the regression of abrasion loss on tensile strength." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solution" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "solution2": "hidden" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = loss ~ strength, data = rubber)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-155.640 -59.919 2.795 61.221 183.285 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 305.2248 79.9962 3.815 0.000688 ***\n", + "strength -0.7192 0.4347 -1.654 0.109232 \n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 85.56 on 28 degrees of freedom\n", + "Multiple R-squared: 0.08904,\tAdjusted R-squared: 0.0565 \n", + "F-statistic: 2.737 on 1 and 28 DF, p-value: 0.1092\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
DfSum SqMean SqF valuePr(>F)
strength 1 20034.7720034.7722.736769 0.1092317
Residuals28 204976.59 7320.593 NA NA
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllll}\n", + " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", + "\\hline\n", + "\tstrength & 1 & 20034.77 & 20034.772 & 2.736769 & 0.1092317\\\\\n", + "\tResiduals & 28 & 204976.59 & 7320.593 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n", + "|---|---|\n", + "| strength | 1 | 20034.77 | 20034.772 | 2.736769 | 0.1092317 | \n", + "| Residuals | 28 | 204976.59 | 7320.593 | NA | NA | \n", + "\n", + "\n" + ], + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "strength 1 20034.77 20034.772 2.736769 0.1092317\n", + "Residuals 28 204976.59 7320.593 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Df Sum Sq Mean Sq F value Pr(>F) PctExp\n", + "strength 1 20034.77 20034.772 2.736769 0.1092317 8.903893\n", + "Residuals 28 204976.59 7320.593 NA NA 91.096107\n" + ] + } + ], + "source": [ + "fit <- lm(loss ~ strength, data = rubber)\n", + "summary(fit)\n", + "anova(fit)\n", + "af <- anova(fit)\n", + "afss <- af$\"Sum Sq\"\n", + "print(cbind(af,PctExp=afss/sum(afss)*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "solution2": "hidden" + }, + "source": [ + "Individually, then, regression of abrasion loss on hardness seems satisfactory, albeit accounting for a disappointingly small percentage of the variance in the response; regression of abrasion loss on tensile strength, on the other hand, seems to have very little explanatory power. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "solution": "hidden" + }, + "source": [ + "However, back in Exercise 3.19 it was shown that the residuals from the former regression showed a clear relationship with the tensile strength values, and this suggested that a regression model involving both variables is necessary. \n", + "\n", + "Let us try it. The only change is to include `strength` in the equations being fitted in the `lm()` function." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = loss ~ hardness + strength, data = rubber)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-79.385 -14.608 3.816 19.755 65.981 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 885.1611 61.7516 14.334 3.84e-14 ***\n", + "hardness -6.5708 0.5832 -11.267 1.03e-11 ***\n", + "strength -1.3743 0.1943 -7.073 1.32e-07 ***\n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 36.49 on 27 degrees of freedom\n", + "Multiple R-squared: 0.8402,\tAdjusted R-squared: 0.8284 \n", + "F-statistic: 71 on 2 and 27 DF, p-value: 1.767e-11\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
DfSum SqMean SqF valuePr(>F)
hardness 1 122455.04 122455.037 91.96967 3.458255e-10
strength 1 66606.59 66606.586 50.02477 1.324645e-07
Residuals27 35949.74 1331.472 NA NA
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllll}\n", + " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", + "\\hline\n", + "\thardness & 1 & 122455.04 & 122455.037 & 91.96967 & 3.458255e-10\\\\\n", + "\tstrength & 1 & 66606.59 & 66606.586 & 50.02477 & 1.324645e-07\\\\\n", + "\tResiduals & 27 & 35949.74 & 1331.472 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n", + "|---|---|---|\n", + "| hardness | 1 | 122455.04 | 122455.037 | 91.96967 | 3.458255e-10 | \n", + "| strength | 1 | 66606.59 | 66606.586 | 50.02477 | 1.324645e-07 | \n", + "| Residuals | 27 | 35949.74 | 1331.472 | NA | NA | \n", + "\n", + "\n" + ], + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "hardness 1 122455.04 122455.037 91.96967 3.458255e-10\n", + "strength 1 66606.59 66606.586 50.02477 1.324645e-07\n", + "Residuals 27 35949.74 1331.472 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Df Sum Sq Mean Sq F value Pr(>F) PctExp\n", + "hardness 1 122455.04 122455.037 91.96967 3.458255e-10 54.42171\n", + "strength 1 66606.59 66606.586 50.02477 1.324645e-07 29.60143\n", + "Residuals 27 35949.74 1331.472 NA NA 15.97686\n" + ] + } + ], + "source": [ + "fit <- lm(loss ~ hardness + strength, data = rubber)\n", + "summary(fit)\n", + "anova(fit)\n", + "af <- anova(fit)\n", + "afss <- af$\"Sum Sq\"\n", + "print(cbind(af,PctExp=afss/sum(afss)*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the regression coefficient output next, the estimated model for the mean response is\n", + "\n", + "$$ \\hat{y} = \\hat{\\alpha} + \\hat{\\beta}_1 x_1 + \\hat{\\beta}_2 x_2 = 885.2 − 6.571 x_1 − 1.374 x_2 $$ \n", + "\n", + "where $x_1$ and $x_2$ stand for values of hardness and tensile strength, respectively. \n", + "\n", + "Associated with each estimated parameter, GenStat gives a standard error (details of the calculation of which need not concern us now) and hence a _t_-statistic (estimate divided by standard error) to be compared with the distribution on `d.f. (Residual)`=27 degrees of freedom. GenStat makes the comparison and gives _p_ values, which in this case are all very small, suggesting strong evidence for the non-zeroness (and hence presence) of each parameter, $\\alpha$, $\\beta_1$ and $\\beta_2$, individually." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, even though the single regression of abrasion loss on tensile strength did not suggest a close relationship, when taken in conjunction with the effect of hardness the effect of tensile strength is also a considerable one. A key idea here is that $\\beta_1$ and $\\beta_2$ (and more generally $\\beta_1, \\beta_2, \\ldots , \\beta_k$ in model (5.1)) are partial regression coefficients. That is, $\\beta_1$ measures the effect of an increase of one unit in $x_1$ treating the value of the other variable $x_2$ as fixed. Contrast this with the single regression model $E(Y) = \\alpha + \\beta_1 x_1$ in which $\\beta_1$ represents an increase of one unit in $x_1$ treating $x_2$ as zero. The meaning of $\\beta_1$ in the regression models with one and two explanatory variables is not the same.\n", + "\n", + "You will notice that the percentage of variance accounted for has increased dramatically in the two-explanatory-variable model to a much more respectable 82.8%. This statistic has the same interpretation — or difficulty of interpretation — as for the case of one explanatory variable (see [Unit 3](unit3.ipynb)).\n", + "\n", + "Other items in the GenStat output are as for the case of one explanatory variable: the `Standard error of observations` is $\\hat{\\sigma}$, and is the square root of `m.s. (Residual)`; the message about standardised residuals will be clarified below; and the message about leverage will be ignored until [Unit 10](unit10.ipynb).\n", + "\n", + "The simple residuals are, again, defined as the differences between the observed and predicted responses:\n", + "\n", + "$$ r_i = y_i - \\left( \\hat{\\alpha} + \\sum_{j=1}^{k} \\hat{\\beta}_j x_{i, j} \\right) , i = 1, 2, \\ldots, n. $$\n", + "\n", + "GenStat can obtain these for you and produce a plot of residuals against fitted values. The fitted values are simply $\\hat{Y}_i = \\hat{\\alpha} + \\sum_{j=1}^{k} \\hat{\\beta}_j x_{i, j}$. As in the regression models in [Unit 3](unit3.ipynb), the default in GenStat is to use deviance residuals which, in this context, are equivalent to standardised residuals (simple residuals divided by their estimated standard errors)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 5.2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "3.4.2" + }, + "toc": { + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "342px" + }, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.ipynb_checkpoints/section5.1solutions-checkpoint.ipynb b/.ipynb_checkpoints/section5.1solutions-checkpoint.ipynb new file mode 100644 index 0000000..9e446d1 --- /dev/null +++ b/.ipynb_checkpoints/section5.1solutions-checkpoint.ipynb @@ -0,0 +1,1557 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Section 5.1: Using the model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "## Imports and defintions" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "── Attaching packages ─────────────────────────────────────── tidyverse 1.2.1 ──\n", + "✔ ggplot2 2.2.1 ✔ purrr 0.2.4\n", + "✔ tibble 1.4.2 ✔ dplyr 0.7.4\n", + "✔ tidyr 0.8.0 ✔ stringr 1.2.0\n", + "✔ readr 1.1.1 ✔ forcats 0.2.0\n", + "── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n", + "✖ dplyr::filter() masks stats::filter()\n", + "✖ dplyr::lag() masks stats::lag()\n" + ] + } + ], + "source": [ + "library(tidyverse)\n", + "# library(cowplot)\n", + "library(repr)\n", + "library(ggfortify)\n", + "\n", + "# Change plot size to 4 x 3\n", + "options(repr.plot.width=6, repr.plot.height=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "# Multiple plot function\n", + "#\n", + "# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)\n", + "# - cols: Number of columns in layout\n", + "# - layout: A matrix specifying the layout. If present, 'cols' is ignored.\n", + "#\n", + "# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),\n", + "# then plot 1 will go in the upper left, 2 will go in the upper right, and\n", + "# 3 will go all the way across the bottom.\n", + "#\n", + "multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {\n", + " library(grid)\n", + "\n", + " # Make a list from the ... arguments and plotlist\n", + " plots <- c(list(...), plotlist)\n", + "\n", + " numPlots = length(plots)\n", + "\n", + " # If layout is NULL, then use 'cols' to determine layout\n", + " if (is.null(layout)) {\n", + " # Make the panel\n", + " # ncol: Number of columns of plots\n", + " # nrow: Number of rows needed, calculated from # of cols\n", + " layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),\n", + " ncol = cols, nrow = ceiling(numPlots/cols))\n", + " }\n", + "\n", + " if (numPlots==1) {\n", + " print(plots[[1]])\n", + "\n", + " } else {\n", + " # Set up the page\n", + " grid.newpage()\n", + " pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))\n", + "\n", + " # Make each plot, in the correct location\n", + " for (i in 1:numPlots) {\n", + " # Get the i,j matrix positions of the regions that contain this subplot\n", + " matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))\n", + "\n", + " print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,\n", + " layout.pos.col = matchidx$col))\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modelling abrasion loss\n", + "\n", + "This example concerns the dataset `rubber`, which you first met in Exercise 3.1. The experiment introduced in that exercise concerned an investigation of the resistance of rubber to abrasion (the abrasion loss) and how that resistance depended on various attributes of the rubber. In Exercise 3.1, the only explanatory variable we analysed was hardness; but in Exercise 3.19, a second explanatory variable, tensile strength, was taken into account too. There are 30 datapoints." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
losshardnessstrength
37245 162
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11271 237
5581 224
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21581 134
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\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lll}\n", + " loss & hardness & strength\\\\\n", + "\\hline\n", + "\t 372 & 45 & 162\\\\\n", + "\t 206 & 55 & 233\\\\\n", + "\t 175 & 61 & 232\\\\\n", + "\t 154 & 66 & 231\\\\\n", + "\t 136 & 71 & 231\\\\\n", + "\t 112 & 71 & 237\\\\\n", + "\t 55 & 81 & 224\\\\\n", + "\t 45 & 86 & 219\\\\\n", + "\t 221 & 53 & 203\\\\\n", + "\t 166 & 60 & 189\\\\\n", + "\t 164 & 64 & 210\\\\\n", + "\t 113 & 68 & 210\\\\\n", + "\t 82 & 79 & 196\\\\\n", + "\t 32 & 81 & 180\\\\\n", + "\t 228 & 56 & 200\\\\\n", + "\t 196 & 68 & 173\\\\\n", + "\t 128 & 75 & 188\\\\\n", + "\t 97 & 83 & 161\\\\\n", + "\t 64 & 88 & 119\\\\\n", + "\t 249 & 59 & 161\\\\\n", + "\t 219 & 71 & 151\\\\\n", + "\t 186 & 80 & 165\\\\\n", + "\t 155 & 82 & 151\\\\\n", + "\t 114 & 89 & 128\\\\\n", + "\t 341 & 51 & 161\\\\\n", + "\t 340 & 59 & 146\\\\\n", + "\t 283 & 65 & 148\\\\\n", + "\t 267 & 74 & 144\\\\\n", + "\t 215 & 81 & 134\\\\\n", + "\t 148 & 86 & 127\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "loss | hardness | strength | \n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 372 | 45 | 162 | \n", + "| 206 | 55 | 233 | \n", + "| 175 | 61 | 232 | \n", + "| 154 | 66 | 231 | \n", + "| 136 | 71 | 231 | \n", + "| 112 | 71 | 237 | \n", + "| 55 | 81 | 224 | \n", + "| 45 | 86 | 219 | \n", + "| 221 | 53 | 203 | \n", + "| 166 | 60 | 189 | \n", + "| 164 | 64 | 210 | \n", + "| 113 | 68 | 210 | \n", + "| 82 | 79 | 196 | \n", + "| 32 | 81 | 180 | \n", + "| 228 | 56 | 200 | \n", + "| 196 | 68 | 173 | \n", + "| 128 | 75 | 188 | \n", + "| 97 | 83 | 161 | \n", + "| 64 | 88 | 119 | \n", + "| 249 | 59 | 161 | \n", + "| 219 | 71 | 151 | \n", + "| 186 | 80 | 165 | \n", + "| 155 | 82 | 151 | \n", + "| 114 | 89 | 128 | \n", + "| 341 | 51 | 161 | \n", + "| 340 | 59 | 146 | \n", + "| 283 | 65 | 148 | \n", + "| 267 | 74 | 144 | \n", + "| 215 | 81 | 134 | \n", + "| 148 | 86 | 127 | \n", + "\n", + "\n" + ], + "text/plain": [ + " loss hardness strength\n", + "1 372 45 162 \n", + "2 206 55 233 \n", + "3 175 61 232 \n", + "4 154 66 231 \n", + "5 136 71 231 \n", + "6 112 71 237 \n", + "7 55 81 224 \n", + "8 45 86 219 \n", + "9 221 53 203 \n", + "10 166 60 189 \n", + "11 164 64 210 \n", + "12 113 68 210 \n", + "13 82 79 196 \n", + "14 32 81 180 \n", + "15 228 56 200 \n", + "16 196 68 173 \n", + "17 128 75 188 \n", + "18 97 83 161 \n", + "19 64 88 119 \n", + "20 249 59 161 \n", + "21 219 71 151 \n", + "22 186 80 165 \n", + "23 155 82 151 \n", + "24 114 89 128 \n", + "25 341 51 161 \n", + "26 340 59 146 \n", + "27 283 65 148 \n", + "28 267 74 144 \n", + "29 215 81 134 \n", + "30 148 86 127 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the data from the file.\n", + "rubber <- read.csv('rubber.csv')\n", + "rubber" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The figures below show scatterplots of abrasion loss against hardness and of abrasion loss against strength. (A scatterplot of abrasion loss against hardness also appeared in Solution 3.1.) Figure (a) suggests a strong decreasing linear relationship of abrasion loss with hardness. Figure (b) is much less indicative of any strong dependence of abrasion loss on tensile strength." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# ggplot(rubber, aes(x=hardness, y=loss)) + geom_point()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hardloss <- ggplot(rubber, aes(x=hardness, y=loss)) + geom_point()\n", + "strloss <- ggplot(rubber, aes(x=strength, y=loss)) + geom_point()\n", + "\n", + "# plot_grid(hardloss, strloss, labels = \"AUTO\")\n", + "\n", + "multiplot(hardloss, strloss, cols=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In exercise 3.5(a) you obtained the following output for the regression of abrasion loss on hardness." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = loss ~ hardness, data = rubber)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-86.15 -46.77 -19.49 54.27 111.49 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 550.4151 65.7867 8.367 4.22e-09 ***\n", + "hardness -5.3366 0.9229 -5.782 3.29e-06 ***\n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 60.52 on 28 degrees of freedom\n", + "Multiple R-squared: 0.5442,\tAdjusted R-squared: 0.5279 \n", + "F-statistic: 33.43 on 1 and 28 DF, p-value: 3.294e-06\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
DfSum SqMean SqF valuePr(>F)
hardness 1 122455.0 122455.037 33.43276 3.294489e-06
Residuals28 102556.3 3662.726 NA NA
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllll}\n", + " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", + "\\hline\n", + "\thardness & 1 & 122455.0 & 122455.037 & 33.43276 & 3.294489e-06\\\\\n", + "\tResiduals & 28 & 102556.3 & 3662.726 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n", + "|---|---|\n", + "| hardness | 1 | 122455.0 | 122455.037 | 33.43276 | 3.294489e-06 | \n", + "| Residuals | 28 | 102556.3 | 3662.726 | NA | NA | \n", + "\n", + "\n" + ], + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "hardness 1 122455.0 122455.037 33.43276 3.294489e-06\n", + "Residuals 28 102556.3 3662.726 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Df Sum Sq Mean Sq F value Pr(>F) PctExp\n", + "hardness 1 122455.0 122455.037 33.43276 3.294489e-06 54.42171\n", + "Residuals 28 102556.3 3662.726 NA NA 45.57829\n" + ] + } + ], + "source": [ + "fit <- lm(loss ~ hardness, data = rubber)\n", + "summary(fit)\n", + "anova(fit)\n", + "af <- anova(fit)\n", + "afss <- af$\"Sum Sq\"\n", + "print(cbind(af,PctExp=afss/sum(afss)*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "solution": "shown", + "solution2": "hidden", + "solution2_first": true, + "solution_first": true + }, + "source": [ + "### Exercise 5.1\n", + "Now repeat the for the regression of abrasion loss on tensile strength.\n", + "\n", + "Enter your solution in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your solution here" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "### Solution" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "hidden": true, + "solution2": "hidden" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = loss ~ strength, data = rubber)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-155.640 -59.919 2.795 61.221 183.285 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 305.2248 79.9962 3.815 0.000688 ***\n", + "strength -0.7192 0.4347 -1.654 0.109232 \n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 85.56 on 28 degrees of freedom\n", + "Multiple R-squared: 0.08904,\tAdjusted R-squared: 0.0565 \n", + "F-statistic: 2.737 on 1 and 28 DF, p-value: 0.1092\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
DfSum SqMean SqF valuePr(>F)
strength 1 20034.7720034.7722.736769 0.1092317
Residuals28 204976.59 7320.593 NA NA
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllll}\n", + " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", + "\\hline\n", + "\tstrength & 1 & 20034.77 & 20034.772 & 2.736769 & 0.1092317\\\\\n", + "\tResiduals & 28 & 204976.59 & 7320.593 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n", + "|---|---|\n", + "| strength | 1 | 20034.77 | 20034.772 | 2.736769 | 0.1092317 | \n", + "| Residuals | 28 | 204976.59 | 7320.593 | NA | NA | \n", + "\n", + "\n" + ], + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "strength 1 20034.77 20034.772 2.736769 0.1092317\n", + "Residuals 28 204976.59 7320.593 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Df Sum Sq Mean Sq F value Pr(>F) PctExp\n", + "strength 1 20034.77 20034.772 2.736769 0.1092317 8.903893\n", + "Residuals 28 204976.59 7320.593 NA NA 91.096107\n" + ] + } + ], + "source": [ + "fit <- lm(loss ~ strength, data = rubber)\n", + "summary(fit)\n", + "anova(fit)\n", + "af <- anova(fit)\n", + "afss <- af$\"Sum Sq\"\n", + "print(cbind(af,PctExp=afss/sum(afss)*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hidden": true, + "solution2": "hidden" + }, + "source": [ + "Individually, then, regression of abrasion loss on hardness seems satisfactory, albeit accounting for a disappointingly small percentage of the variance in the response; regression of abrasion loss on tensile strength, on the other hand, seems to have very little explanatory power. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "solution": "hidden" + }, + "source": [ + "### Multiple regression\n", + "However, back in Exercise 3.19 it was shown that the residuals from the former regression showed a clear relationship with the tensile strength values, and this suggested that a regression model involving both variables is necessary. \n", + "\n", + "Let us try it. The only change is to include `strength` in the equations being fitted in the `lm()` function. Instead of \n", + "\n", + "```\n", + "lm(loss ~ hardness, data = rubber)\n", + "```\n", + "use \n", + "```\n", + "lm(loss ~ hardness + strength, data = rubber)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = loss ~ hardness + strength, data = rubber)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-79.385 -14.608 3.816 19.755 65.981 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 885.1611 61.7516 14.334 3.84e-14 ***\n", + "hardness -6.5708 0.5832 -11.267 1.03e-11 ***\n", + "strength -1.3743 0.1943 -7.073 1.32e-07 ***\n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 36.49 on 27 degrees of freedom\n", + "Multiple R-squared: 0.8402,\tAdjusted R-squared: 0.8284 \n", + "F-statistic: 71 on 2 and 27 DF, p-value: 1.767e-11\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
DfSum SqMean SqF valuePr(>F)
hardness 1 122455.04 122455.037 91.96967 3.458255e-10
strength 1 66606.59 66606.586 50.02477 1.324645e-07
Residuals27 35949.74 1331.472 NA NA
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllll}\n", + " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", + "\\hline\n", + "\thardness & 1 & 122455.04 & 122455.037 & 91.96967 & 3.458255e-10\\\\\n", + "\tstrength & 1 & 66606.59 & 66606.586 & 50.02477 & 1.324645e-07\\\\\n", + "\tResiduals & 27 & 35949.74 & 1331.472 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n", + "|---|---|---|\n", + "| hardness | 1 | 122455.04 | 122455.037 | 91.96967 | 3.458255e-10 | \n", + "| strength | 1 | 66606.59 | 66606.586 | 50.02477 | 1.324645e-07 | \n", + "| Residuals | 27 | 35949.74 | 1331.472 | NA | NA | \n", + "\n", + "\n" + ], + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "hardness 1 122455.04 122455.037 91.96967 3.458255e-10\n", + "strength 1 66606.59 66606.586 50.02477 1.324645e-07\n", + "Residuals 27 35949.74 1331.472 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Df Sum Sq Mean Sq F value Pr(>F) PctExp\n", + "hardness 1 122455.04 122455.037 91.96967 3.458255e-10 54.42171\n", + "strength 1 66606.59 66606.586 50.02477 1.324645e-07 29.60143\n", + "Residuals 27 35949.74 1331.472 NA NA 15.97686\n" + ] + } + ], + "source": [ + "fit <- lm(loss ~ hardness + strength, data = rubber)\n", + "summary(fit)\n", + "anova(fit)\n", + "af <- anova(fit)\n", + "afss <- af$\"Sum Sq\"\n", + "print(cbind(af,PctExp=afss/sum(afss)*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the regression coefficient output next, the estimated model for the mean response is\n", + "\n", + "$$ \\hat{y} = \\hat{\\alpha} + \\hat{\\beta}_1 x_1 + \\hat{\\beta}_2 x_2 = 885.2 − 6.571 x_1 − 1.374 x_2 $$ \n", + "\n", + "where $x_1$ and $x_2$ stand for values of hardness and tensile strength, respectively. \n", + "\n", + "Associated with each estimated parameter, GenStat gives a standard error (details of the calculation of which need not concern us now) and hence a _t_-statistic (estimate divided by standard error) to be compared with the distribution on `d.f. (Residual)`=27 degrees of freedom. GenStat makes the comparison and gives _p_ values, which in this case are all very small, suggesting strong evidence for the non-zeroness (and hence presence) of each parameter, $\\alpha$, $\\beta_1$ and $\\beta_2$, individually." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, even though the single regression of abrasion loss on tensile strength did not suggest a close relationship, when taken in conjunction with the effect of hardness the effect of tensile strength is also a considerable one. A key idea here is that $\\beta_1$ and $\\beta_2$ (and more generally $\\beta_1, \\beta_2, \\ldots , \\beta_k$ in model (5.1)) are partial regression coefficients. That is, $\\beta_1$ measures the effect of an increase of one unit in $x_1$ treating the value of the other variable $x_2$ as fixed. Contrast this with the single regression model $E(Y) = \\alpha + \\beta_1 x_1$ in which $\\beta_1$ represents an increase of one unit in $x_1$ treating $x_2$ as zero. The meaning of $\\beta_1$ in the regression models with one and two explanatory variables is not the same.\n", + "\n", + "You will notice that the percentage of variance accounted for has increased dramatically in the two-explanatory-variable model to a much more respectable 82.8%. This statistic has the same interpretation — or difficulty of interpretation — as for the case of one explanatory variable (see [Unit 3](unit3.ipynb)).\n", + "\n", + "Other items in the GenStat output are as for the case of one explanatory variable: the `Standard error of observations` is $\\hat{\\sigma}$, and is the square root of `m.s. (Residual)`; the message about standardised residuals will be clarified below; and the message about leverage will be ignored until [Unit 10](unit10.ipynb).\n", + "\n", + "The simple residuals are, again, defined as the differences between the observed and predicted responses:\n", + "\n", + "$$ r_i = y_i - \\left( \\hat{\\alpha} + \\sum_{j=1}^{k} \\hat{\\beta}_j x_{i, j} \\right) ,\\ i = 1, 2, \\ldots, n. $$\n", + "\n", + "GenStat can obtain these for you and produce a plot of residuals against fitted values. The fitted values are simply $\\hat{Y}_i = \\hat{\\alpha} + \\sum_{j=1}^{k} \\hat{\\beta}_j x_{i, j}$. As in the regression models in [Unit 3](unit3.ipynb), the default in GenStat is to use deviance residuals which, in this context, are equivalent to standardised residuals (simple residuals divided by their estimated standard errors)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gratuitous example of more complex maths markup\n", + "If \n", + "\n", + "$$ \\rho(z) = \\rho_c \\exp \\left( - \\frac{z^2}{2H^2} \\right) $$\n", + "\n", + "then\n", + "\n", + "\\begin{eqnarray*}\n", + "\\frac{\\partial \\rho(z)}{\\partial z} & = & \\rho_c \\frac{\\partial}{\\partial z}\\exp \n", + " \\left( - \\frac{z^2}{2H^2} \\right) \\\\\n", + "\t& = & \\rho_c \\exp \\left( - \\frac{z^2}{2H^2} \\right) \\cdot \n", + "\t \\frac{\\partial}{\\partial z} \\left( - \\frac{z^2}{2H^2} \\right) \\\\\n", + "\t& = & \\rho_c \\exp \\left( - \\frac{z^2}{2H^2} \\right) \\cdot \n", + "\t - \\frac{z}{H^2} \\\\\n", + "\t& = & - \\frac{z}{H^2} \\rho(z) \n", + "\\end{eqnarray*}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 5.2\n", + "Something about plots of residuals..." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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VaHhIRYWlqaOxwATE2lUkVFRSUnJ7PZbG9vb29v7zIbVDhOVb+rIRYWFl5eXvpE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\nRK5CYII=", + "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fit <- lm(loss ~ hardness + strength, data = rubber)\n", + "autoplot(fit, label.size = 3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These plots are of just the same sort as those in [Unit 3](unit3.ipynb), and are interpreted in the same way. In this case, the normal plot and histogram show some slight suggestion of skewness in the residuals, but there is no obvious cause to doubt the model. In the regression output, GenStat flagged one of the points (number 19) as having a large standardised (i.e. deviance) residual. To decide which points to flag in this way, GenStat uses the same rules as described in [Units 3](unit3.ipynb) and [4](unit4.ipynb). In this case, it warned us about the most negative residual. However, the value of this standardised residual is not very large (at −2.38), and the plot of residuals against fitted values in Figure 5.2 makes it clear that this particular residual is not exceptionally large relative to some of the others, which are almost as large." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 5.2\n", + "The table below shows values for the first five datapoints, of 13, of a dataset concerned with predicting the heat evolved when cement sets via knowledge of its constituents. There are two explanatory variables, tricalcium aluminate (TA) and tricalcium silicate (TS), and the response variable is the heat generated in calories per gram (heat).\n", + "\n", + "heat | TA | TS\n", + "-----|----|-----\n", + "78.5 | 7 | 26\n", + "74.3 | 1 | 29\n", + "104.3 | 11 | 56\n", + "87.6 | 11 | 31\n", + "95.9 | 7 | 52\n", + " $\\vdots$ | $\\vdots$ | $\\vdots$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "a. Load the `cemheat` dataset.\n", + "\n", + "b. Make scatterplots of heat against each of TA and TS in turn, and comment on what you see.\n", + "\n", + "c. Use GenStat to fit each individual regression equation (of heat on TA and of heat on TS) in turn, and then to fit the regression equation with two explanatory variables. Does the latter regression equation give you a better model than either of the individual ones?\n", + "\n", + "d. According to the regression equation with two explanatory variables fitted in part (b), what is the predicted value of heat when TA = 15 and TS = 55?\n", + "\n", + "e. By looking at the (default) composite residual plots, comment on the appropriateness of the fitted regression model." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
heatTATS
78.5 7 26
74.3 1 29
104.311 56
87.611 31
95.9 7 52
109.211 55
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lll}\n", + " heat & TA & TS\\\\\n", + "\\hline\n", + "\t 78.5 & 7 & 26 \\\\\n", + "\t 74.3 & 1 & 29 \\\\\n", + "\t 104.3 & 11 & 56 \\\\\n", + "\t 87.6 & 11 & 31 \\\\\n", + "\t 95.9 & 7 & 52 \\\\\n", + "\t 109.2 & 11 & 55 \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "heat | TA | TS | \n", + "|---|---|---|---|---|---|\n", + "| 78.5 | 7 | 26 | \n", + "| 74.3 | 1 | 29 | \n", + "| 104.3 | 11 | 56 | \n", + "| 87.6 | 11 | 31 | \n", + "| 95.9 | 7 | 52 | \n", + "| 109.2 | 11 | 55 | \n", + "\n", + "\n" + ], + "text/plain": [ + " heat TA TS\n", + "1 78.5 7 26\n", + "2 74.3 1 29\n", + "3 104.3 11 56\n", + "4 87.6 11 31\n", + "5 95.9 7 52\n", + "6 109.2 11 55" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cemheat <- read.csv('cemheat.csv')\n", + "head(cemheat)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "taheat <- ggplot(cemheat, aes(x=TA, y=heat)) + geom_point()\n", + "tsheat <- ggplot(cemheat, aes(x=TS, y=heat)) + geom_point()\n", + "\n", + "# plot_grid(hardloss, strloss, labels = \"AUTO\")\n", + "\n", + "options(repr.plot.width=6, repr.plot.height=4)\n", + "multiplot(taheat, tsheat, cols=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = heat ~ TA, data = cemheat)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-16.061 -9.048 1.339 7.883 15.614 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 81.4793 4.9273 16.54 4.07e-09 ***\n", + "TA 1.8687 0.5264 3.55 0.00455 ** \n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 10.73 on 11 degrees of freedom\n", + "Multiple R-squared: 0.5339,\tAdjusted R-squared: 0.4916 \n", + "F-statistic: 12.6 on 1 and 11 DF, p-value: 0.004552\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
DfSum SqMean SqF valuePr(>F)
TA 1 1450.076 1450.0763 12.60252 0.004552045
Residuals11 1265.687 115.0624 NA NA
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllll}\n", + " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", + "\\hline\n", + "\tTA & 1 & 1450.076 & 1450.0763 & 12.60252 & 0.004552045\\\\\n", + "\tResiduals & 11 & 1265.687 & 115.0624 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n", + "|---|---|\n", + "| TA | 1 | 1450.076 | 1450.0763 | 12.60252 | 0.004552045 | \n", + "| Residuals | 11 | 1265.687 | 115.0624 | NA | NA | \n", + "\n", + "\n" + ], + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "TA 1 1450.076 1450.0763 12.60252 0.004552045\n", + "Residuals 11 1265.687 115.0624 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Df Sum Sq Mean Sq F value Pr(>F) PctExp\n", + "TA 1 1450.076 1450.0763 12.60252 0.004552045 53.3948\n", + "Residuals 11 1265.687 115.0624 NA NA 46.6052\n" + ] + } + ], + "source": [ + "fit <- lm(heat ~ TA, data = cemheat)\n", + "summary(fit)\n", + "anova(fit)\n", + "af <- anova(fit)\n", + "afss <- af$\"Sum Sq\"\n", + "print(cbind(af,PctExp=afss/sum(afss)*100))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = heat ~ TS, data = cemheat)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-10.752 -6.008 -1.684 3.794 21.387 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 57.4237 8.4906 6.763 3.1e-05 ***\n", + "TS 0.7891 0.1684 4.686 0.000665 ***\n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 9.077 on 11 degrees of freedom\n", + "Multiple R-squared: 0.6663,\tAdjusted R-squared: 0.6359 \n", + "F-statistic: 21.96 on 1 and 11 DF, p-value: 0.0006648\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
DfSum SqMean SqF valuePr(>F)
TS 1 1809.4267 1809.42673 21.9606 0.0006648249
Residuals11 906.3363 82.39421 NA NA
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllll}\n", + " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", + "\\hline\n", + "\tTS & 1 & 1809.4267 & 1809.42673 & 21.9606 & 0.0006648249\\\\\n", + "\tResiduals & 11 & 906.3363 & 82.39421 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n", + "|---|---|\n", + "| TS | 1 | 1809.4267 | 1809.42673 | 21.9606 | 0.0006648249 | \n", + "| Residuals | 11 | 906.3363 | 82.39421 | NA | NA | \n", + "\n", + "\n" + ], + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "TS 1 1809.4267 1809.42673 21.9606 0.0006648249\n", + "Residuals 11 906.3363 82.39421 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Df Sum Sq Mean Sq F value Pr(>F) PctExp\n", + "TS 1 1809.4267 1809.42673 21.9606 0.0006648249 66.62683\n", + "Residuals 11 906.3363 82.39421 NA NA 33.37317\n" + ] + } + ], + "source": [ + "fit <- lm(heat ~ TS, data = cemheat)\n", + "summary(fit)\n", + "anova(fit)\n", + "af <- anova(fit)\n", + "afss <- af$\"Sum Sq\"\n", + "print(cbind(af,PctExp=afss/sum(afss)*100))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = heat ~ TA + TS, data = cemheat)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-2.893 -1.574 -1.302 1.363 4.048 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 52.57735 2.28617 23.00 5.46e-10 ***\n", + "TA 1.46831 0.12130 12.11 2.69e-07 ***\n", + "TS 0.66225 0.04585 14.44 5.03e-08 ***\n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 2.406 on 10 degrees of freedom\n", + "Multiple R-squared: 0.9787,\tAdjusted R-squared: 0.9744 \n", + "F-statistic: 229.5 on 2 and 10 DF, p-value: 4.407e-09\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
DfSum SqMean SqF valuePr(>F)
TA 1 1450.07633 1450.076328 250.4256 2.088092e-08
TS 1 1207.78227 1207.782266 208.5818 5.028960e-08
Residuals10 57.90448 5.790448 NA NA
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllll}\n", + " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", + "\\hline\n", + "\tTA & 1 & 1450.07633 & 1450.076328 & 250.4256 & 2.088092e-08\\\\\n", + "\tTS & 1 & 1207.78227 & 1207.782266 & 208.5818 & 5.028960e-08\\\\\n", + "\tResiduals & 10 & 57.90448 & 5.790448 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n", + "|---|---|---|\n", + "| TA | 1 | 1450.07633 | 1450.076328 | 250.4256 | 2.088092e-08 | \n", + "| TS | 1 | 1207.78227 | 1207.782266 | 208.5818 | 5.028960e-08 | \n", + "| Residuals | 10 | 57.90448 | 5.790448 | NA | NA | \n", + "\n", + "\n" + ], + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "TA 1 1450.07633 1450.076328 250.4256 2.088092e-08\n", + "TS 1 1207.78227 1207.782266 208.5818 5.028960e-08\n", + "Residuals 10 57.90448 5.790448 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Df Sum Sq Mean Sq F value Pr(>F) PctExp\n", + "TA 1 1450.07633 1450.076328 250.4256 2.088092e-08 53.394802\n", + "TS 1 1207.78227 1207.782266 208.5818 5.028960e-08 44.473035\n", + "Residuals 10 57.90448 5.790448 NA NA 2.132163\n" + ] + } + ], + "source": [ + "fit <- lm(heat ~ TA + TS, data = cemheat)\n", + "summary(fit)\n", + "anova(fit)\n", + "af <- anova(fit)\n", + "afss <- af$\"Sum Sq\"\n", + "print(cbind(af,PctExp=afss/sum(afss)*100))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "1: 111.025712035428" + ], + "text/latex": [ + "\\textbf{1:} 111.025712035428" + ], + "text/markdown": [ + "**1:** 111.025712035428" + ], + "text/plain": [ + " 1 \n", + "111.0257 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "predict(fit, data.frame(\"TA\" = 15, \"TS\" = 55))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 5.3: Fitting a quadratic regression model\n", + "In Unit 3, the dataset `anaerob` was briefly considered (Example 3.1) and swiftly dismissed as a candidate for simple linear regression modelling. The reason is clear from the figure below, in which the response variable, expired ventilation ($y$), is plotted against the single explanatory variable, oxygen uptake ($x$). (The same plot appeared as Figure 3.1 in Example 3.1.) A model quadratic in $x$, i.e. $E(Y) = \\alpha + \\beta x + \\gamma x^2$, was suggested instead." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "anaerobic <- read.csv('anaerob.csv')\n", + "ggplot(anaerobic, aes(x=oxygen, y=ventil)) + geom_point()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since this quadratic model is nonetheless linear in its parameters, we can fit it using multiple regression of $y$ on $x$ plus the new variable $x^2$. Even though $x_1 = x$ and $x_2 = x^2$ are closely related, there is no immediate impediment to forgetting this and regressing on them in the usual way. A variable such as $x_2$ is sometimes called a derived variable.\n", + "\n", + "a. Using GenStat, perform the regression of expired ventilation (`ventil`) on oxygen uptake (`oxygen`). Are you at all surprised by how good this regression model seems?\n", + "\n", + "b. Now form a new variable `oxy2`, say, by squaring oxygen. (Create a new column in the `anearobic` dataframe which is `anaerobic$oxygen ^ 2`.) Perform the regression of ventil on `oxygen` and `oxy2`. Comment on the fit of this model according to the printed output (and with recourse to Figure 3.2 in Example 3.1).\n", + "\n", + "c. Make the usual residual plots and comment on the fit of the model again." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ventil ~ oxygen, data = anaerobic)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-15.502 -9.716 -3.391 7.881 26.446 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) -18.448734 3.815196 -4.836 1.26e-05 ***\n", + "oxygen 0.031141 0.001355 22.987 < 2e-16 ***\n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 11.96 on 51 degrees of freedom\n", + "Multiple R-squared: 0.912,\tAdjusted R-squared: 0.9103 \n", + "F-statistic: 528.4 on 1 and 51 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
DfSum SqMean SqF valuePr(>F)
oxygen 1 75555.216 75555.2158 528.403 1.419964e-28
Residuals51 7292.381 142.9879 NA NA
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllll}\n", + " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", + "\\hline\n", + "\toxygen & 1 & 75555.216 & 75555.2158 & 528.403 & 1.419964e-28\\\\\n", + "\tResiduals & 51 & 7292.381 & 142.9879 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n", + "|---|---|\n", + "| oxygen | 1 | 75555.216 | 75555.2158 | 528.403 | 1.419964e-28 | \n", + "| Residuals | 51 | 7292.381 | 142.9879 | NA | NA | \n", + "\n", + "\n" + ], + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "oxygen 1 75555.216 75555.2158 528.403 1.419964e-28\n", + "Residuals 51 7292.381 142.9879 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Df Sum Sq Mean Sq F value Pr(>F) PctExp\n", + "oxygen 1 75555.216 75555.2158 528.403 1.419964e-28 91.197836\n", + "Residuals 51 7292.381 142.9879 NA NA 8.802164\n" + ] + } + ], + "source": [ + "fit <- lm(ventil ~ oxygen, data = anaerobic)\n", + "summary(fit)\n", + "anova(fit)\n", + "af <- anova(fit)\n", + "afss <- af$\"Sum Sq\"\n", + "print(cbind(af,PctExp=afss/sum(afss)*100))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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np6U6dOnTp1qpWVlYaGxvLlywEAGhoa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\nizVr1qx79+4hh0+fPp0+fXpjY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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "layout(matrix(1:4,2,2)) \n", + "autoplot(fit)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
ventiloxygenoxy2
21.9 574 329476
18.6 592 350464
18.6 664 440896
19.1 667 444889
19.2 718 515524
16.9 770 592900
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lll}\n", + " ventil & oxygen & oxy2\\\\\n", + "\\hline\n", + "\t 21.9 & 574 & 329476\\\\\n", + "\t 18.6 & 592 & 350464\\\\\n", + "\t 18.6 & 664 & 440896\\\\\n", + "\t 19.1 & 667 & 444889\\\\\n", + "\t 19.2 & 718 & 515524\\\\\n", + "\t 16.9 & 770 & 592900\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "ventil | oxygen | oxy2 | \n", + "|---|---|---|---|---|---|\n", + "| 21.9 | 574 | 329476 | \n", + "| 18.6 | 592 | 350464 | \n", + "| 18.6 | 664 | 440896 | \n", + "| 19.1 | 667 | 444889 | \n", + "| 19.2 | 718 | 515524 | \n", + "| 16.9 | 770 | 592900 | \n", + "\n", + "\n" + ], + "text/plain": [ + " ventil oxygen oxy2 \n", + "1 21.9 574 329476\n", + "2 18.6 592 350464\n", + "3 18.6 664 440896\n", + "4 19.1 667 444889\n", + "5 19.2 718 515524\n", + "6 16.9 770 592900" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "anaerobic$oxy2 <- anaerobic$oxygen^2\n", + "head(anaerobic)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ventil ~ oxygen + oxy2, data = anaerobic)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-9.4713 -1.3675 -0.4201 2.1925 7.7817 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 2.427e+01 1.940e+00 12.509 < 2e-16 ***\n", + "oxygen -1.344e-02 1.762e-03 -7.628 6.27e-10 ***\n", + "oxy2 8.902e-06 3.444e-07 25.850 < 2e-16 ***\n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 3.186 on 50 degrees of freedom\n", + "Multiple R-squared: 0.9939,\tAdjusted R-squared: 0.9936 \n", + "F-statistic: 4055 on 2 and 50 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
DfSum SqMean SqF valuePr(>F)
oxygen 1 75555.2158 75555.21580 7441.5695 4.587875e-56
oxy2 1 6784.7247 6784.72473 668.2398 1.357823e-30
Residuals50 507.6565 10.15313 NA NA
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllll}\n", + " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", + "\\hline\n", + "\toxygen & 1 & 75555.2158 & 75555.21580 & 7441.5695 & 4.587875e-56\\\\\n", + "\toxy2 & 1 & 6784.7247 & 6784.72473 & 668.2398 & 1.357823e-30\\\\\n", + "\tResiduals & 50 & 507.6565 & 10.15313 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n", + "|---|---|---|\n", + "| oxygen | 1 | 75555.2158 | 75555.21580 | 7441.5695 | 4.587875e-56 | \n", + "| oxy2 | 1 | 6784.7247 | 6784.72473 | 668.2398 | 1.357823e-30 | \n", + "| Residuals | 50 | 507.6565 | 10.15313 | NA | NA | \n", + "\n", + "\n" + ], + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "oxygen 1 75555.2158 75555.21580 7441.5695 4.587875e-56\n", + "oxy2 1 6784.7247 6784.72473 668.2398 1.357823e-30\n", + "Residuals 50 507.6565 10.15313 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Df Sum Sq Mean Sq F value Pr(>F) PctExp\n", + "oxygen 1 75555.2158 75555.21580 7441.5695 4.587875e-56 91.1978362\n", + "oxy2 1 6784.7247 6784.72473 668.2398 1.357823e-30 8.1894044\n", + "Residuals 50 507.6565 10.15313 NA NA 0.6127594\n" + ] + } + ], + "source": [ + "fit <- lm(ventil ~ oxygen + oxy2, data = anaerobic)\n", + "summary(fit)\n", + "anova(fit)\n", + "af <- anova(fit)\n", + "afss <- af$\"Sum Sq\"\n", + "print(cbind(af,PctExp=afss/sum(afss)*100))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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jgQiK7D77//TlsbkPLy8lOnTk2bNq3DdFi2bBn9eNeuXRUVFbdu3erXrx89mJyc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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "layout(matrix(1:4,2,2)) \n", + "autoplot(fit)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "3.4.2" + }, + "toc": { + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "342px" + }, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.ipynb_checkpoints/unit10-checkpoint.ipynb b/.ipynb_checkpoints/unit10-checkpoint.ipynb new file mode 100644 index 0000000..92dea90 --- /dev/null +++ b/.ipynb_checkpoints/unit10-checkpoint.ipynb @@ -0,0 +1,39 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Unit 10\n", + "\n", + "Sample text for unit 10 goes here\n", + "\n", + "Back to [section 5.1](section5.1.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "3.4.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/.ipynb_checkpoints/unit3-checkpoint.ipynb b/.ipynb_checkpoints/unit3-checkpoint.ipynb new file mode 100644 index 0000000..2fd6442 --- /dev/null +++ b/.ipynb_checkpoints/unit3-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/accidents-regression.ipynb b/accidents-regression.ipynb new file mode 100644 index 0000000..56a78c4 --- /dev/null +++ b/accidents-regression.ipynb @@ -0,0 +1,1554 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "library(tidyverse)\n", + "library(mongolite)\n", + "library(lubridate)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "library(mongolite)\n", + "accidents <- mongo(\"accidents\", db='accidents', url=\"mongodb://localhost\")\n", + "roads <- mongo(\"roads\", db='accidents', url=\"mongodb://localhost\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " 'accidents' \n", + " $aggregate(pipeline = \"{}\", options = \"{\\\"allowDiskUse\\\":true}\", handler = NULL, pagesize = 1000) \n", + " $count(query = \"{}\") \n", + " $distinct(key, query = \"{}\") \n", + " $drop() \n", + " $export(con = stdout(), bson = FALSE) \n", + " $find(query = \"{}\", fields = \"{\\\"_id\\\":0}\", sort = \"{}\", skip = 0, limit = 0, handler = NULL, pagesize = 1000) \n", + " $import(con, bson = FALSE) \n", + " $index(add = NULL, remove = NULL) \n", + " $info() \n", + " $insert(data, pagesize = 1000, stop_on_error = TRUE, ...) \n", + " $iterate(query = \"{}\", fields = \"{\\\"_id\\\":0}\", sort = \"{}\", skip = 0, limit = 0) \n", + " $mapreduce(map, reduce, query = \"{}\", sort = \"{}\", limit = 0, out = NULL, scope = NULL) \n", + " $remove(query, just_one = FALSE) \n", + " $rename(name, db = NULL) \n", + " $replace(query, update = \"{}\", upsert = FALSE) \n", + " $update(query, update = \"{\\\"$set\\\":{}}\", filters = NULL, upsert = FALSE, multiple = FALSE) " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "accidents" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "1355615" + ], + "text/latex": [ + "1355615" + ], + "text/markdown": [ + "1355615" + ], + "text/plain": [ + "[1] 1355615" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "accidents$count('{}')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "292738" + ], + "text/latex": [ + "292738" + ], + "text/markdown": [ + "292738" + ], + "text/plain": [ + "[1] 292738" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "roads$count('{}')" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "$`1st_Road_Class`\n", + "[1] 3\n", + "\n", + "$`1st_Road_Number`\n", + "[1] 3218\n", + "\n", + "$`2nd_Road_Class`\n", + "[1] -1\n", + "\n", + "$`2nd_Road_Number`\n", + "[1] 0\n", + "\n", + "$Accident_Index\n", + "[1] \"200501BS00001\"\n", + "\n", + "$Accident_Severity\n", + "[1] 2\n", + "\n", + "$Carriageway_Hazards\n", + "[1] 0\n", + "\n", + "$Casualties\n", + "$Casualties[[1]]\n", + "$Casualties[[1]]$Vehicle_Reference\n", + "[1] 1\n", + "\n", + "$Casualties[[1]]$Casualty_Reference\n", + "[1] 1\n", + "\n", + "$Casualties[[1]]$Casualty_Class\n", + "[1] 3\n", + "\n", + "$Casualties[[1]]$Sex_of_Casualty\n", + "[1] 1\n", + "\n", + "$Casualties[[1]]$Age_Band_of_Casualty\n", + "[1] 7\n", + "\n", + "$Casualties[[1]]$Casualty_Severity\n", + "[1] 2\n", + "\n", + "$Casualties[[1]]$Pedestrian_Location\n", + "[1] 1\n", + "\n", + "$Casualties[[1]]$Pedestrian_Movement\n", + "[1] 1\n", + "\n", + "$Casualties[[1]]$Car_Passenger\n", + "[1] 0\n", + "\n", + "$Casualties[[1]]$Bus_or_Coach_Passenger\n", + "[1] 0\n", + "\n", + "$Casualties[[1]]$Pedestrian_Road_Maintenance_Worker\n", + "[1] -1\n", + "\n", + "$Casualties[[1]]$Casualty_Type\n", + "[1] 0\n", + "\n", + "$Casualties[[1]]$Casualty_Home_Area_Type\n", + "[1] 1\n", + "\n", + "\n", + "\n", + "$Date\n", + "[1] \"04/01/2005\"\n", + "\n", + "$Datetime\n", + "[1] \"2005-01-04 17:42:00 GMT\"\n", + "\n", + "$Day_of_Week\n", + "[1] 3\n", + "\n", + "$Did_Police_Officer_Attend_Scene_of_Accident\n", + "[1] 1\n", + "\n", + "$Junction_Control\n", + "[1] -1\n", + "\n", + "$Junction_Detail\n", + "[1] 0\n", + "\n", + "$LSOA_of_Accident_Location\n", + "[1] \"E01002849\"\n", + "\n", + "$Latitude\n", + "[1] 51.4891\n", + "\n", + "$Light_Conditions\n", + "[1] 1\n", + "\n", + "$`Local_Authority_(District)`\n", + "[1] 12\n", + "\n", + "$`Local_Authority_(Highway)`\n", + "[1] \"E09000020\"\n", + "\n", + "$Location_Easting_OSGR\n", + "[1] 525680\n", + "\n", + "$Location_Northing_OSGR\n", + "[1] 178240\n", + "\n", + "$Longitude\n", + "[1] -0.19117\n", + "\n", + "$Number_of_Casualties\n", + "[1] 1\n", + "\n", + "$Number_of_Vehicles\n", + "[1] 1\n", + "\n", + "$`Pedestrian_Crossing-Human_Control`\n", + "[1] 0\n", + "\n", + "$`Pedestrian_Crossing-Physical_Facilities`\n", + "[1] 1\n", + "\n", + "$Police_Force\n", + "[1] 1\n", + "\n", + "$Road_Surface_Conditions\n", + "[1] 2\n", + "\n", + "$Road_Type\n", + "[1] 6\n", + "\n", + "$Special_Conditions_at_Site\n", + "[1] 0\n", + "\n", + "$Speed_limit\n", + "[1] 30\n", + "\n", + "$Time\n", + "[1] \"17:42\"\n", + "\n", + "$Urban_or_Rural_Area\n", + "[1] 1\n", + "\n", + "$Vehicles\n", + "$Vehicles[[1]]\n", + "$Vehicles[[1]]$Vehicle_Reference\n", + "[1] 1\n", + "\n", + "$Vehicles[[1]]$Vehicle_Type\n", + "[1] 9\n", + "\n", + "$Vehicles[[1]]$Towing_and_Articulation\n", + "[1] 0\n", + "\n", + "$Vehicles[[1]]$Vehicle_Manoeuvre\n", + "[1] 18\n", + "\n", + "$Vehicles[[1]]$`Vehicle_Location-Restricted_Lane`\n", + "[1] 0\n", + "\n", + "$Vehicles[[1]]$Junction_Location\n", + "[1] 0\n", + "\n", + "$Vehicles[[1]]$Skidding_and_Overturning\n", + "[1] 0\n", + "\n", + "$Vehicles[[1]]$Hit_Object_in_Carriageway\n", + "[1] 0\n", + "\n", + "$Vehicles[[1]]$Vehicle_Leaving_Carriageway\n", + "[1] 0\n", + "\n", + "$Vehicles[[1]]$Hit_Object_off_Carriageway\n", + "[1] 0\n", + "\n", + "$Vehicles[[1]]$`1st_Point_of_Impact`\n", + "[1] 1\n", + "\n", + "$Vehicles[[1]]$`Was_Vehicle_Left_Hand_Drive?`\n", + "[1] 1\n", + "\n", + "$Vehicles[[1]]$Journey_Purpose_of_Driver\n", + "[1] 15\n", + "\n", + "$Vehicles[[1]]$Sex_of_Driver\n", + "[1] 2\n", + "\n", + "$Vehicles[[1]]$Age_Band_of_Driver\n", + "[1] 10\n", + "\n", + "$Vehicles[[1]]$`Engine_Capacity_(CC)`\n", + "[1] -1\n", + "\n", + "$Vehicles[[1]]$Propulsion_Code\n", + "[1] -1\n", + "\n", + "$Vehicles[[1]]$Age_of_Vehicle\n", + "[1] -1\n", + "\n", + "$Vehicles[[1]]$Driver_IMD_Decile\n", + "[1] 7\n", + "\n", + "$Vehicles[[1]]$Driver_Home_Area_Type\n", + "[1] 1\n", + "\n", + "\n", + "\n", + "$Weather_Conditions\n", + "[1] 2\n", + "\n", + "$loc\n", + "$loc$type\n", + "[1] \"Point\"\n", + "\n", + "$loc$coordinates\n", + "$loc$coordinates[[1]]\n", + "[1] -0.19117\n", + "\n", + "$loc$coordinates[[2]]\n", + "[1] 51.4891\n", + "\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "accidents$iterate()$one()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + 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Datetime_id
2005-01-04 17:42:00 52a9c93692c4e16686bebba9
2005-01-05 17:36:00 52a9c93692c4e16686bebbaa
2005-01-07 10:35:00 52a9c93692c4e16686bebbac
2005-01-06 00:15:00 52a9c93692c4e16686bebbab
2005-01-10 21:13:00 52a9c93692c4e16686bebbad
2005-01-11 12:40:00 52a9c93692c4e16686bebbae
2005-01-13 20:40:00 52a9c93692c4e16686bebbaf
2005-01-14 17:35:00 52a9c93692c4e16686bebbb0
2005-01-15 22:43:00 52a9c93692c4e16686bebbb1
2005-01-15 16:00:00 52a9c93692c4e16686bebbb2
2005-01-16 00:42:00 52a9c93692c4e16686bebbb3
2005-01-25 20:48:00 52a9c93692c4e16686bebbb4
2005-01-11 12:55:00 52a9c93692c4e16686bebbb5
2005-01-18 05:01:00 52a9c93692c4e16686bebbb6
2005-01-18 11:15:00 52a9c93692c4e16686bebbb7
2005-01-18 10:50:00 52a9c93692c4e16686bebbb8
2005-01-20 00:15:00 52a9c93692c4e16686bebbb9
2005-01-21 09:15:00 52a9c93692c4e16686bebbba
2005-01-21 21:16:00 52a9c93692c4e16686bebbbb
2005-01-08 03:00:00 52a9c93692c4e16686bebbbc
2005-01-24 21:45:00 52a9c93692c4e16686bebbbd
2005-01-19 16:35:00 52a9c93692c4e16686bebbc2
2005-01-24 17:05:00 52a9c93692c4e16686bebbbe
2005-01-24 21:30:00 52a9c93692c4e16686bebbbf
2005-01-18 17:25:00 52a9c93692c4e16686bebbc0
2005-01-29 07:34:00 52a9c93692c4e16686bebbc1
2005-01-30 20:00:00 52a9c93692c4e16686bebbc3
2005-01-29 13:15:00 52a9c93692c4e16686bebbc4
2005-02-01 18:20:00 52a9c93692c4e16686bebbc5
2005-02-02 07:25:00 52a9c93692c4e16686bebbc6
⋮⋮
2005-02-24 09:15:00 52a9c93692c4e16686bebbef
2005-02-25 00:11:00 52a9c93692c4e16686bebbf0
2005-02-23 19:00:00 52a9c93692c4e16686bebbf1
2005-02-26 01:48:00 52a9c93692c4e16686bebbf2
2005-02-23 07:58:00 52a9c93692c4e16686bebbf3
2005-02-25 14:55:00 52a9c93692c4e16686bebbf4
2005-02-25 08:22:00 52a9c93692c4e16686bebbf5
2005-02-25 04:38:00 52a9c93692c4e16686bebbf6
2005-03-04 13:20:00 52a9c93692c4e16686bebbf7
2005-03-01 00:30:00 52a9c93692c4e16686bebbf8
2005-02-28 19:30:00 52a9c93692c4e16686bebbf9
2005-03-01 12:03:00 52a9c93692c4e16686bebbfa
2005-02-14 23:03:00 52a9c93692c4e16686bebbfb
2005-02-02 22:19:00 52a9c93692c4e16686bebbfc
2005-03-03 14:30:00 52a9c93692c4e16686bebbfd
2005-03-01 19:15:00 52a9c93692c4e16686bebbfe
2005-03-01 18:31:00 52a9c93692c4e16686bebbff
2005-03-05 02:58:00 52a9c93692c4e16686bebc00
2005-03-01 10:40:00 52a9c93692c4e16686bebc01
2005-03-08 23:40:00 52a9c93692c4e16686bebc02
2005-03-03 21:07:00 52a9c93692c4e16686bebc03
2005-03-05 18:40:00 52a9c93692c4e16686bebc04
2005-03-05 16:55:00 52a9c93692c4e16686bebc05
2005-03-08 13:36:00 52a9c93692c4e16686bebc06
2005-03-06 01:05:00 52a9c93692c4e16686bebc07
2005-03-08 12:20:00 52a9c93692c4e16686bebc08
2005-03-14 17:35:00 52a9c93692c4e16686bebc0c
2005-03-08 08:56:00 52a9c93692c4e16686bebc09
2005-03-09 16:55:00 52a9c93692c4e16686bebc0a
2005-03-09 15:35:00 52a9c93692c4e16686bebc0b
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52a9c93692c4e16686bebbef\\\\\n", + "\t 2005-02-25 00:11:00 & 52a9c93692c4e16686bebbf0\\\\\n", + "\t 2005-02-23 19:00:00 & 52a9c93692c4e16686bebbf1\\\\\n", + "\t 2005-02-26 01:48:00 & 52a9c93692c4e16686bebbf2\\\\\n", + "\t 2005-02-23 07:58:00 & 52a9c93692c4e16686bebbf3\\\\\n", + "\t 2005-02-25 14:55:00 & 52a9c93692c4e16686bebbf4\\\\\n", + "\t 2005-02-25 08:22:00 & 52a9c93692c4e16686bebbf5\\\\\n", + "\t 2005-02-25 04:38:00 & 52a9c93692c4e16686bebbf6\\\\\n", + "\t 2005-03-04 13:20:00 & 52a9c93692c4e16686bebbf7\\\\\n", + "\t 2005-03-01 00:30:00 & 52a9c93692c4e16686bebbf8\\\\\n", + "\t 2005-02-28 19:30:00 & 52a9c93692c4e16686bebbf9\\\\\n", + "\t 2005-03-01 12:03:00 & 52a9c93692c4e16686bebbfa\\\\\n", + "\t 2005-02-14 23:03:00 & 52a9c93692c4e16686bebbfb\\\\\n", + "\t 2005-02-02 22:19:00 & 52a9c93692c4e16686bebbfc\\\\\n", + "\t 2005-03-03 14:30:00 & 52a9c93692c4e16686bebbfd\\\\\n", + "\t 2005-03-01 19:15:00 & 52a9c93692c4e16686bebbfe\\\\\n", + "\t 2005-03-01 18:31:00 & 52a9c93692c4e16686bebbff\\\\\n", + "\t 2005-03-05 02:58:00 & 52a9c93692c4e16686bebc00\\\\\n", + "\t 2005-03-01 10:40:00 & 52a9c93692c4e16686bebc01\\\\\n", + "\t 2005-03-08 23:40:00 & 52a9c93692c4e16686bebc02\\\\\n", + "\t 2005-03-03 21:07:00 & 52a9c93692c4e16686bebc03\\\\\n", + "\t 2005-03-05 18:40:00 & 52a9c93692c4e16686bebc04\\\\\n", + "\t 2005-03-05 16:55:00 & 52a9c93692c4e16686bebc05\\\\\n", + "\t 2005-03-08 13:36:00 & 52a9c93692c4e16686bebc06\\\\\n", + "\t 2005-03-06 01:05:00 & 52a9c93692c4e16686bebc07\\\\\n", + "\t 2005-03-08 12:20:00 & 52a9c93692c4e16686bebc08\\\\\n", + "\t 2005-03-14 17:35:00 & 52a9c93692c4e16686bebc0c\\\\\n", + "\t 2005-03-08 08:56:00 & 52a9c93692c4e16686bebc09\\\\\n", + "\t 2005-03-09 16:55:00 & 52a9c93692c4e16686bebc0a\\\\\n", + "\t 2005-03-09 15:35:00 & 52a9c93692c4e16686bebc0b\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "Datetime | _id | \n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 2005-01-04 17:42:00 | 52a9c93692c4e16686bebba9 | \n", + "| 2005-01-05 17:36:00 | 52a9c93692c4e16686bebbaa | \n", + "| 2005-01-07 10:35:00 | 52a9c93692c4e16686bebbac | \n", + "| 2005-01-06 00:15:00 | 52a9c93692c4e16686bebbab | \n", + "| 2005-01-10 21:13:00 | 52a9c93692c4e16686bebbad | \n", + "| 2005-01-11 12:40:00 | 52a9c93692c4e16686bebbae | \n", + "| 2005-01-13 20:40:00 | 52a9c93692c4e16686bebbaf | \n", + "| 2005-01-14 17:35:00 | 52a9c93692c4e16686bebbb0 | \n", + "| 2005-01-15 22:43:00 | 52a9c93692c4e16686bebbb1 | \n", + "| 2005-01-15 16:00:00 | 52a9c93692c4e16686bebbb2 | \n", + "| 2005-01-16 00:42:00 | 52a9c93692c4e16686bebbb3 | \n", + "| 2005-01-25 20:48:00 | 52a9c93692c4e16686bebbb4 | \n", + "| 2005-01-11 12:55:00 | 52a9c93692c4e16686bebbb5 | \n", + "| 2005-01-18 05:01:00 | 52a9c93692c4e16686bebbb6 | \n", + "| 2005-01-18 11:15:00 | 52a9c93692c4e16686bebbb7 | \n", + "| 2005-01-18 10:50:00 | 52a9c93692c4e16686bebbb8 | \n", + "| 2005-01-20 00:15:00 | 52a9c93692c4e16686bebbb9 | \n", + "| 2005-01-21 09:15:00 | 52a9c93692c4e16686bebbba | \n", + "| 2005-01-21 21:16:00 | 52a9c93692c4e16686bebbbb | \n", + "| 2005-01-08 03:00:00 | 52a9c93692c4e16686bebbbc | \n", + "| 2005-01-24 21:45:00 | 52a9c93692c4e16686bebbbd | \n", + "| 2005-01-19 16:35:00 | 52a9c93692c4e16686bebbc2 | \n", + "| 2005-01-24 17:05:00 | 52a9c93692c4e16686bebbbe | \n", + "| 2005-01-24 21:30:00 | 52a9c93692c4e16686bebbbf | \n", + "| 2005-01-18 17:25:00 | 52a9c93692c4e16686bebbc0 | \n", + "| 2005-01-29 07:34:00 | 52a9c93692c4e16686bebbc1 | \n", + "| 2005-01-30 20:00:00 | 52a9c93692c4e16686bebbc3 | \n", + "| 2005-01-29 13:15:00 | 52a9c93692c4e16686bebbc4 | \n", + "| 2005-02-01 18:20:00 | 52a9c93692c4e16686bebbc5 | \n", + "| 2005-02-02 07:25:00 | 52a9c93692c4e16686bebbc6 | \n", + "| ⋮ | ⋮ | \n", + "| 2005-02-24 09:15:00 | 52a9c93692c4e16686bebbef | \n", + "| 2005-02-25 00:11:00 | 52a9c93692c4e16686bebbf0 | \n", + "| 2005-02-23 19:00:00 | 52a9c93692c4e16686bebbf1 | \n", + "| 2005-02-26 01:48:00 | 52a9c93692c4e16686bebbf2 | \n", + "| 2005-02-23 07:58:00 | 52a9c93692c4e16686bebbf3 | \n", + "| 2005-02-25 14:55:00 | 52a9c93692c4e16686bebbf4 | \n", + "| 2005-02-25 08:22:00 | 52a9c93692c4e16686bebbf5 | \n", + "| 2005-02-25 04:38:00 | 52a9c93692c4e16686bebbf6 | \n", + "| 2005-03-04 13:20:00 | 52a9c93692c4e16686bebbf7 | \n", + "| 2005-03-01 00:30:00 | 52a9c93692c4e16686bebbf8 | \n", + "| 2005-02-28 19:30:00 | 52a9c93692c4e16686bebbf9 | \n", + "| 2005-03-01 12:03:00 | 52a9c93692c4e16686bebbfa | \n", + "| 2005-02-14 23:03:00 | 52a9c93692c4e16686bebbfb | \n", + "| 2005-02-02 22:19:00 | 52a9c93692c4e16686bebbfc | \n", + "| 2005-03-03 14:30:00 | 52a9c93692c4e16686bebbfd | \n", + "| 2005-03-01 19:15:00 | 52a9c93692c4e16686bebbfe | \n", + "| 2005-03-01 18:31:00 | 52a9c93692c4e16686bebbff | \n", + "| 2005-03-05 02:58:00 | 52a9c93692c4e16686bebc00 | \n", + "| 2005-03-01 10:40:00 | 52a9c93692c4e16686bebc01 | \n", + "| 2005-03-08 23:40:00 | 52a9c93692c4e16686bebc02 | \n", + "| 2005-03-03 21:07:00 | 52a9c93692c4e16686bebc03 | \n", + "| 2005-03-05 18:40:00 | 52a9c93692c4e16686bebc04 | \n", + "| 2005-03-05 16:55:00 | 52a9c93692c4e16686bebc05 | \n", + "| 2005-03-08 13:36:00 | 52a9c93692c4e16686bebc06 | \n", + "| 2005-03-06 01:05:00 | 52a9c93692c4e16686bebc07 | \n", + "| 2005-03-08 12:20:00 | 52a9c93692c4e16686bebc08 | \n", + "| 2005-03-14 17:35:00 | 52a9c93692c4e16686bebc0c | \n", + "| 2005-03-08 08:56:00 | 52a9c93692c4e16686bebc09 | \n", + "| 2005-03-09 16:55:00 | 52a9c93692c4e16686bebc0a | \n", + "| 2005-03-09 15:35:00 | 52a9c93692c4e16686bebc0b | \n", + "\n", + "\n" + ], + "text/plain": [ + " Datetime _id \n", + "1 2005-01-04 17:42:00 52a9c93692c4e16686bebba9\n", + "2 2005-01-05 17:36:00 52a9c93692c4e16686bebbaa\n", + "3 2005-01-07 10:35:00 52a9c93692c4e16686bebbac\n", + "4 2005-01-06 00:15:00 52a9c93692c4e16686bebbab\n", + "5 2005-01-10 21:13:00 52a9c93692c4e16686bebbad\n", + "6 2005-01-11 12:40:00 52a9c93692c4e16686bebbae\n", + "7 2005-01-13 20:40:00 52a9c93692c4e16686bebbaf\n", + "8 2005-01-14 17:35:00 52a9c93692c4e16686bebbb0\n", + "9 2005-01-15 22:43:00 52a9c93692c4e16686bebbb1\n", + "10 2005-01-15 16:00:00 52a9c93692c4e16686bebbb2\n", + "11 2005-01-16 00:42:00 52a9c93692c4e16686bebbb3\n", + "12 2005-01-25 20:48:00 52a9c93692c4e16686bebbb4\n", + "13 2005-01-11 12:55:00 52a9c93692c4e16686bebbb5\n", + "14 2005-01-18 05:01:00 52a9c93692c4e16686bebbb6\n", + "15 2005-01-18 11:15:00 52a9c93692c4e16686bebbb7\n", + "16 2005-01-18 10:50:00 52a9c93692c4e16686bebbb8\n", + "17 2005-01-20 00:15:00 52a9c93692c4e16686bebbb9\n", + "18 2005-01-21 09:15:00 52a9c93692c4e16686bebbba\n", + "19 2005-01-21 21:16:00 52a9c93692c4e16686bebbbb\n", + "20 2005-01-08 03:00:00 52a9c93692c4e16686bebbbc\n", + "21 2005-01-24 21:45:00 52a9c93692c4e16686bebbbd\n", + "22 2005-01-19 16:35:00 52a9c93692c4e16686bebbc2\n", + "23 2005-01-24 17:05:00 52a9c93692c4e16686bebbbe\n", + "24 2005-01-24 21:30:00 52a9c93692c4e16686bebbbf\n", + "25 2005-01-18 17:25:00 52a9c93692c4e16686bebbc0\n", + "26 2005-01-29 07:34:00 52a9c93692c4e16686bebbc1\n", + "27 2005-01-30 20:00:00 52a9c93692c4e16686bebbc3\n", + "28 2005-01-29 13:15:00 52a9c93692c4e16686bebbc4\n", + "29 2005-02-01 18:20:00 52a9c93692c4e16686bebbc5\n", + "30 2005-02-02 07:25:00 52a9c93692c4e16686bebbc6\n", + "⋮ ⋮ ⋮ \n", + "71 2005-02-24 09:15:00 52a9c93692c4e16686bebbef\n", + "72 2005-02-25 00:11:00 52a9c93692c4e16686bebbf0\n", + "73 2005-02-23 19:00:00 52a9c93692c4e16686bebbf1\n", + "74 2005-02-26 01:48:00 52a9c93692c4e16686bebbf2\n", + "75 2005-02-23 07:58:00 52a9c93692c4e16686bebbf3\n", + "76 2005-02-25 14:55:00 52a9c93692c4e16686bebbf4\n", + "77 2005-02-25 08:22:00 52a9c93692c4e16686bebbf5\n", + "78 2005-02-25 04:38:00 52a9c93692c4e16686bebbf6\n", + "79 2005-03-04 13:20:00 52a9c93692c4e16686bebbf7\n", + "80 2005-03-01 00:30:00 52a9c93692c4e16686bebbf8\n", + "81 2005-02-28 19:30:00 52a9c93692c4e16686bebbf9\n", + "82 2005-03-01 12:03:00 52a9c93692c4e16686bebbfa\n", + "83 2005-02-14 23:03:00 52a9c93692c4e16686bebbfb\n", + "84 2005-02-02 22:19:00 52a9c93692c4e16686bebbfc\n", + "85 2005-03-03 14:30:00 52a9c93692c4e16686bebbfd\n", + "86 2005-03-01 19:15:00 52a9c93692c4e16686bebbfe\n", + "87 2005-03-01 18:31:00 52a9c93692c4e16686bebbff\n", + "88 2005-03-05 02:58:00 52a9c93692c4e16686bebc00\n", + "89 2005-03-01 10:40:00 52a9c93692c4e16686bebc01\n", + "90 2005-03-08 23:40:00 52a9c93692c4e16686bebc02\n", + "91 2005-03-03 21:07:00 52a9c93692c4e16686bebc03\n", + "92 2005-03-05 18:40:00 52a9c93692c4e16686bebc04\n", + "93 2005-03-05 16:55:00 52a9c93692c4e16686bebc05\n", + "94 2005-03-08 13:36:00 52a9c93692c4e16686bebc06\n", + "95 2005-03-06 01:05:00 52a9c93692c4e16686bebc07\n", + "96 2005-03-08 12:20:00 52a9c93692c4e16686bebc08\n", + "97 2005-03-14 17:35:00 52a9c93692c4e16686bebc0c\n", + "98 2005-03-08 08:56:00 52a9c93692c4e16686bebc09\n", + "99 2005-03-09 16:55:00 52a9c93692c4e16686bebc0a\n", + "100 2005-03-09 15:35:00 52a9c93692c4e16686bebc0b" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "accidents$find('{}', fields='{\"Datetime\": true}', limit=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + 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DatetimeNumber_of_CasualtiesNumber_of_Vehicles_id
2005-01-04 17:42:00 1 1 52a9c93692c4e16686bebba9
2005-01-05 17:36:00 1 1 52a9c93692c4e16686bebbaa
2005-01-07 10:35:00 1 1 52a9c93692c4e16686bebbac
2005-01-06 00:15:00 1 2 52a9c93692c4e16686bebbab
2005-01-10 21:13:00 1 1 52a9c93692c4e16686bebbad
2005-01-11 12:40:00 1 2 52a9c93692c4e16686bebbae
2005-01-13 20:40:00 1 2 52a9c93692c4e16686bebbaf
2005-01-14 17:35:00 2 1 52a9c93692c4e16686bebbb0
2005-01-15 22:43:00 2 2 52a9c93692c4e16686bebbb1
2005-01-15 16:00:00 5 2 52a9c93692c4e16686bebbb2
2005-01-16 00:42:00 1 1 52a9c93692c4e16686bebbb3
2005-01-25 20:48:00 1 2 52a9c93692c4e16686bebbb4
2005-01-11 12:55:00 1 1 52a9c93692c4e16686bebbb5
2005-01-18 05:01:00 1 2 52a9c93692c4e16686bebbb6
2005-01-18 11:15:00 2 1 52a9c93692c4e16686bebbb7
2005-01-18 10:50:00 1 1 52a9c93692c4e16686bebbb8
2005-01-20 00:15:00 1 2 52a9c93692c4e16686bebbb9
2005-01-21 09:15:00 1 2 52a9c93692c4e16686bebbba
2005-01-21 21:16:00 1 2 52a9c93692c4e16686bebbbb
2005-01-08 03:00:00 1 1 52a9c93692c4e16686bebbbc
2005-01-24 21:45:00 1 1 52a9c93692c4e16686bebbbd
2005-01-19 16:35:00 1 1 52a9c93692c4e16686bebbc2
2005-01-24 17:05:00 1 2 52a9c93692c4e16686bebbbe
2005-01-24 21:30:00 1 2 52a9c93692c4e16686bebbbf
2005-01-18 17:25:00 1 2 52a9c93692c4e16686bebbc0
2005-01-29 07:34:00 1 2 52a9c93692c4e16686bebbc1
2005-01-30 20:00:00 1 2 52a9c93692c4e16686bebbc3
2005-01-29 13:15:00 2 2 52a9c93692c4e16686bebbc4
2005-02-01 18:20:00 1 2 52a9c93692c4e16686bebbc5
2005-02-02 07:25:00 1 2 52a9c93692c4e16686bebbc6
⋮⋮⋮⋮
2012-09-08 22:12:00 4 2 52a9c98492c4e16686d36aea
2012-09-18 06:22:00 1 2 52a9c98492c4e16686d36aeb
2012-09-08 13:15:00 1 1 52a9c98492c4e16686d36aec
2012-09-23 14:00:00 1 1 52a9c98492c4e16686d36aed
2012-09-19 09:41:00 1 1 52a9c98492c4e16686d36aee
2012-09-16 13:00:00 1 1 52a9c98492c4e16686d36aef
2012-10-02 14:20:00 2 2 52a9c98492c4e16686d36af0
2012-10-15 15:58:00 1 2 52a9c98492c4e16686d36af1
2012-10-15 19:55:00 1 1 52a9c98492c4e16686d36af2
2012-10-18 10:53:00 1 1 52a9c98492c4e16686d36af3
2012-10-17 20:59:00 1 1 52a9c98492c4e16686d36af4
2012-10-21 01:06:00 1 2 52a9c98492c4e16686d36af5
2012-10-24 18:12:00 1 2 52a9c98492c4e16686d36af6
2012-10-27 13:29:00 1 1 52a9c98492c4e16686d36af7
2012-10-24 10:10:00 1 2 52a9c98492c4e16686d36af8
2012-11-11 16:00:00 1 1 52a9c98492c4e16686d36af9
2012-11-07 14:04:00 1 1 52a9c98492c4e16686d36afa
2012-11-19 07:30:00 1 1 52a9c98492c4e16686d36afb
2012-11-21 06:40:00 1 1 52a9c98492c4e16686d36afc
2012-11-28 10:00:00 1 2 52a9c98492c4e16686d36afd
2012-11-28 09:40:00 1 2 52a9c98492c4e16686d36afe
2012-11-29 08:39:00 1 1 52a9c98492c4e16686d36aff
2012-12-04 07:36:00 1 2 52a9c98492c4e16686d36b00
2012-12-03 22:48:00 1 1 52a9c98492c4e16686d36b01
2012-11-30 22:16:00 2 1 52a9c98492c4e16686d36b02
2012-12-06 12:45:00 1 2 52a9c98492c4e16686d36b03
2012-12-20 20:00:00 1 1 52a9c98492c4e16686d36b04
2012-12-22 13:01:00 1 1 52a9c98492c4e16686d36b05
2012-12-25 11:33:00 2 1 52a9c98492c4e16686d36b06
2012-12-27 16:30:00 1 1 52a9c98492c4e16686d36b07
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|llll}\n", + " Datetime & Number\\_of\\_Casualties & Number\\_of\\_Vehicles & \\_id\\\\\n", + "\\hline\n", + "\t 2005-01-04 17:42:00 & 1 & 1 & 52a9c93692c4e16686bebba9\\\\\n", + "\t 2005-01-05 17:36:00 & 1 & 1 & 52a9c93692c4e16686bebbaa\\\\\n", + "\t 2005-01-07 10:35:00 & 1 & 1 & 52a9c93692c4e16686bebbac\\\\\n", + "\t 2005-01-06 00:15:00 & 1 & 2 & 52a9c93692c4e16686bebbab\\\\\n", + "\t 2005-01-10 21:13:00 & 1 & 1 & 52a9c93692c4e16686bebbad\\\\\n", + "\t 2005-01-11 12:40:00 & 1 & 2 & 52a9c93692c4e16686bebbae\\\\\n", + "\t 2005-01-13 20:40:00 & 1 & 2 & 52a9c93692c4e16686bebbaf\\\\\n", + "\t 2005-01-14 17:35:00 & 2 & 1 & 52a9c93692c4e16686bebbb0\\\\\n", + "\t 2005-01-15 22:43:00 & 2 & 2 & 52a9c93692c4e16686bebbb1\\\\\n", + "\t 2005-01-15 16:00:00 & 5 & 2 & 52a9c93692c4e16686bebbb2\\\\\n", + "\t 2005-01-16 00:42:00 & 1 & 1 & 52a9c93692c4e16686bebbb3\\\\\n", + "\t 2005-01-25 20:48:00 & 1 & 2 & 52a9c93692c4e16686bebbb4\\\\\n", + "\t 2005-01-11 12:55:00 & 1 & 1 & 52a9c93692c4e16686bebbb5\\\\\n", + "\t 2005-01-18 05:01:00 & 1 & 2 & 52a9c93692c4e16686bebbb6\\\\\n", + "\t 2005-01-18 11:15:00 & 2 & 1 & 52a9c93692c4e16686bebbb7\\\\\n", + "\t 2005-01-18 10:50:00 & 1 & 1 & 52a9c93692c4e16686bebbb8\\\\\n", + "\t 2005-01-20 00:15:00 & 1 & 2 & 52a9c93692c4e16686bebbb9\\\\\n", + "\t 2005-01-21 09:15:00 & 1 & 2 & 52a9c93692c4e16686bebbba\\\\\n", + "\t 2005-01-21 21:16:00 & 1 & 2 & 52a9c93692c4e16686bebbbb\\\\\n", + "\t 2005-01-08 03:00:00 & 1 & 1 & 52a9c93692c4e16686bebbbc\\\\\n", + "\t 2005-01-24 21:45:00 & 1 & 1 & 52a9c93692c4e16686bebbbd\\\\\n", + "\t 2005-01-19 16:35:00 & 1 & 1 & 52a9c93692c4e16686bebbc2\\\\\n", + "\t 2005-01-24 17:05:00 & 1 & 2 & 52a9c93692c4e16686bebbbe\\\\\n", + "\t 2005-01-24 21:30:00 & 1 & 2 & 52a9c93692c4e16686bebbbf\\\\\n", + "\t 2005-01-18 17:25:00 & 1 & 2 & 52a9c93692c4e16686bebbc0\\\\\n", + "\t 2005-01-29 07:34:00 & 1 & 2 & 52a9c93692c4e16686bebbc1\\\\\n", + "\t 2005-01-30 20:00:00 & 1 & 2 & 52a9c93692c4e16686bebbc3\\\\\n", + "\t 2005-01-29 13:15:00 & 2 & 2 & 52a9c93692c4e16686bebbc4\\\\\n", + "\t 2005-02-01 18:20:00 & 1 & 2 & 52a9c93692c4e16686bebbc5\\\\\n", + "\t 2005-02-02 07:25:00 & 1 & 2 & 52a9c93692c4e16686bebbc6\\\\\n", + "\t ⋮ & ⋮ & ⋮ & ⋮\\\\\n", + "\t 2012-09-08 22:12:00 & 4 & 2 & 52a9c98492c4e16686d36aea\\\\\n", + "\t 2012-09-18 06:22:00 & 1 & 2 & 52a9c98492c4e16686d36aeb\\\\\n", + "\t 2012-09-08 13:15:00 & 1 & 1 & 52a9c98492c4e16686d36aec\\\\\n", + "\t 2012-09-23 14:00:00 & 1 & 1 & 52a9c98492c4e16686d36aed\\\\\n", + "\t 2012-09-19 09:41:00 & 1 & 1 & 52a9c98492c4e16686d36aee\\\\\n", + "\t 2012-09-16 13:00:00 & 1 & 1 & 52a9c98492c4e16686d36aef\\\\\n", + "\t 2012-10-02 14:20:00 & 2 & 2 & 52a9c98492c4e16686d36af0\\\\\n", + "\t 2012-10-15 15:58:00 & 1 & 2 & 52a9c98492c4e16686d36af1\\\\\n", + "\t 2012-10-15 19:55:00 & 1 & 1 & 52a9c98492c4e16686d36af2\\\\\n", + "\t 2012-10-18 10:53:00 & 1 & 1 & 52a9c98492c4e16686d36af3\\\\\n", + "\t 2012-10-17 20:59:00 & 1 & 1 & 52a9c98492c4e16686d36af4\\\\\n", + "\t 2012-10-21 01:06:00 & 1 & 2 & 52a9c98492c4e16686d36af5\\\\\n", + "\t 2012-10-24 18:12:00 & 1 & 2 & 52a9c98492c4e16686d36af6\\\\\n", + "\t 2012-10-27 13:29:00 & 1 & 1 & 52a9c98492c4e16686d36af7\\\\\n", + "\t 2012-10-24 10:10:00 & 1 & 2 & 52a9c98492c4e16686d36af8\\\\\n", + "\t 2012-11-11 16:00:00 & 1 & 1 & 52a9c98492c4e16686d36af9\\\\\n", + "\t 2012-11-07 14:04:00 & 1 & 1 & 52a9c98492c4e16686d36afa\\\\\n", + "\t 2012-11-19 07:30:00 & 1 & 1 & 52a9c98492c4e16686d36afb\\\\\n", + "\t 2012-11-21 06:40:00 & 1 & 1 & 52a9c98492c4e16686d36afc\\\\\n", + "\t 2012-11-28 10:00:00 & 1 & 2 & 52a9c98492c4e16686d36afd\\\\\n", + "\t 2012-11-28 09:40:00 & 1 & 2 & 52a9c98492c4e16686d36afe\\\\\n", + "\t 2012-11-29 08:39:00 & 1 & 1 & 52a9c98492c4e16686d36aff\\\\\n", + "\t 2012-12-04 07:36:00 & 1 & 2 & 52a9c98492c4e16686d36b00\\\\\n", + "\t 2012-12-03 22:48:00 & 1 & 1 & 52a9c98492c4e16686d36b01\\\\\n", + "\t 2012-11-30 22:16:00 & 2 & 1 & 52a9c98492c4e16686d36b02\\\\\n", + "\t 2012-12-06 12:45:00 & 1 & 2 & 52a9c98492c4e16686d36b03\\\\\n", + "\t 2012-12-20 20:00:00 & 1 & 1 & 52a9c98492c4e16686d36b04\\\\\n", + "\t 2012-12-22 13:01:00 & 1 & 1 & 52a9c98492c4e16686d36b05\\\\\n", + "\t 2012-12-25 11:33:00 & 2 & 1 & 52a9c98492c4e16686d36b06\\\\\n", + "\t 2012-12-27 16:30:00 & 1 & 1 & 52a9c98492c4e16686d36b07\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "Datetime | Number_of_Casualties | Number_of_Vehicles | _id | \n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 2005-01-04 17:42:00 | 1 | 1 | 52a9c93692c4e16686bebba9 | \n", + "| 2005-01-05 17:36:00 | 1 | 1 | 52a9c93692c4e16686bebbaa | \n", + "| 2005-01-07 10:35:00 | 1 | 1 | 52a9c93692c4e16686bebbac | \n", + "| 2005-01-06 00:15:00 | 1 | 2 | 52a9c93692c4e16686bebbab | \n", + "| 2005-01-10 21:13:00 | 1 | 1 | 52a9c93692c4e16686bebbad | \n", + "| 2005-01-11 12:40:00 | 1 | 2 | 52a9c93692c4e16686bebbae | \n", + "| 2005-01-13 20:40:00 | 1 | 2 | 52a9c93692c4e16686bebbaf | \n", + "| 2005-01-14 17:35:00 | 2 | 1 | 52a9c93692c4e16686bebbb0 | \n", + "| 2005-01-15 22:43:00 | 2 | 2 | 52a9c93692c4e16686bebbb1 | \n", + "| 2005-01-15 16:00:00 | 5 | 2 | 52a9c93692c4e16686bebbb2 | \n", + "| 2005-01-16 00:42:00 | 1 | 1 | 52a9c93692c4e16686bebbb3 | \n", + "| 2005-01-25 20:48:00 | 1 | 2 | 52a9c93692c4e16686bebbb4 | \n", + "| 2005-01-11 12:55:00 | 1 | 1 | 52a9c93692c4e16686bebbb5 | \n", + "| 2005-01-18 05:01:00 | 1 | 2 | 52a9c93692c4e16686bebbb6 | \n", + "| 2005-01-18 11:15:00 | 2 | 1 | 52a9c93692c4e16686bebbb7 | \n", + "| 2005-01-18 10:50:00 | 1 | 1 | 52a9c93692c4e16686bebbb8 | \n", + "| 2005-01-20 00:15:00 | 1 | 2 | 52a9c93692c4e16686bebbb9 | \n", + "| 2005-01-21 09:15:00 | 1 | 2 | 52a9c93692c4e16686bebbba | \n", + "| 2005-01-21 21:16:00 | 1 | 2 | 52a9c93692c4e16686bebbbb | \n", + "| 2005-01-08 03:00:00 | 1 | 1 | 52a9c93692c4e16686bebbbc | \n", + "| 2005-01-24 21:45:00 | 1 | 1 | 52a9c93692c4e16686bebbbd | \n", + "| 2005-01-19 16:35:00 | 1 | 1 | 52a9c93692c4e16686bebbc2 | \n", + "| 2005-01-24 17:05:00 | 1 | 2 | 52a9c93692c4e16686bebbbe | \n", + "| 2005-01-24 21:30:00 | 1 | 2 | 52a9c93692c4e16686bebbbf | \n", + "| 2005-01-18 17:25:00 | 1 | 2 | 52a9c93692c4e16686bebbc0 | \n", + "| 2005-01-29 07:34:00 | 1 | 2 | 52a9c93692c4e16686bebbc1 | \n", + "| 2005-01-30 20:00:00 | 1 | 2 | 52a9c93692c4e16686bebbc3 | \n", + "| 2005-01-29 13:15:00 | 2 | 2 | 52a9c93692c4e16686bebbc4 | \n", + "| 2005-02-01 18:20:00 | 1 | 2 | 52a9c93692c4e16686bebbc5 | \n", + "| 2005-02-02 07:25:00 | 1 | 2 | 52a9c93692c4e16686bebbc6 | \n", + "| ⋮ | ⋮ | ⋮ | ⋮ | \n", + "| 2012-09-08 22:12:00 | 4 | 2 | 52a9c98492c4e16686d36aea | \n", + "| 2012-09-18 06:22:00 | 1 | 2 | 52a9c98492c4e16686d36aeb | \n", + "| 2012-09-08 13:15:00 | 1 | 1 | 52a9c98492c4e16686d36aec | \n", + "| 2012-09-23 14:00:00 | 1 | 1 | 52a9c98492c4e16686d36aed | \n", + "| 2012-09-19 09:41:00 | 1 | 1 | 52a9c98492c4e16686d36aee | \n", + "| 2012-09-16 13:00:00 | 1 | 1 | 52a9c98492c4e16686d36aef | \n", + "| 2012-10-02 14:20:00 | 2 | 2 | 52a9c98492c4e16686d36af0 | \n", + "| 2012-10-15 15:58:00 | 1 | 2 | 52a9c98492c4e16686d36af1 | \n", + "| 2012-10-15 19:55:00 | 1 | 1 | 52a9c98492c4e16686d36af2 | \n", + "| 2012-10-18 10:53:00 | 1 | 1 | 52a9c98492c4e16686d36af3 | \n", + "| 2012-10-17 20:59:00 | 1 | 1 | 52a9c98492c4e16686d36af4 | \n", + "| 2012-10-21 01:06:00 | 1 | 2 | 52a9c98492c4e16686d36af5 | \n", + "| 2012-10-24 18:12:00 | 1 | 2 | 52a9c98492c4e16686d36af6 | \n", + "| 2012-10-27 13:29:00 | 1 | 1 | 52a9c98492c4e16686d36af7 | \n", + "| 2012-10-24 10:10:00 | 1 | 2 | 52a9c98492c4e16686d36af8 | \n", + "| 2012-11-11 16:00:00 | 1 | 1 | 52a9c98492c4e16686d36af9 | \n", + "| 2012-11-07 14:04:00 | 1 | 1 | 52a9c98492c4e16686d36afa | \n", + "| 2012-11-19 07:30:00 | 1 | 1 | 52a9c98492c4e16686d36afb | \n", + "| 2012-11-21 06:40:00 | 1 | 1 | 52a9c98492c4e16686d36afc | \n", + "| 2012-11-28 10:00:00 | 1 | 2 | 52a9c98492c4e16686d36afd | \n", + "| 2012-11-28 09:40:00 | 1 | 2 | 52a9c98492c4e16686d36afe | \n", + "| 2012-11-29 08:39:00 | 1 | 1 | 52a9c98492c4e16686d36aff | \n", + "| 2012-12-04 07:36:00 | 1 | 2 | 52a9c98492c4e16686d36b00 | \n", + "| 2012-12-03 22:48:00 | 1 | 1 | 52a9c98492c4e16686d36b01 | \n", + "| 2012-11-30 22:16:00 | 2 | 1 | 52a9c98492c4e16686d36b02 | \n", + "| 2012-12-06 12:45:00 | 1 | 2 | 52a9c98492c4e16686d36b03 | \n", + "| 2012-12-20 20:00:00 | 1 | 1 | 52a9c98492c4e16686d36b04 | \n", + "| 2012-12-22 13:01:00 | 1 | 1 | 52a9c98492c4e16686d36b05 | \n", + "| 2012-12-25 11:33:00 | 2 | 1 | 52a9c98492c4e16686d36b06 | \n", + "| 2012-12-27 16:30:00 | 1 | 1 | 52a9c98492c4e16686d36b07 | \n", + "\n", + "\n" + ], + "text/plain": [ + " Datetime Number_of_Casualties Number_of_Vehicles\n", + "1 2005-01-04 17:42:00 1 1 \n", + "2 2005-01-05 17:36:00 1 1 \n", + "3 2005-01-07 10:35:00 1 1 \n", + "4 2005-01-06 00:15:00 1 2 \n", + "5 2005-01-10 21:13:00 1 1 \n", + "6 2005-01-11 12:40:00 1 2 \n", + "7 2005-01-13 20:40:00 1 2 \n", + "8 2005-01-14 17:35:00 2 1 \n", + "9 2005-01-15 22:43:00 2 2 \n", + "10 2005-01-15 16:00:00 5 2 \n", + "11 2005-01-16 00:42:00 1 1 \n", + "12 2005-01-25 20:48:00 1 2 \n", + "13 2005-01-11 12:55:00 1 1 \n", + "14 2005-01-18 05:01:00 1 2 \n", + "15 2005-01-18 11:15:00 2 1 \n", + "16 2005-01-18 10:50:00 1 1 \n", + "17 2005-01-20 00:15:00 1 2 \n", + "18 2005-01-21 09:15:00 1 2 \n", + "19 2005-01-21 21:16:00 1 2 \n", + "20 2005-01-08 03:00:00 1 1 \n", + "21 2005-01-24 21:45:00 1 1 \n", + "22 2005-01-19 16:35:00 1 1 \n", + "23 2005-01-24 17:05:00 1 2 \n", + "24 2005-01-24 21:30:00 1 2 \n", + "25 2005-01-18 17:25:00 1 2 \n", + "26 2005-01-29 07:34:00 1 2 \n", + "27 2005-01-30 20:00:00 1 2 \n", + "28 2005-01-29 13:15:00 2 2 \n", + "29 2005-02-01 18:20:00 1 2 \n", + "30 2005-02-02 07:25:00 1 2 \n", + "⋮ ⋮ ⋮ ⋮ \n", + "1355586 2012-09-08 22:12:00 4 2 \n", + "1355587 2012-09-18 06:22:00 1 2 \n", + "1355588 2012-09-08 13:15:00 1 1 \n", + "1355589 2012-09-23 14:00:00 1 1 \n", + "1355590 2012-09-19 09:41:00 1 1 \n", + "1355591 2012-09-16 13:00:00 1 1 \n", + "1355592 2012-10-02 14:20:00 2 2 \n", + "1355593 2012-10-15 15:58:00 1 2 \n", + "1355594 2012-10-15 19:55:00 1 1 \n", + "1355595 2012-10-18 10:53:00 1 1 \n", + "1355596 2012-10-17 20:59:00 1 1 \n", + "1355597 2012-10-21 01:06:00 1 2 \n", + "1355598 2012-10-24 18:12:00 1 2 \n", + "1355599 2012-10-27 13:29:00 1 1 \n", + "1355600 2012-10-24 10:10:00 1 2 \n", + "1355601 2012-11-11 16:00:00 1 1 \n", + "1355602 2012-11-07 14:04:00 1 1 \n", + "1355603 2012-11-19 07:30:00 1 1 \n", + "1355604 2012-11-21 06:40:00 1 1 \n", + "1355605 2012-11-28 10:00:00 1 2 \n", + "1355606 2012-11-28 09:40:00 1 2 \n", + "1355607 2012-11-29 08:39:00 1 1 \n", + "1355608 2012-12-04 07:36:00 1 2 \n", + "1355609 2012-12-03 22:48:00 1 1 \n", + "1355610 2012-11-30 22:16:00 2 1 \n", + "1355611 2012-12-06 12:45:00 1 2 \n", + "1355612 2012-12-20 20:00:00 1 1 \n", + "1355613 2012-12-22 13:01:00 1 1 \n", + "1355614 2012-12-25 11:33:00 2 1 \n", + "1355615 2012-12-27 16:30:00 1 1 \n", + " _id \n", + "1 52a9c93692c4e16686bebba9\n", + "2 52a9c93692c4e16686bebbaa\n", + "3 52a9c93692c4e16686bebbac\n", + "4 52a9c93692c4e16686bebbab\n", + "5 52a9c93692c4e16686bebbad\n", + "6 52a9c93692c4e16686bebbae\n", + "7 52a9c93692c4e16686bebbaf\n", + "8 52a9c93692c4e16686bebbb0\n", + "9 52a9c93692c4e16686bebbb1\n", + "10 52a9c93692c4e16686bebbb2\n", + "11 52a9c93692c4e16686bebbb3\n", + "12 52a9c93692c4e16686bebbb4\n", + "13 52a9c93692c4e16686bebbb5\n", + "14 52a9c93692c4e16686bebbb6\n", + "15 52a9c93692c4e16686bebbb7\n", + "16 52a9c93692c4e16686bebbb8\n", + "17 52a9c93692c4e16686bebbb9\n", + "18 52a9c93692c4e16686bebbba\n", + "19 52a9c93692c4e16686bebbbb\n", + "20 52a9c93692c4e16686bebbbc\n", + "21 52a9c93692c4e16686bebbbd\n", + "22 52a9c93692c4e16686bebbc2\n", + "23 52a9c93692c4e16686bebbbe\n", + "24 52a9c93692c4e16686bebbbf\n", + "25 52a9c93692c4e16686bebbc0\n", + "26 52a9c93692c4e16686bebbc1\n", + "27 52a9c93692c4e16686bebbc3\n", + "28 52a9c93692c4e16686bebbc4\n", + "29 52a9c93692c4e16686bebbc5\n", + "30 52a9c93692c4e16686bebbc6\n", + "⋮ ⋮ \n", + "1355586 52a9c98492c4e16686d36aea\n", + "1355587 52a9c98492c4e16686d36aeb\n", + "1355588 52a9c98492c4e16686d36aec\n", + "1355589 52a9c98492c4e16686d36aed\n", + "1355590 52a9c98492c4e16686d36aee\n", + "1355591 52a9c98492c4e16686d36aef\n", + "1355592 52a9c98492c4e16686d36af0\n", + "1355593 52a9c98492c4e16686d36af1\n", + "1355594 52a9c98492c4e16686d36af2\n", + "1355595 52a9c98492c4e16686d36af3\n", + "1355596 52a9c98492c4e16686d36af4\n", + "1355597 52a9c98492c4e16686d36af5\n", + "1355598 52a9c98492c4e16686d36af6\n", + "1355599 52a9c98492c4e16686d36af7\n", + "1355600 52a9c98492c4e16686d36af8\n", + "1355601 52a9c98492c4e16686d36af9\n", + "1355602 52a9c98492c4e16686d36afa\n", + "1355603 52a9c98492c4e16686d36afb\n", + "1355604 52a9c98492c4e16686d36afc\n", + "1355605 52a9c98492c4e16686d36afd\n", + "1355606 52a9c98492c4e16686d36afe\n", + "1355607 52a9c98492c4e16686d36aff\n", + "1355608 52a9c98492c4e16686d36b00\n", + "1355609 52a9c98492c4e16686d36b01\n", + "1355610 52a9c98492c4e16686d36b02\n", + "1355611 52a9c98492c4e16686d36b03\n", + "1355612 52a9c98492c4e16686d36b04\n", + "1355613 52a9c98492c4e16686d36b05\n", + "1355614 52a9c98492c4e16686d36b06\n", + "1355615 52a9c98492c4e16686d36b07" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cv_date <- accidents$find(\n", + " # query = '{\"Datetime\": { \"$gte\" : { \"$date\" : \"2012-12-01T00:00:00Z\" }}}',\n", + " query = '{}',\n", + " fields = '{\"Number_of_Casualties\": true, \"Number_of_Vehicles\": true, \"Datetime\": true}')\n", + "cv_date" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'data.frame':\t1355615 obs. of 4 variables:\n", + " $ Datetime : POSIXct, format: \"2005-01-04 17:42:00\" \"2005-01-05 17:36:00\" ...\n", + " $ Number_of_Casualties: int 1 1 1 1 1 1 1 2 2 5 ...\n", + " $ Number_of_Vehicles : int 1 1 1 2 1 2 2 1 2 2 ...\n", + " $ _id : chr \"52a9c93692c4e16686bebba9\" \"52a9c93692c4e16686bebbaa\" \"52a9c93692c4e16686bebbac\" \"52a9c93692c4e16686bebbab\" ...\n" + ] + } + ], + "source": [ + "str(cv_date)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "cv_date <- mutate(cv_date, year = year(Datetime))\n", + "cv_date$yearf <- factor(cv_date$year)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + 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Number_of_CasualtiesNumber_of_Vehiclesyearn
1 1 2005 52027
1 1 2006 49358
1 1 2007 48023
1 1 2008 46121
1 1 2009 44076
1 1 2010 41760
1 1 2011 41082
1 1 2012 39217
1 2 2005 86717
1 2 2006 82553
1 2 2007 79828
1 2 2008 74819
1 2 2009 71802
1 2 2010 68416
1 2 2011 68019
1 2 2012 65680
1 3 2005 9756
1 3 2006 9122
1 3 2007 8610
1 3 2008 7795
1 3 2009 7249
1 3 2010 6701
1 3 2011 6379
1 3 2012 6168
1 4 2005 1714
1 4 2006 1574
1 4 2007 1612
1 4 2008 1373
1 4 2009 1280
1 4 2010 1180
⋮⋮⋮⋮
28 2 20071
28 4 20081
29 1 20071
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29 5 20071
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33 2 20121
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\n" + ], + "text/latex": [ + "\\begin{tabular}{r|llll}\n", + " Number\\_of\\_Casualties & Number\\_of\\_Vehicles & year & n\\\\\n", + "\\hline\n", + "\t 1 & 1 & 2005 & 52027\\\\\n", + "\t 1 & 1 & 2006 & 49358\\\\\n", + "\t 1 & 1 & 2007 & 48023\\\\\n", + "\t 1 & 1 & 2008 & 46121\\\\\n", + "\t 1 & 1 & 2009 & 44076\\\\\n", + "\t 1 & 1 & 2010 & 41760\\\\\n", + "\t 1 & 1 & 2011 & 41082\\\\\n", + "\t 1 & 1 & 2012 & 39217\\\\\n", + "\t 1 & 2 & 2005 & 86717\\\\\n", + "\t 1 & 2 & 2006 & 82553\\\\\n", + "\t 1 & 2 & 2007 & 79828\\\\\n", + "\t 1 & 2 & 2008 & 74819\\\\\n", + "\t 1 & 2 & 2009 & 71802\\\\\n", + "\t 1 & 2 & 2010 & 68416\\\\\n", + "\t 1 & 2 & 2011 & 68019\\\\\n", + "\t 1 & 2 & 2012 & 65680\\\\\n", + "\t 1 & 3 & 2005 & 9756\\\\\n", + "\t 1 & 3 & 2006 & 9122\\\\\n", + "\t 1 & 3 & 2007 & 8610\\\\\n", + "\t 1 & 3 & 2008 & 7795\\\\\n", + "\t 1 & 3 & 2009 & 7249\\\\\n", + "\t 1 & 3 & 2010 & 6701\\\\\n", + "\t 1 & 3 & 2011 & 6379\\\\\n", + "\t 1 & 3 & 2012 & 6168\\\\\n", + "\t 1 & 4 & 2005 & 1714\\\\\n", + "\t 1 & 4 & 2006 & 1574\\\\\n", + "\t 1 & 4 & 2007 & 1612\\\\\n", + "\t 1 & 4 & 2008 & 1373\\\\\n", + "\t 1 & 4 & 2009 & 1280\\\\\n", + "\t 1 & 4 & 2010 & 1180\\\\\n", + "\t ⋮ & ⋮ & ⋮ & ⋮\\\\\n", + "\t 28 & 2 & 2007 & 1 \\\\\n", + "\t 28 & 4 & 2008 & 1 \\\\\n", + "\t 29 & 1 & 2007 & 1 \\\\\n", + "\t 29 & 2 & 2005 & 1 \\\\\n", + "\t 29 & 2 & 2007 & 1 \\\\\n", + "\t 29 & 5 & 2007 & 1 \\\\\n", + "\t 32 & 2 & 2008 & 1 \\\\\n", + "\t 33 & 2 & 2012 & 1 \\\\\n", + "\t 35 & 4 & 2005 & 1 \\\\\n", + "\t 36 & 2 & 2006 & 1 \\\\\n", + "\t 36 & 3 & 2010 & 1 \\\\\n", + "\t 38 & 4 & 2012 & 1 \\\\\n", + "\t 40 & 2 & 2010 & 1 \\\\\n", + "\t 40 & 4 & 2007 & 1 \\\\\n", + "\t 41 & 2 & 2006 & 1 \\\\\n", + "\t 42 & 1 & 2010 & 1 \\\\\n", + "\t 42 & 2 & 2012 & 1 \\\\\n", + "\t 42 & 8 & 2007 & 1 \\\\\n", + "\t 43 & 1 & 2010 & 1 \\\\\n", + "\t 43 & 4 & 2010 & 1 \\\\\n", + "\t 45 & 1 & 2006 & 1 \\\\\n", + "\t 45 & 3 & 2008 & 1 \\\\\n", + "\t 47 & 3 & 2008 & 1 \\\\\n", + "\t 48 & 1 & 2009 & 1 \\\\\n", + "\t 51 & 2 & 2011 & 1 \\\\\n", + "\t 51 & 34 & 2011 & 1 \\\\\n", + "\t 62 & 2 & 2008 & 1 \\\\\n", + "\t 63 & 2 & 2011 & 1 \\\\\n", + "\t 68 & 1 & 2007 & 1 \\\\\n", + "\t 87 & 5 & 2011 & 1 \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "Number_of_Casualties | Number_of_Vehicles | year | n | \n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | 1 | 2005 | 52027 | \n", + "| 1 | 1 | 2006 | 49358 | \n", + "| 1 | 1 | 2007 | 48023 | \n", + "| 1 | 1 | 2008 | 46121 | \n", + "| 1 | 1 | 2009 | 44076 | \n", + "| 1 | 1 | 2010 | 41760 | \n", + "| 1 | 1 | 2011 | 41082 | \n", + "| 1 | 1 | 2012 | 39217 | \n", + "| 1 | 2 | 2005 | 86717 | \n", + "| 1 | 2 | 2006 | 82553 | \n", + "| 1 | 2 | 2007 | 79828 | \n", + "| 1 | 2 | 2008 | 74819 | \n", + "| 1 | 2 | 2009 | 71802 | \n", + "| 1 | 2 | 2010 | 68416 | \n", + "| 1 | 2 | 2011 | 68019 | \n", + "| 1 | 2 | 2012 | 65680 | \n", + "| 1 | 3 | 2005 | 9756 | \n", + "| 1 | 3 | 2006 | 9122 | \n", + "| 1 | 3 | 2007 | 8610 | \n", + "| 1 | 3 | 2008 | 7795 | \n", + "| 1 | 3 | 2009 | 7249 | \n", + "| 1 | 3 | 2010 | 6701 | \n", + "| 1 | 3 | 2011 | 6379 | \n", + "| 1 | 3 | 2012 | 6168 | \n", + "| 1 | 4 | 2005 | 1714 | \n", + "| 1 | 4 | 2006 | 1574 | \n", + "| 1 | 4 | 2007 | 1612 | \n", + "| 1 | 4 | 2008 | 1373 | \n", + "| 1 | 4 | 2009 | 1280 | \n", + "| 1 | 4 | 2010 | 1180 | \n", + "| ⋮ | ⋮ | ⋮ | ⋮ | \n", + "| 28 | 2 | 2007 | 1 | \n", + "| 28 | 4 | 2008 | 1 | \n", + "| 29 | 1 | 2007 | 1 | \n", + "| 29 | 2 | 2005 | 1 | \n", + "| 29 | 2 | 2007 | 1 | \n", + "| 29 | 5 | 2007 | 1 | \n", + "| 32 | 2 | 2008 | 1 | \n", + "| 33 | 2 | 2012 | 1 | \n", + "| 35 | 4 | 2005 | 1 | \n", + "| 36 | 2 | 2006 | 1 | \n", + "| 36 | 3 | 2010 | 1 | \n", + "| 38 | 4 | 2012 | 1 | \n", + "| 40 | 2 | 2010 | 1 | \n", + "| 40 | 4 | 2007 | 1 | \n", + "| 41 | 2 | 2006 | 1 | \n", + "| 42 | 1 | 2010 | 1 | \n", + "| 42 | 2 | 2012 | 1 | \n", + "| 42 | 8 | 2007 | 1 | \n", + "| 43 | 1 | 2010 | 1 | \n", + "| 43 | 4 | 2010 | 1 | \n", + "| 45 | 1 | 2006 | 1 | \n", + "| 45 | 3 | 2008 | 1 | \n", + "| 47 | 3 | 2008 | 1 | \n", + "| 48 | 1 | 2009 | 1 | \n", + "| 51 | 2 | 2011 | 1 | \n", + "| 51 | 34 | 2011 | 1 | \n", + "| 62 | 2 | 2008 | 1 | \n", + "| 63 | 2 | 2011 | 1 | \n", + "| 68 | 1 | 2007 | 1 | \n", + "| 87 | 5 | 2011 | 1 | \n", + "\n", + "\n" + ], + "text/plain": [ + " Number_of_Casualties Number_of_Vehicles year n \n", + "1 1 1 2005 52027\n", + "2 1 1 2006 49358\n", + "3 1 1 2007 48023\n", + "4 1 1 2008 46121\n", + "5 1 1 2009 44076\n", + "6 1 1 2010 41760\n", + "7 1 1 2011 41082\n", + "8 1 1 2012 39217\n", + "9 1 2 2005 86717\n", + "10 1 2 2006 82553\n", + "11 1 2 2007 79828\n", + "12 1 2 2008 74819\n", + "13 1 2 2009 71802\n", + "14 1 2 2010 68416\n", + "15 1 2 2011 68019\n", + "16 1 2 2012 65680\n", + "17 1 3 2005 9756\n", + "18 1 3 2006 9122\n", + "19 1 3 2007 8610\n", + "20 1 3 2008 7795\n", + "21 1 3 2009 7249\n", + "22 1 3 2010 6701\n", + "23 1 3 2011 6379\n", + "24 1 3 2012 6168\n", + "25 1 4 2005 1714\n", + "26 1 4 2006 1574\n", + "27 1 4 2007 1612\n", + "28 1 4 2008 1373\n", + "29 1 4 2009 1280\n", + "30 1 4 2010 1180\n", + "⋮ ⋮ ⋮ ⋮ ⋮ \n", + "925 28 2 2007 1 \n", + "926 28 4 2008 1 \n", + "927 29 1 2007 1 \n", + "928 29 2 2005 1 \n", + "929 29 2 2007 1 \n", + "930 29 5 2007 1 \n", + "931 32 2 2008 1 \n", + "932 33 2 2012 1 \n", + "933 35 4 2005 1 \n", + "934 36 2 2006 1 \n", + "935 36 3 2010 1 \n", + "936 38 4 2012 1 \n", + "937 40 2 2010 1 \n", + "938 40 4 2007 1 \n", + "939 41 2 2006 1 \n", + "940 42 1 2010 1 \n", + "941 42 2 2012 1 \n", + "942 42 8 2007 1 \n", + "943 43 1 2010 1 \n", + "944 43 4 2010 1 \n", + "945 45 1 2006 1 \n", + "946 45 3 2008 1 \n", + "947 47 3 2008 1 \n", + "948 48 1 2009 1 \n", + "949 51 2 2011 1 \n", + "950 51 34 2011 1 \n", + "951 62 2 2008 1 \n", + "952 63 2 2011 1 \n", + "953 68 1 2007 1 \n", + "954 87 5 2011 1 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cv_date %>% count(Number_of_Casualties, Number_of_Vehicles, yearf) -> cvyf_xtab\n", + "cv_date %>% count(Number_of_Casualties, Number_of_Vehicles, year) -> cvy_xtab\n", + "cvy_xtab" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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jt0I3xe6ogL025McfZsUSd5eXnl5eVr1qxxuVyPPvro0KFDd+3alZGRkZ+fX1hY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\nW1eVvZinisx745E9Xdr2uN1VqNSWj7mCl9xDP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p/KuZR3cM7yZOcqS+0c+tYbNT2X47J5zklppSg8H4kGzYz+nj7RxPWu5uDrXqWC5xxO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\nSUVORK5CYII=", + "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ggplot(cvyf_xtab, aes(x=Number_of_Casualties, y=Number_of_Vehicles, size=n, color=yearf)) +\n", + " geom_point(alpha=0.5) +\n", + " scale_fill_hue(l=40)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = Number_of_Casualties ~ Number_of_Vehicles, data = cvy_xtab)\n", + "\n", + "Coefficients:\n", + " (Intercept) Number_of_Vehicles \n", + " 10.2691 -0.4065 \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "lm_all = lm(Number_of_Casualties~Number_of_Vehicles, data=cvy_xtab)\n", + "lm_all" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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JEjR5aXl2/fvt3n8wkhIpHI9ddfX1pa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sc5kAYFIWkSTWYak74b8qkLfHwwAgJfCI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sR/+2SF/FKi5irj1K7tAQM8NUA9NUAdCVpf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te/3112fOnHn//fd35fiTJ09mZmY+/vjjhJBb\nb7316quvrq+vX7Zs2VVXXdV3lkZ88cUXMcZWq1V5GB0djTEObpEAAIMKE+wCAAAQwzD33nvv\n9u3bg12QXlVWVoYQeu6559LS0rpy/OLFi0+cOPH+++/fdtttyhZRFO+7774NGzasW7duxYoV\nl7CsFyo8PDzYRQAADC5wxw6A4Hv88cd37NjxwQcfBLsgXeXxeA4cOHCRJ1Fus6nV6q4c/O23\n3+bl5a1Zs6Y11SGEGIZ5/fXXQ0NDN27ceJGFuUSOHDlSU1MT7FIAAAYRCHYABN/DDz+clpa2\nevXqlpaWDg8YM2bMwoUL/bcsXLiwtQfewoULr7nmmoMHD86ZM8disWRlZX311VeCIDz00EOp\nqakmk2nBggVVVVX+T//oo48mTpxoMpmys7PXr1/vv6u8vPyGG25ITEw0mUzTpk3btGlT6675\n8+cvWrTo22+/jYyMXLRoUVfe2oEDB6688sqoqKjo6Ogrr7zy4MGDyvZFixYtW7YMIZSYmDh/\n/vzznueVV17R6/XnNtqyLLthw4Ybb7zR5/O1vrWcnByLxcJx3NixY995553Wgx0Ox+OPP56a\nmqrT6VJSUh5++GGXy6XsCnyFA5+21YwZM1avXo0QCgsLUwLo/Pnzx40b13pAgGsboGwAANB1\nEOwACD61Wr1u3br6+vonnnjiws5QUFDwyCOP/PGPf/zpp5/0ev3ixYsnTZpkMpm+//77t99+\ne/PmzatWrWo9+NNPP73nnnuysrJWrFjhcrnuvffe1tEbhw8fzszM3LVr14033vjQQw81NTUt\nWLDg3XffbX1uWVnZbbfdNn/+/Icffvi8pcrNzZ04ceKxY8eWLl26dOnS/Pz8CRMm5ObmIoSe\neeYZ5Qz/+te//vKXv5z3VMeOHRs1apTFYjl317XXXvvoo4+yLIsQ+vzzz2+55RaM8SOPPHLP\nPfeIonjXXXd9+umnypG33377Cy+8MHr06P/5n/8ZNmzY2rVrH3zwwfO+9HlP2+qVV15Zvnw5\nQuirr74696MMfG0vuGwAANAGAQAEj3LXSvn5hhtuoChq//79ysPx48ePHDlS+TkzM3PBggX+\nT1ywYEHr3gULFtA0XVFRoTzctm0bQmjx4sWtB1999dVDhgwhhIiiiBDCGO/du1fZ5Xa7J0yY\nwLKs8vRp06bFx8dbrVZlr8/nmz59utFodDgchJB58+YhhDZu3NiVtyZJ0siRI2NjYxsaGpQt\njY2NMTExo0ePlmWZEKLc9GotdgAulwtjfOONN573yGuuuSYuLo7neeWh1+vlOO73v/89IcRm\ns2GMV65c2Xrw4sWL09LSlJ8DX+EApyWETJ48efLkycrPa9euRQg1NjYqD+fNm5eVlaX8HODa\nBi4bAAB0HdyxA6CveOmll/R6/fLlyy9g/o7k5OSEhATl58jISITQzJkzW/dGRUV5PJ7WhzNn\nzszJyVF+1mq1Tz31lM/n27p1a3Nz8/bt23//+9+HhIQoe1Uq1f333+9wOPLy8pQtZrN5yZIl\nXSlSRUXF0aNHly9fHhYWpmwJDQ295557Dh8+fPLkyW69O6/XSwjpSm+8t99++8iRI8rdO4SQ\nw+GQJMntdiOElNGpO3fubG2V/vjjj4uKirpSgACn7aLA1/ZiygYAAP4g2AHQV8TExDzzzDMH\nDhz461//2t3n6vX61p+VlHDullYjR470fzh27FiEUGlpqZIk1qxZg/1cf/31CKGGhgbl4NjY\nWIrqUr1RWlp67mspD5VdXRcSEmI2m5VRtOdqamo6fPhwU1MTQig0NNRqtX7wwQd/+MMfpk+f\nHhcX19pTzWg0PvPMM4cOHUpISJg+ffoTTzyxd+/eLhYgwGm7KPC1vZiyAQCAPwh2APQhK1as\nuOyyy5544om6urrAR3q93p56UXJmdKpyR+qxxx7bdo7p06crB2u12m6dth0lFCotwt2SlpZ2\n9OhR//uOrZ577rnMzMzCwkKE0Ouvvz58+PAHH3ywvr7+pptu2rNnz5AhQ1qPfPLJJ48cObJm\nzRpJkl588cUJEyZcddVVkiR1+Ir+VzjwabvivNe2W2UDAIDOQLADoA9hGObNN9+02WznDk1o\n1z7b3Zte/o4cOeL/UBmpmpqaOnToUIQQRVHT/CiTzJnN5u6+SkpKCkKooKDAf6MyD3MXJ67z\nd+eddzY3N7/xxhvttoui+J///Een040bN87lcj388MM333xzfX39Bx98cPfdd48ZM4bneeVI\nm81WVFSUlJT09NNP79y5s7a2dtmyZV9//fV3332nHNDZFQ582i4KfG3PWzYAAOgiCHYA9C2T\nJk1aunTpBx984B+JtFptYWFh6/2bTZs2VVRUXPBL/Pjjjzt27FB+9ng8f/zjH00m09y5czmO\nmzlz5oYNG1obXti0Nf4AAAMxSURBVGVZXrJkyY033qhSqbr7KsnJycOGDXvzzTebm5uVLU1N\nTevXrx8+fHhrd8Cu+93vfpeamvrUU0/985//bN0oy/KTTz5ZXFy8fPlylUpVXl7O83xWVhZN\n08oBP/zwQ319vZLYDhw4kJGR8dZbbym7zGbzVVddhc7kuQBXOPBpO3TursDXNnDZAACg62Dl\nCQD6nOeff/7LL79sampqbe+bOXPms88++9vf/va6664rLS195513pkyZ0hqYuis7O3v+/PlL\nly4NCwv77LPPjh49+tprrykzibzwwgtTp04dPXr00qVLaZr+9ttvf/755w8++KA103QdRVEv\nvfTSwoULs7Kybr31VkLIhx9+WFdXt3Hjxi720vPHMMwnn3wyZ86cm2+++aWXXho3bhxFUbt2\n7Tp8+PC4ceOeffZZhFBaWlpcXNyf//znhoaG5OTkffv2ffbZZ3FxcVu2bPnb3/62aNGipKSk\nNWvWHD58eMSIEUVFRV9++WVSUpLSEhrgCgc+7R133OFfTiUBv/zyy1deeeXkyZP9dwW4tuPH\njw9QNgAA6IbgDsoFYJDzn+7E34YNGxBCrdNteL3eVatWxcbGms3mOXPm5OXlvfXWW8uWLVP2\nLliwIDMzs/W5Sm+zDz/8sHXLvffem5qaSgiRJGnWrFlbtmxZv359VlYWx3GTJk3697//7f/S\nRUVFyuweJpNp0qRJ33zzTesu/8k7uigvL2/u3LmRkZGRkZHz5s07cOBA666uT3fSqrGx8bHH\nHhs2bJhWq42IiJg8efKrr74qimLrAUeOHJk1axbHcfHx8TfddFNFRcWePXumTp2qXKuioqLF\nixfHxMSo1erExMRly5adOHFCeWLgKxz4tP7TnVRUVMyYMUOn0913333nXrEA1zZA2QAAoOsw\n6TOLZwMAAAAAgIsBfewAAAAAAAYI6GMHALgQ77//futCZB1aunTpn//8514+FQAADHLQFAsA\nAAAAMEBAUywAAAAAwAABwQ4AAAAAYICAYAcAAAAAMEBAsAMAAAAAGCAg2AEAAAAADBAQ7AAA\nAAAABggIdgAAAAAAAwQEOwAAAACAAeL/A3LNrJ61xjWKAAAAAElFTkSuQmCC", + "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ggplot(cvyf_xtab, aes(x=Number_of_Casualties, y=Number_of_Vehicles, size=n, color=yearf)) +\n", + " geom_point(alpha=0.5) +\n", + " scale_fill_hue(l=40) + \n", + " geom_smooth(method = \"lm\", se = FALSE)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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ACgEhQ7AAAAlaDYAQAAqATFDgAAQCUo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oMAxjGMbzvGEPZJgoiqJp2vO8h5+N+wrH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\n69aMjIxQyUceeaSmp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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ggplot(filter(cvy_xtab, year==2011), aes(x=Number_of_Casualties, y=Number_of_Vehicles, size=n, color=year)) +\n", + " geom_point(alpha=0.5) +\n", + " scale_fill_hue(l=40) + \n", + " geom_smooth(method = \"lm\", se = FALSE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pearson _r_ is defined as \n", + "$$r = \\frac{\\sum{(x-m_x)(y-m_y)}}{\\sqrt{\\sum{(x-m_x)^2}\\sum{(y-m_y)^2}}}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tPearson's product-moment correlation\n", + "\n", + "data: cv_date$Number_of_Casualties and cv_date$Number_of_Vehicles\n", + "t = 276.26, df = 1355600, p-value < 2.2e-16\n", + "alternative hypothesis: true correlation is not equal to 0\n", + "95 percent confidence interval:\n", + " 0.2292713 0.2324586\n", + "sample estimates:\n", + " cor \n", + "0.2308656 \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cor.test(cv_date$Number_of_Casualties, cv_date$Number_of_Vehicles, method=\"pearson\")" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = Number_of_Casualties ~ Number_of_Vehicles + year, \n", + " data = cvy_xtab)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-8.918 -4.612 -1.804 1.818 78.806 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 42.76083 233.54983 0.183 0.855 \n", + "Number_of_Vehicles -0.40683 0.06826 -5.960 3.54e-09 ***\n", + "year -0.01618 0.11628 -0.139 0.889 \n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 8.219 on 951 degrees of freedom\n", + "Multiple R-squared: 0.03601,\tAdjusted R-squared: 0.03398 \n", + "F-statistic: 17.76 on 2 and 951 DF, p-value: 2.667e-08\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "lm_fit <- lm(Number_of_Casualties ~ Number_of_Vehicles + year, data=cvy_xtab)\n", + "summary(lm_fit)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "predicted_df <- data.frame(noc_pred = predict(lm_fit, cvy_xtab), Number_of_Vehicles=cvy_xtab$Number_of_Vehicles)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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yToZscBAJiJGbug8fTsKYSwcDYWZkhL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\nr7E5bW7mzPwuXQyLFhVbYyIRs359Xkmnvc+fr61fv9he0b27ITq6fKdSvf6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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ggplot(data = cvy_xtab, aes(x = Number_of_Casualties, y = Number_of_Vehicles)) + \n", + " geom_point(color='blue') +\n", + " geom_line(color='red',data = predicted_df, aes(x=noc_pred, y=Number_of_Vehicles))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "3.4.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/anaerob.csv b/anaerob.csv new file mode 100644 index 0000000..c532000 --- /dev/null +++ b/anaerob.csv @@ -0,0 +1,54 @@ +ventil,oxygen +21.9,574 +18.6,592 +18.6,664 +19.1,667 +19.2,718 +16.9,770 +18.3,927 +17.2,947 +19,1020 +19,1096 +18.6,1277 +22.8,1323 +24.6,1330 +24.9,1599 +29.2,1639 +32,1787 +27.9,1790 +31,1794 +30.7,1874 +35.4,2049 +36.1,2132 +39.1,2160 +42.6,2292 +39.9,2312 +46.2,2475 +50.9,2489 +46.5,2490 +46.3,2577 +55.8,2766 +54.5,2812 +63.5,2893 +60.3,2957 +64.8,3052 +69.2,3151 +74.7,3161 +72.9,3266 +80.4,3386 +83,3452 +86,3521 +88.9,3543 +96.8,3676 +89.1,3741 +100.9,3844 +103,3878 +113.4,4002 +111.4,4114 +119.9,4152 +127.2,4252 +126.4,4290 +135.5,4331 +138.9,4332 +143.7,4390 +144.8,4393 diff --git a/cemheat.csv b/cemheat.csv new file mode 100644 index 0000000..04a9124 --- /dev/null +++ b/cemheat.csv @@ -0,0 +1,14 @@ +heat,TA,TS +78.5,7,26 +74.3,1,29 +104.3,11,56 +87.6,11,31 +95.9,7,52 +109.2,11,55 +102.7,3,71 +72.5,1,31 +93.1,2,54 +115.9,21,47 +83.8,1,40 +113.3,11,66 +109.4,10,68 diff --git a/rubber.csv b/rubber.csv new file mode 100644 index 0000000..a5bcffe --- /dev/null +++ b/rubber.csv @@ -0,0 +1,31 @@ +loss,hardness,strength +372,45,162 +206,55,233 +175,61,232 +154,66,231 +136,71,231 +112,71,237 +55,81,224 +45,86,219 +221,53,203 +166,60,189 +164,64,210 +113,68,210 +82,79,196 +32,81,180 +228,56,200 +196,68,173 +128,75,188 +97,83,161 +64,88,119 +249,59,161 +219,71,151 +186,80,165 +155,82,151 +114,89,128 +341,51,161 +340,59,146 +283,65,148 +267,74,144 +215,81,134 +148,86,127 diff --git a/section5.1.ipynb b/section5.1.ipynb new file mode 100644 index 0000000..5b433be --- /dev/null +++ b/section5.1.ipynb @@ -0,0 +1,840 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Section 5.1: Using the model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "### Imports and defintions" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "library(tidyverse)\n", + "# library(cowplot)\n", + "library(repr)\n", + "library(ggfortify)\n", + "\n", + "# Change plot size to 4 x 3\n", + "options(repr.plot.width=6, repr.plot.height=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "# Multiple plot function\n", + "#\n", + "# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)\n", + "# - cols: Number of columns in layout\n", + "# - layout: A matrix specifying the layout. If present, 'cols' is ignored.\n", + "#\n", + "# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),\n", + "# then plot 1 will go in the upper left, 2 will go in the upper right, and\n", + "# 3 will go all the way across the bottom.\n", + "#\n", + "multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {\n", + " library(grid)\n", + "\n", + " # Make a list from the ... arguments and plotlist\n", + " plots <- c(list(...), plotlist)\n", + "\n", + " numPlots = length(plots)\n", + "\n", + " # If layout is NULL, then use 'cols' to determine layout\n", + " if (is.null(layout)) {\n", + " # Make the panel\n", + " # ncol: Number of columns of plots\n", + " # nrow: Number of rows needed, calculated from # of cols\n", + " layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),\n", + " ncol = cols, nrow = ceiling(numPlots/cols))\n", + " }\n", + "\n", + " if (numPlots==1) {\n", + " print(plots[[1]])\n", + "\n", + " } else {\n", + " # Set up the page\n", + " grid.newpage()\n", + " pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))\n", + "\n", + " # Make each plot, in the correct location\n", + " for (i in 1:numPlots) {\n", + " # Get the i,j matrix positions of the regions that contain this subplot\n", + " matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))\n", + "\n", + " print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,\n", + " layout.pos.col = matchidx$col))\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modelling abrasion loss\n", + "\n", + "This example concerns the dataset `rubber`, which you first met in Exercise 3.1. The experiment introduced in that exercise concerned an investigation of the resistance of rubber to abrasion (the abrasion loss) and how that resistance depended on various attributes of the rubber. In Exercise 3.1, the only explanatory variable we analysed was hardness; but in Exercise 3.19, a second explanatory variable, tensile strength, was taken into account too. There are 30 datapoints." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
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\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lll}\n", + " loss & hardness & strength\\\\\n", + "\\hline\n", + "\t 372 & 45 & 162\\\\\n", + "\t 206 & 55 & 233\\\\\n", + "\t 175 & 61 & 232\\\\\n", + "\t 154 & 66 & 231\\\\\n", + "\t 136 & 71 & 231\\\\\n", + "\t 112 & 71 & 237\\\\\n", + "\t 55 & 81 & 224\\\\\n", + "\t 45 & 86 & 219\\\\\n", + "\t 221 & 53 & 203\\\\\n", + "\t 166 & 60 & 189\\\\\n", + "\t 164 & 64 & 210\\\\\n", + "\t 113 & 68 & 210\\\\\n", + "\t 82 & 79 & 196\\\\\n", + "\t 32 & 81 & 180\\\\\n", + "\t 228 & 56 & 200\\\\\n", + "\t 196 & 68 & 173\\\\\n", + "\t 128 & 75 & 188\\\\\n", + "\t 97 & 83 & 161\\\\\n", + "\t 64 & 88 & 119\\\\\n", + "\t 249 & 59 & 161\\\\\n", + "\t 219 & 71 & 151\\\\\n", + "\t 186 & 80 & 165\\\\\n", + "\t 155 & 82 & 151\\\\\n", + "\t 114 & 89 & 128\\\\\n", + "\t 341 & 51 & 161\\\\\n", + "\t 340 & 59 & 146\\\\\n", + "\t 283 & 65 & 148\\\\\n", + "\t 267 & 74 & 144\\\\\n", + "\t 215 & 81 & 134\\\\\n", + "\t 148 & 86 & 127\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "loss | hardness | strength | \n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 372 | 45 | 162 | \n", + "| 206 | 55 | 233 | \n", + "| 175 | 61 | 232 | \n", + "| 154 | 66 | 231 | \n", + "| 136 | 71 | 231 | \n", + "| 112 | 71 | 237 | \n", + "| 55 | 81 | 224 | \n", + "| 45 | 86 | 219 | \n", + "| 221 | 53 | 203 | \n", + "| 166 | 60 | 189 | \n", + "| 164 | 64 | 210 | \n", + "| 113 | 68 | 210 | \n", + "| 82 | 79 | 196 | \n", + "| 32 | 81 | 180 | \n", + "| 228 | 56 | 200 | \n", + "| 196 | 68 | 173 | \n", + "| 128 | 75 | 188 | \n", + "| 97 | 83 | 161 | \n", + "| 64 | 88 | 119 | \n", + "| 249 | 59 | 161 | \n", + "| 219 | 71 | 151 | \n", + "| 186 | 80 | 165 | \n", + "| 155 | 82 | 151 | \n", + "| 114 | 89 | 128 | \n", + "| 341 | 51 | 161 | \n", + "| 340 | 59 | 146 | \n", + "| 283 | 65 | 148 | \n", + "| 267 | 74 | 144 | \n", + "| 215 | 81 | 134 | \n", + "| 148 | 86 | 127 | \n", + "\n", + "\n" + ], + "text/plain": [ + " loss hardness strength\n", + "1 372 45 162 \n", + "2 206 55 233 \n", + "3 175 61 232 \n", + "4 154 66 231 \n", + "5 136 71 231 \n", + "6 112 71 237 \n", + "7 55 81 224 \n", + "8 45 86 219 \n", + "9 221 53 203 \n", + "10 166 60 189 \n", + "11 164 64 210 \n", + "12 113 68 210 \n", + "13 82 79 196 \n", + "14 32 81 180 \n", + "15 228 56 200 \n", + "16 196 68 173 \n", + "17 128 75 188 \n", + "18 97 83 161 \n", + "19 64 88 119 \n", + "20 249 59 161 \n", + "21 219 71 151 \n", + "22 186 80 165 \n", + "23 155 82 151 \n", + "24 114 89 128 \n", + "25 341 51 161 \n", + "26 340 59 146 \n", + "27 283 65 148 \n", + "28 267 74 144 \n", + "29 215 81 134 \n", + "30 148 86 127 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rubber <- read.csv('rubber.csv')\n", + "rubber" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The figures below show scatterplots of abrasion loss against hardness and of abrasion loss against strength. (A scatterplot of abrasion loss against hardness also appeared in Solution 3.1.) Figure (a) suggests a strong decreasing linear relationship of abrasion loss with hardness. Figure (b) is much less indicative of any strong dependence of abrasion loss on tensile strength." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hardloss <- ggplot(rubber, aes(x=hardness, y=loss)) + geom_point()\n", + "strloss <- ggplot(rubber, aes(x=strength, y=loss)) + geom_point()\n", + "\n", + "multiplot(hardloss, strloss, cols=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In exercise 3.5(a) you obtained the following output for the regression of abrasion loss on hardness." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = loss ~ hardness, data = rubber)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-86.15 -46.77 -19.49 54.27 111.49 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 550.4151 65.7867 8.367 4.22e-09 ***\n", + "hardness -5.3366 0.9229 -5.782 3.29e-06 ***\n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 60.52 on 28 degrees of freedom\n", + "Multiple R-squared: 0.5442,\tAdjusted R-squared: 0.5279 \n", + "F-statistic: 33.43 on 1 and 28 DF, p-value: 3.294e-06\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
DfSum SqMean SqF valuePr(>F)
hardness 1 122455.0 122455.037 33.43276 3.294489e-06
Residuals28 102556.3 3662.726 NA NA
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllll}\n", + " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", + "\\hline\n", + "\thardness & 1 & 122455.0 & 122455.037 & 33.43276 & 3.294489e-06\\\\\n", + "\tResiduals & 28 & 102556.3 & 3662.726 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n", + "|---|---|\n", + "| hardness | 1 | 122455.0 | 122455.037 | 33.43276 | 3.294489e-06 | \n", + "| Residuals | 28 | 102556.3 | 3662.726 | NA | NA | \n", + "\n", + "\n" + ], + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "hardness 1 122455.0 122455.037 33.43276 3.294489e-06\n", + "Residuals 28 102556.3 3662.726 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fit <- lm(loss ~ hardness, data = rubber)\n", + "summary(fit)\n", + "anova(fit)\n", + "# af <- anova(fit)\n", + "# afss <- af$\"Sum Sq\"\n", + "# print(cbind(af,PctExp=afss/sum(afss)*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "solution": "shown", + "solution2": "hidden", + "solution2_first": true, + "solution_first": true + }, + "source": [ + "### Exercise 5.1\n", + "Now repeat the for the regression of abrasion loss on tensile strength.\n", + "\n", + "Enter your solution in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your solution here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solution" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "solution2": "hidden" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = loss ~ strength, data = rubber)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-155.640 -59.919 2.795 61.221 183.285 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 305.2248 79.9962 3.815 0.000688 ***\n", + "strength -0.7192 0.4347 -1.654 0.109232 \n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 85.56 on 28 degrees of freedom\n", + "Multiple R-squared: 0.08904,\tAdjusted R-squared: 0.0565 \n", + "F-statistic: 2.737 on 1 and 28 DF, p-value: 0.1092\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
DfSum SqMean SqF valuePr(>F)
strength 1 20034.7720034.7722.736769 0.1092317
Residuals28 204976.59 7320.593 NA NA
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllll}\n", + " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", + "\\hline\n", + "\tstrength & 1 & 20034.77 & 20034.772 & 2.736769 & 0.1092317\\\\\n", + "\tResiduals & 28 & 204976.59 & 7320.593 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n", + "|---|---|\n", + "| strength | 1 | 20034.77 | 20034.772 | 2.736769 | 0.1092317 | \n", + "| Residuals | 28 | 204976.59 | 7320.593 | NA | NA | \n", + "\n", + "\n" + ], + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "strength 1 20034.77 20034.772 2.736769 0.1092317\n", + "Residuals 28 204976.59 7320.593 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fit <- lm(loss ~ strength, data = rubber)\n", + "summary(fit)\n", + "anova(fit)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "solution2": "hidden" + }, + "source": [ + "Individually, then, regression of abrasion loss on hardness seems satisfactory, albeit accounting for a disappointingly small percentage of the variance in the response; regression of abrasion loss on tensile strength, on the other hand, seems to have very little explanatory power. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "solution": "hidden" + }, + "source": [ + "### Multiple regression\n", + "However, back in Exercise 3.19 it was shown that the residuals from the former regression showed a clear relationship with the tensile strength values, and this suggested that a regression model involving both variables is necessary. \n", + "\n", + "Let us try it. The only change is to include `strength` in the equations being fitted in the `lm()` function. Instead of \n", + "\n", + "```\n", + "lm(loss ~ hardness, data = rubber)\n", + "```\n", + "use \n", + "```\n", + "lm(loss ~ hardness + strength, data = rubber)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = loss ~ hardness + strength, data = rubber)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-79.385 -14.608 3.816 19.755 65.981 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 885.1611 61.7516 14.334 3.84e-14 ***\n", + "hardness -6.5708 0.5832 -11.267 1.03e-11 ***\n", + "strength -1.3743 0.1943 -7.073 1.32e-07 ***\n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 36.49 on 27 degrees of freedom\n", + "Multiple R-squared: 0.8402,\tAdjusted R-squared: 0.8284 \n", + "F-statistic: 71 on 2 and 27 DF, p-value: 1.767e-11\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
DfSum SqMean SqF valuePr(>F)
hardness 1 122455.04 122455.037 91.96967 3.458255e-10
strength 1 66606.59 66606.586 50.02477 1.324645e-07
Residuals27 35949.74 1331.472 NA NA
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllll}\n", + " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", + "\\hline\n", + "\thardness & 1 & 122455.04 & 122455.037 & 91.96967 & 3.458255e-10\\\\\n", + "\tstrength & 1 & 66606.59 & 66606.586 & 50.02477 & 1.324645e-07\\\\\n", + "\tResiduals & 27 & 35949.74 & 1331.472 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n", + "|---|---|---|\n", + "| hardness | 1 | 122455.04 | 122455.037 | 91.96967 | 3.458255e-10 | \n", + "| strength | 1 | 66606.59 | 66606.586 | 50.02477 | 1.324645e-07 | \n", + "| Residuals | 27 | 35949.74 | 1331.472 | NA | NA | \n", + "\n", + "\n" + ], + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "hardness 1 122455.04 122455.037 91.96967 3.458255e-10\n", + "strength 1 66606.59 66606.586 50.02477 1.324645e-07\n", + "Residuals 27 35949.74 1331.472 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fit <- lm(loss ~ hardness + strength, data = rubber)\n", + "summary(fit)\n", + "anova(fit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the regression coefficient output next, the estimated model for the mean response is\n", + "\n", + "$$ \\hat{y} = \\hat{\\alpha} + \\hat{\\beta}_1 x_1 + \\hat{\\beta}_2 x_2 = 885.2 − 6.571 x_1 − 1.374 x_2 $$ \n", + "\n", + "where $x_1$ and $x_2$ stand for values of hardness and tensile strength, respectively. \n", + "\n", + "Associated with each estimated parameter, GenStat gives a standard error (details of the calculation of which need not concern us now) and hence a _t_-statistic (estimate divided by standard error) to be compared with the distribution on `d.f. (Residual)`=27 degrees of freedom. GenStat makes the comparison and gives _p_ values, which in this case are all very small, suggesting strong evidence for the non-zeroness (and hence presence) of each parameter, $\\alpha$, $\\beta_1$ and $\\beta_2$, individually." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, even though the single regression of abrasion loss on tensile strength did not suggest a close relationship, when taken in conjunction with the effect of hardness the effect of tensile strength is also a considerable one. A key idea here is that $\\beta_1$ and $\\beta_2$ (and more generally $\\beta_1, \\beta_2, \\ldots , \\beta_k$ in model (5.1)) are partial regression coefficients. That is, $\\beta_1$ measures the effect of an increase of one unit in $x_1$ treating the value of the other variable $x_2$ as fixed. Contrast this with the single regression model $E(Y) = \\alpha + \\beta_1 x_1$ in which $\\beta_1$ represents an increase of one unit in $x_1$ treating $x_2$ as zero. The meaning of $\\beta_1$ in the regression models with one and two explanatory variables is not the same.\n", + "\n", + "You will notice that the percentage of variance accounted for has increased dramatically in the two-explanatory-variable model to a much more respectable 82.8%. This statistic has the same interpretation — or difficulty of interpretation — as for the case of one explanatory variable (see [Unit 3](unit3.ipynb)).\n", + "\n", + "Other items in the GenStat output are as for the case of one explanatory variable: the `Standard error of observations` is $\\hat{\\sigma}$, and is the square root of `m.s. (Residual)`; the message about standardised residuals will be clarified below; and the message about leverage will be ignored until [Unit 10](unit10.ipynb).\n", + "\n", + "The simple residuals are, again, defined as the differences between the observed and predicted responses:\n", + "\n", + "$$ r_i = y_i - \\left( \\hat{\\alpha} + \\sum_{j=1}^{k} \\hat{\\beta}_j x_{i, j} \\right) ,\\ i = 1, 2, \\ldots, n. $$\n", + "\n", + "GenStat can obtain these for you and produce a plot of residuals against fitted values. The fitted values are simply $\\hat{Y}_i = \\hat{\\alpha} + \\sum_{j=1}^{k} \\hat{\\beta}_j x_{i, j}$. As in the regression models in [Unit 3](unit3.ipynb), the default in GenStat is to use deviance residuals which, in this context, are equivalent to standardised residuals (simple residuals divided by their estimated standard errors)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gratuitous example of more complex maths markup\n", + "If \n", + "\n", + "$$ \\rho(z) = \\rho_c \\exp \\left( - \\frac{z^2}{2H^2} \\right) $$\n", + "\n", + "then\n", + "\n", + "\\begin{eqnarray*}\n", + "\\frac{\\partial \\rho(z)}{\\partial z} & = & \\rho_c \\frac{\\partial}{\\partial z}\\exp \n", + " \\left( - \\frac{z^2}{2H^2} \\right) \\\\\n", + "\t& = & \\rho_c \\exp \\left( - \\frac{z^2}{2H^2} \\right) \\cdot \n", + "\t \\frac{\\partial}{\\partial z} \\left( - \\frac{z^2}{2H^2} \\right) \\\\\n", + "\t& = & \\rho_c \\exp \\left( - \\frac{z^2}{2H^2} \\right) \\cdot \n", + "\t - \\frac{z}{H^2} \\\\\n", + "\t& = & - \\frac{z}{H^2} \\rho(z) \n", + "\\end{eqnarray*}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 5.2\n", + "Something about plots of residuals..." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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LhWpCA6Fbm5uUVFRSqNjY2NMO8FAsEoVZqhywBeBKILc/DgQWVXokQi2bdvX6uq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\nEAgEAsEd6HBAIBAIBALBnf8F8z7wZ89es08AAAAASUVORK5CYII=", + "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fit <- lm(loss ~ hardness + strength, data = rubber)\n", + "autoplot(fit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These plots are of just the same sort as those in [Unit 3](unit3.ipynb), and are interpreted in the same way. In this case, the normal plot and histogram show some slight suggestion of skewness in the residuals, but there is no obvious cause to doubt the model. In the regression output, GenStat flagged one of the points (number 19) as having a large standardised (i.e. deviance) residual. To decide which points to flag in this way, GenStat uses the same rules as described in [Units 3](unit3.ipynb) and [4](unit4.ipynb). In this case, it warned us about the most negative residual. However, the value of this standardised residual is not very large (at −2.38), and the plot of residuals against fitted values in Figure 5.2 makes it clear that this particular residual is not exceptionally large relative to some of the others, which are almost as large." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 5.2\n", + "The table below shows values for the first five datapoints, of 13, of a dataset concerned with predicting the heat evolved when cement sets via knowledge of its constituents. There are two explanatory variables, tricalcium aluminate (TA) and tricalcium silicate (TS), and the response variable is the heat generated in calories per gram (heat).\n", + "\n", + "heat | TA | TS\n", + "-----|----|-----\n", + "78.5 | 7 | 26\n", + "74.3 | 1 | 29\n", + "104.3 | 11 | 56\n", + "87.6 | 11 | 31\n", + "95.9 | 7 | 52\n", + " $\\vdots$ | $\\vdots$ | $\\vdots$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "a. Load the `cemheat` dataset.\n", + "\n", + "b. Make scatterplots of heat against each of TA and TS in turn, and comment on what you see.\n", + "\n", + "c. Use GenStat to fit each individual regression equation (of heat on TA and of heat on TS) in turn, and then to fit the regression equation with two explanatory variables. Does the latter regression equation give you a better model than either of the individual ones?\n", + "\n", + "d. According to the regression equation with two explanatory variables fitted in part (b), what is the predicted value of heat when TA = 15 and TS = 55?\n", + "\n", + "e. By looking at the (default) composite residual plots, comment on the appropriateness of the fitted regression model.\n", + "\n", + "The solution is in the [Section 5.1 solutions](section5.1solutions.ipynb) notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "# Your solution here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 5.3: Fitting a quadratic regression model\n", + "In Unit 3, the dataset `anaerob` was briefly considered (Example 3.1) and swiftly dismissed as a candidate for simple linear regression modelling. The reason is clear from the figure below, in which the response variable, expired ventilation ($y$), is plotted against the single explanatory variable, oxygen uptake ($x$). (The same plot appeared as Figure 3.1 in Example 3.1.) A model quadratic in $x$, i.e. $E(Y) = \\alpha + \\beta x + \\gamma x^2$, was suggested instead." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "anaerobic <- read.csv('anaerob.csv')\n", + "ggplot(anaerobic, aes(x=oxygen, y=ventil)) + geom_point()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since this quadratic model is nonetheless linear in its parameters, we can fit it using multiple regression of $y$ on $x$ plus the new variable $x^2$. Even though $x_1 = x$ and $x_2 = x^2$ are closely related, there is no immediate impediment to forgetting this and regressing on them in the usual way. A variable such as $x_2$ is sometimes called a derived variable.\n", + "\n", + "a. Using GenStat, perform the regression of expired ventilation (`ventil`) on oxygen uptake (`oxygen`). Are you at all surprised by how good this regression model seems?\n", + "\n", + "b. Now form a new variable `oxy2`, say, by squaring oxygen. (Create a new column in the `anearobic` dataframe which is `anaerobic$oxygen ^ 2`.) Perform the regression of ventil on `oxygen` and `oxy2`. Comment on the fit of this model according to the printed output (and with recourse to Figure 3.2 in Example 3.1).\n", + "\n", + "c. Make the usual residual plots and comment on the fit of the model again.\n", + "\n", + "The solution is in the [Section 5.1 solutions](section5.1solutions.ipynb) notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "# Your solution here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next steps\n", + "Move to the next notebook, or go back to the course material, or something." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "3.4.2" + }, + "toc": { + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "342px" + }, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/section5.1solutions.ipynb b/section5.1solutions.ipynb new file mode 100644 index 0000000..cb823b8 --- /dev/null +++ b/section5.1solutions.ipynb @@ -0,0 +1,896 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Section 5.1 solutions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "### Imports and defintions" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "library(tidyverse)\n", + "# library(cowplot)\n", + "library(repr)\n", + "library(ggfortify)\n", + "\n", + "# Change plot size to 4 x 3\n", + "options(repr.plot.width=6, repr.plot.height=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "# Multiple plot function\n", + "#\n", + "# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)\n", + "# - cols: Number of columns in layout\n", + "# - layout: A matrix specifying the layout. If present, 'cols' is ignored.\n", + "#\n", + "# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),\n", + "# then plot 1 will go in the upper left, 2 will go in the upper right, and\n", + "# 3 will go all the way across the bottom.\n", + "#\n", + "multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {\n", + " library(grid)\n", + "\n", + " # Make a list from the ... arguments and plotlist\n", + " plots <- c(list(...), plotlist)\n", + "\n", + " numPlots = length(plots)\n", + "\n", + " # If layout is NULL, then use 'cols' to determine layout\n", + " if (is.null(layout)) {\n", + " # Make the panel\n", + " # ncol: Number of columns of plots\n", + " # nrow: Number of rows needed, calculated from # of cols\n", + " layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),\n", + " ncol = cols, nrow = ceiling(numPlots/cols))\n", + " }\n", + "\n", + " if (numPlots==1) {\n", + " print(plots[[1]])\n", + "\n", + " } else {\n", + " # Set up the page\n", + " grid.newpage()\n", + " pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))\n", + "\n", + " # Make each plot, in the correct location\n", + " for (i in 1:numPlots) {\n", + " # Get the i,j matrix positions of the regions that contain this subplot\n", + " matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))\n", + "\n", + " print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,\n", + " layout.pos.col = matchidx$col))\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 5.2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load the `cemheat` dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
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95.9 7 52
109.211 55
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lll}\n", + " heat & TA & TS\\\\\n", + "\\hline\n", + "\t 78.5 & 7 & 26 \\\\\n", + "\t 74.3 & 1 & 29 \\\\\n", + "\t 104.3 & 11 & 56 \\\\\n", + "\t 87.6 & 11 & 31 \\\\\n", + "\t 95.9 & 7 & 52 \\\\\n", + "\t 109.2 & 11 & 55 \\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "heat | TA | TS | \n", + "|---|---|---|---|---|---|\n", + "| 78.5 | 7 | 26 | \n", + "| 74.3 | 1 | 29 | \n", + "| 104.3 | 11 | 56 | \n", + "| 87.6 | 11 | 31 | \n", + "| 95.9 | 7 | 52 | \n", + "| 109.2 | 11 | 55 | \n", + "\n", + "\n" + ], + "text/plain": [ + " heat TA TS\n", + "1 78.5 7 26\n", + "2 74.3 1 29\n", + "3 104.3 11 56\n", + "4 87.6 11 31\n", + "5 95.9 7 52\n", + "6 109.2 11 55" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cemheat <- read.csv('cemheat.csv')\n", + "head(cemheat)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Make scatterplots of heat against each of TA and TS in turn, and comment on what you see." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "taheat <- ggplot(cemheat, aes(x=TA, y=heat)) + geom_point()\n", + "tsheat <- ggplot(cemheat, aes(x=TS, y=heat)) + geom_point()\n", + "\n", + "multiplot(taheat, tsheat, cols=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use GenStat to fit each individual regression equation (of heat on TA and of heat on TS) in turn, and then to fit the regression equation with two explanatory variables. Does the latter regression equation give you a better model than either of the individual ones?" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = heat ~ TA, data = cemheat)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-16.061 -9.048 1.339 7.883 15.614 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 81.4793 4.9273 16.54 4.07e-09 ***\n", + "TA 1.8687 0.5264 3.55 0.00455 ** \n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 10.73 on 11 degrees of freedom\n", + "Multiple R-squared: 0.5339,\tAdjusted R-squared: 0.4916 \n", + "F-statistic: 12.6 on 1 and 11 DF, p-value: 0.004552\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
DfSum SqMean SqF valuePr(>F)
TA 1 1450.076 1450.0763 12.60252 0.004552045
Residuals11 1265.687 115.0624 NA NA
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllll}\n", + " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", + "\\hline\n", + "\tTA & 1 & 1450.076 & 1450.0763 & 12.60252 & 0.004552045\\\\\n", + "\tResiduals & 11 & 1265.687 & 115.0624 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n", + "|---|---|\n", + "| TA | 1 | 1450.076 | 1450.0763 | 12.60252 | 0.004552045 | \n", + "| Residuals | 11 | 1265.687 | 115.0624 | NA | NA | \n", + "\n", + "\n" + ], + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "TA 1 1450.076 1450.0763 12.60252 0.004552045\n", + "Residuals 11 1265.687 115.0624 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fit <- lm(heat ~ TA, data = cemheat)\n", + "summary(fit)\n", + "anova(fit)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = heat ~ TS, data = cemheat)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-10.752 -6.008 -1.684 3.794 21.387 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 57.4237 8.4906 6.763 3.1e-05 ***\n", + "TS 0.7891 0.1684 4.686 0.000665 ***\n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 9.077 on 11 degrees of freedom\n", + "Multiple R-squared: 0.6663,\tAdjusted R-squared: 0.6359 \n", + "F-statistic: 21.96 on 1 and 11 DF, p-value: 0.0006648\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
DfSum SqMean SqF valuePr(>F)
TS 1 1809.4267 1809.42673 21.9606 0.0006648249
Residuals11 906.3363 82.39421 NA NA
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllll}\n", + " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", + "\\hline\n", + "\tTS & 1 & 1809.4267 & 1809.42673 & 21.9606 & 0.0006648249\\\\\n", + "\tResiduals & 11 & 906.3363 & 82.39421 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n", + "|---|---|\n", + "| TS | 1 | 1809.4267 | 1809.42673 | 21.9606 | 0.0006648249 | \n", + "| Residuals | 11 | 906.3363 | 82.39421 | NA | NA | \n", + "\n", + "\n" + ], + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "TS 1 1809.4267 1809.42673 21.9606 0.0006648249\n", + "Residuals 11 906.3363 82.39421 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fit <- lm(heat ~ TS, data = cemheat)\n", + "summary(fit)\n", + "anova(fit)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = heat ~ TA + TS, data = cemheat)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-2.893 -1.574 -1.302 1.363 4.048 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 52.57735 2.28617 23.00 5.46e-10 ***\n", + "TA 1.46831 0.12130 12.11 2.69e-07 ***\n", + "TS 0.66225 0.04585 14.44 5.03e-08 ***\n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 2.406 on 10 degrees of freedom\n", + "Multiple R-squared: 0.9787,\tAdjusted R-squared: 0.9744 \n", + "F-statistic: 229.5 on 2 and 10 DF, p-value: 4.407e-09\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
DfSum SqMean SqF valuePr(>F)
TA 1 1450.07633 1450.076328 250.4256 2.088092e-08
TS 1 1207.78227 1207.782266 208.5818 5.028960e-08
Residuals10 57.90448 5.790448 NA NA
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllll}\n", + " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", + "\\hline\n", + "\tTA & 1 & 1450.07633 & 1450.076328 & 250.4256 & 2.088092e-08\\\\\n", + "\tTS & 1 & 1207.78227 & 1207.782266 & 208.5818 & 5.028960e-08\\\\\n", + "\tResiduals & 10 & 57.90448 & 5.790448 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n", + "|---|---|---|\n", + "| TA | 1 | 1450.07633 | 1450.076328 | 250.4256 | 2.088092e-08 | \n", + "| TS | 1 | 1207.78227 | 1207.782266 | 208.5818 | 5.028960e-08 | \n", + "| Residuals | 10 | 57.90448 | 5.790448 | NA | NA | \n", + "\n", + "\n" + ], + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "TA 1 1450.07633 1450.076328 250.4256 2.088092e-08\n", + "TS 1 1207.78227 1207.782266 208.5818 5.028960e-08\n", + "Residuals 10 57.90448 5.790448 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fit <- lm(heat ~ TA + TS, data = cemheat)\n", + "summary(fit)\n", + "anova(fit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### According to the regression equation with two explanatory variables fitted in part (b), what is the predicted value of heat when TA = 15 and TS = 55?" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "1: 111.025712035428" + ], + "text/latex": [ + "\\textbf{1:} 111.025712035428" + ], + "text/markdown": [ + "**1:** 111.025712035428" + ], + "text/plain": [ + " 1 \n", + "111.0257 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "predict(fit, data.frame(\"TA\" = 15, \"TS\" = 55))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### By looking at the (default) composite residual plots, comment on the appropriateness of the fitted regression model." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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mNjZ8yYERERsXbtWn9/fwBAVlbW559//uDBg6NHj5q6dhDUQUmlUgBAdXW1mZnZ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CAQAAAADSQcIBAAAAANJBwgEAAAAA0kHC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\nUcdUE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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "autoplot(fit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 5.3: Fitting a quadratic regression model" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "anaerobic <- read.csv('anaerob.csv')\n", + "ggplot(anaerobic, aes(x=oxygen, y=ventil)) + geom_point()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using GenStat, perform the regression of expired ventilation (`ventil`) on oxygen uptake (`oxygen`). Are you at all surprised by how good this regression model seems?" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ventil ~ oxygen, data = anaerobic)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-15.502 -9.716 -3.391 7.881 26.446 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) -18.448734 3.815196 -4.836 1.26e-05 ***\n", + "oxygen 0.031141 0.001355 22.987 < 2e-16 ***\n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 11.96 on 51 degrees of freedom\n", + "Multiple R-squared: 0.912,\tAdjusted R-squared: 0.9103 \n", + "F-statistic: 528.4 on 1 and 51 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
DfSum SqMean SqF valuePr(>F)
oxygen 1 75555.216 75555.2158 528.403 1.419964e-28
Residuals51 7292.381 142.9879 NA NA
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllll}\n", + " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", + "\\hline\n", + "\toxygen & 1 & 75555.216 & 75555.2158 & 528.403 & 1.419964e-28\\\\\n", + "\tResiduals & 51 & 7292.381 & 142.9879 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n", + "|---|---|\n", + "| oxygen | 1 | 75555.216 | 75555.2158 | 528.403 | 1.419964e-28 | \n", + "| Residuals | 51 | 7292.381 | 142.9879 | NA | NA | \n", + "\n", + "\n" + ], + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "oxygen 1 75555.216 75555.2158 528.403 1.419964e-28\n", + "Residuals 51 7292.381 142.9879 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Df Sum Sq Mean Sq F value Pr(>F) PctExp\n", + "oxygen 1 75555.216 75555.2158 528.403 1.419964e-28 91.197836\n", + "Residuals 51 7292.381 142.9879 NA NA 8.802164\n" + ] + } + ], + "source": [ + "fit <- lm(ventil ~ oxygen, data = anaerobic)\n", + "summary(fit)\n", + "anova(fit)\n", + "af <- anova(fit)\n", + "afss <- af$\"Sum Sq\"\n", + "print(cbind(af,PctExp=afss/sum(afss)*100))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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np6U6dOnTp1qpWVlYaGxvLlywEAGhoa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\nizVr1qx79+4hh0+fPp0+fXpjY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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "autoplot(fit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now form a new variable `oxy2`, say, by squaring oxygen.\n", + "(Create a new column in the `anearobic` dataframe which is `anaerobic$oxygen ^ 2`.) Perform the regression of ventil on `oxygen` and `oxy2`. Comment on the fit of this model according to the printed output (and with recourse to Figure 3.2 in Example 3.1)." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
ventiloxygenoxy2
21.9 574 329476
18.6 592 350464
18.6 664 440896
19.1 667 444889
19.2 718 515524
16.9 770 592900
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lll}\n", + " ventil & oxygen & oxy2\\\\\n", + "\\hline\n", + "\t 21.9 & 574 & 329476\\\\\n", + "\t 18.6 & 592 & 350464\\\\\n", + "\t 18.6 & 664 & 440896\\\\\n", + "\t 19.1 & 667 & 444889\\\\\n", + "\t 19.2 & 718 & 515524\\\\\n", + "\t 16.9 & 770 & 592900\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "ventil | oxygen | oxy2 | \n", + "|---|---|---|---|---|---|\n", + "| 21.9 | 574 | 329476 | \n", + "| 18.6 | 592 | 350464 | \n", + "| 18.6 | 664 | 440896 | \n", + "| 19.1 | 667 | 444889 | \n", + "| 19.2 | 718 | 515524 | \n", + "| 16.9 | 770 | 592900 | \n", + "\n", + "\n" + ], + "text/plain": [ + " ventil oxygen oxy2 \n", + "1 21.9 574 329476\n", + "2 18.6 592 350464\n", + "3 18.6 664 440896\n", + "4 19.1 667 444889\n", + "5 19.2 718 515524\n", + "6 16.9 770 592900" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "anaerobic$oxy2 <- anaerobic$oxygen^2\n", + "head(anaerobic)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Call:\n", + "lm(formula = ventil ~ oxygen + oxy2, data = anaerobic)\n", + "\n", + "Residuals:\n", + " Min 1Q Median 3Q Max \n", + "-9.4713 -1.3675 -0.4201 2.1925 7.7817 \n", + "\n", + "Coefficients:\n", + " Estimate Std. Error t value Pr(>|t|) \n", + "(Intercept) 2.427e+01 1.940e+00 12.509 < 2e-16 ***\n", + "oxygen -1.344e-02 1.762e-03 -7.628 6.27e-10 ***\n", + "oxy2 8.902e-06 3.444e-07 25.850 < 2e-16 ***\n", + "---\n", + "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", + "\n", + "Residual standard error: 3.186 on 50 degrees of freedom\n", + "Multiple R-squared: 0.9939,\tAdjusted R-squared: 0.9936 \n", + "F-statistic: 4055 on 2 and 50 DF, p-value: < 2.2e-16\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
DfSum SqMean SqF valuePr(>F)
oxygen 1 75555.2158 75555.21580 7441.5695 4.587875e-56
oxy2 1 6784.7247 6784.72473 668.2398 1.357823e-30
Residuals50 507.6565 10.15313 NA NA
\n" + ], + "text/latex": [ + "\\begin{tabular}{r|lllll}\n", + " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", + "\\hline\n", + "\toxygen & 1 & 75555.2158 & 75555.21580 & 7441.5695 & 4.587875e-56\\\\\n", + "\toxy2 & 1 & 6784.7247 & 6784.72473 & 668.2398 & 1.357823e-30\\\\\n", + "\tResiduals & 50 & 507.6565 & 10.15313 & NA & NA\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) | \n", + "|---|---|---|\n", + "| oxygen | 1 | 75555.2158 | 75555.21580 | 7441.5695 | 4.587875e-56 | \n", + "| oxy2 | 1 | 6784.7247 | 6784.72473 | 668.2398 | 1.357823e-30 | \n", + "| Residuals | 50 | 507.6565 | 10.15313 | NA | NA | \n", + "\n", + "\n" + ], + "text/plain": [ + " Df Sum Sq Mean Sq F value Pr(>F) \n", + "oxygen 1 75555.2158 75555.21580 7441.5695 4.587875e-56\n", + "oxy2 1 6784.7247 6784.72473 668.2398 1.357823e-30\n", + "Residuals 50 507.6565 10.15313 NA NA" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Df Sum Sq Mean Sq F value Pr(>F) PctExp\n", + "oxygen 1 75555.2158 75555.21580 7441.5695 4.587875e-56 91.1978362\n", + "oxy2 1 6784.7247 6784.72473 668.2398 1.357823e-30 8.1894044\n", + "Residuals 50 507.6565 10.15313 NA NA 0.6127594\n" + ] + } + ], + "source": [ + "fit <- lm(ventil ~ oxygen + oxy2, data = anaerobic)\n", + "summary(fit)\n", + "anova(fit)\n", + "af <- anova(fit)\n", + "afss <- af$\"Sum Sq\"\n", + "print(cbind(af,PctExp=afss/sum(afss)*100))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Make the usual residual plots and comment on the fit of the model again." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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jgQiK7D77//TlsbkPLy8lOnTk2bNq3DdFi2bBn9eNeuXRUVFbdu3erXrx89mJyc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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "layout(matrix(1:4,2,2)) \n", + "autoplot(fit)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "3.4.2" + }, + "toc": { + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "342px" + }, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/unit10.ipynb b/unit10.ipynb new file mode 100644 index 0000000..92dea90 --- /dev/null +++ b/unit10.ipynb @@ -0,0 +1,39 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Unit 10\n", + "\n", + "Sample text for unit 10 goes here\n", + "\n", + "Back to [section 5.1](section5.1.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "3.4.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/unit3.ipynb b/unit3.ipynb new file mode 100644 index 0000000..e48e66e --- /dev/null +++ b/unit3.ipynb @@ -0,0 +1,39 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Unit 3\n", + "\n", + "Sample text for unit 3 goes here\n", + "\n", + "Back to [section 5.1](section5.1.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "3.4.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} -- 2.34.1