Using R for Introductory Statistics : John Verzani

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Bivariate data 101produce a scatterplot of the variable time modeled by temp. The pch=cens argumentmarks points that were censored with a square; otherwise a circle is used. Make thescatterplot and answer the following:1. How many different temperatures were used in the experiment?2. Does the data look to be a candidate for a linear model? (You might want toconsider why the data point (150,8000) is marked with a square.)3. Fit a linear model. What are the coefficients?4. Use the linear model to make a prediction for the accelerated lifetime of a motor runat a temperature of 210°C.3.33 The data set mw.ages (UsingR) contains census 2000 data on the age distributionof residents of Maplewood, New Jersey. The data is broken down by male and female.Attach the data set and make a plot of the Male and Female variables added together.Connect the dots using the argument type="l". For example, with the commandplot(1:103,Male + Female,type="l").Next, layer on top two trend lines, one for male and one for female, using thesupsmu() function. What age group is missing from this town?
Chapter 4Multivariate DataMultivariate data can be summarized and viewed in ways that are similar to thosediscussed for bivariate and univariate data, although differences exist, as there are manymore possible relationships. These differences can be handled by looking at all thevariables simultaneously, or by holding some variables constant while we look at others.The tools used are similar to those for bivariate data, though if we enhance our datamanipulationskills the work will be easier. This chapter includes more details on R’sdata frames, lists, and model formula notation. Also included is an introduction to R’slattice graphics package, which greatly enhances certain explorations of multivariate data.4.1 Viewing multivariate dataIn this chapter we look at three typical examples of multivariate data. The firstsummarizes survey results (categorical data), the second compares independent samples;and the third searches for relationships among many different variables.4.1.1 Summarizing categorical dataJust as we used tables to summarize bivariate data, we use them for multivariate data aswell. However, as a table shows only two relationships at once, we will need to useseveral when looking at three or more relationships.■ Example 4.1: Student expenses The student. expenses (UsingR) data set containsthe results of a simple survey. Students were asked which of five different expenses theyincur. The data is in Table 4.1.Even a small table like this contains too much information for us to identifyTable 4.1 Student expenses surveyStudent cell.phone cable, tv dial.up cable.modem car1 Y Y Y N Y2 Y N N N N3 N N N N Y4 Y Y N Y Y5 N N N N N6 Y N Y N Y7 Y N N Y N8 N N N N Y9 Y Y N N Y
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Using R for Introductory Statistics
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This edition published in the Taylo
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PrefaceWhat is R?R is a computer la
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Web accompanimentsThe home page for
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Chapter 1Data1.1 What is data?When
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Data 3Journal of Economics that leg
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Data 5When R starts, it searches fo
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Data 71.2.3 AssignmentIt is often c
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Data 9Giving data vectors named ent
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Data 11> x = c(2,3,5,7,11)> xbar =
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Data 13Simple sequences A sequence
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Data 153. Find the differences of t
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Data 17> ebay[−1] # all but the f
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Data 19empty vector if i=0x[c (2, 3
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Data 21Many R functions have an arg
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Data 231.4 Reading in other sources
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Data 25A convenient method, which r
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Data 27Using source () to read in R
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Data 291.4.4 Problems1.20 The built
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Chapter 2Univariate dataIn statisti
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Univariate data 33The table() funct
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Univariate data 35There are names o
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Univariate data 37Why are pie chart
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Univariate data 392.5 Web developer
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Univariate data 4121:Read 20 items>
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Univariate data 43compare different
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Univariate data 45Figure 2.8 The me
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Univariate data 47numbers first and
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Univariate data 49> var(test.scores
Page 62 and 63: Univariate data 51As with the quant
Page 64 and 65: Univariate data 532.13 Can you copy
Page 66 and 67: Univariate data 55Comment on any pa
Page 68 and 69: Univariate data 57> hist(waiting) #
Page 70 and 71: Univariate data 59Figure 2.13 Frequ
Page 72 and 73: Univariate data 61Figure 2.15 Galax
Page 74 and 75: Univariate data 63Figure 2.17 Amoun
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Page 78 and 79: Chapter 3Bivariate dataThis chapter
Page 80 and 81: Bivariate data 69> colnames(x) = c(
Page 82 and 83: Bivariate data 71unbuckled 56 8 64b
Page 84 and 85: Bivariate data 73Figure 3.1 Segment
Page 86 and 87: Bivariate data 75interested in comp
Page 88 and 89: Bivariate data 77> stripchart(list(
Page 90 and 91: Bivariate data 79Figure 3.5 Six qqn
Page 92 and 93: Bivariate data 81Figure 3.6 Assesse
Page 94 and 95: Bivariate data 83> plot(height, wei
Page 96 and 97: Bivariate data 85correlation only i
Page 98 and 99: Bivariate data 873.17 The data set
Page 100 and 101: Bivariate data 89Figure 3.10 Predic
Page 102 and 103: Bivariate data 91That is, the y-val
Page 104 and 105: Bivariate data 93■ Example 3.6: K
Page 106 and 107: Bivariate data 95[1] 13 50> florida
Page 108 and 109: Bivariate data 97Just like the mean
Page 110 and 111: Bivariate data 99Figure 3.16 Temper
Page 114 and 115: Multivariate data 10310 Y N Y N Nan
Page 116 and 117: Multivariate data 105Figure 4.1 Tax
Page 118 and 119: Multivariate data 107> plot(gestati
Page 120 and 121: Multivariate data 109Make the above
Page 122 and 123: Multivariate data 111One difference
Page 124 and 125: Multivariate data 113Accessing a da
Page 126 and 127: Multivariate data 115Figure 4.4 Sca
Page 128 and 129: Multivariate data 117To illustrate,
Page 130 and 131: Multivariate data 119mtcars[[’mpg
Page 132 and 133: Multivariate data 121We can apply f
Page 134 and 135: Multivariate data 123appropriate. T
Page 136 and 137: Multivariate data 125pickup 70 71 5
Page 138 and 139: Multivariate data 1272 42 stomach
Page 140 and 141: Multivariate data 129make several p
Page 142 and 143: Multivariate data 131+ subset=(wt !
Page 144 and 145: Multivariate data 133When a factor
Page 146 and 147: Multivariate data 135Coercion is th
Page 148 and 149: Multivariate data 137[1,] 1 3 5 7[2
Page 150 and 151: Describing populations 1395.1.1 Dis
Page 152 and 153: Describing populations 141This is a
Page 154 and 155: Describing populations 1435.1.3 Sam
Page 156 and 157: Describing populations 1455.7 Toss
Page 158 and 159: Describing populations 147In R the
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Describing populations 151> res = r
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Describing populations 153> hist(re
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Describing populations 155more woul
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Describing populations 157Figure 5.
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Describing populations 159use the n
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Chapter 6SimulationOne informal des
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Simulation 163Figure 6.2 Quantile-n
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Simulation 1656.4 Defining a functi
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Simulation 1676.4.3 The function bo
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Simulation 169> summary(res.25)Min.
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Simulation 1716.5.2 The geometric d
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Simulation 173> xbarstar = c()> for
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Simulation 1756.3 For what value of
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Simulation 177This function will do
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Confidence intervals 179Figure 7.1
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Confidence intervals 181which, when
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Confidence intervals 183In R this b
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Confidence intervals 185alternative
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Confidence intervals 1877.3 Confide
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Confidence intervals 189> zstar = q
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Confidence intervals 191correlates
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Confidence intervals 193In general,
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Confidence intervals 195contains th
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Confidence intervals 197distributed
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Confidence intervals 199mean of x m
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Confidence intervals 201in Table 7.
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Confidence intervals 203data is mor
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Confidence intervals 205−18 282sa
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Chapter 8Significance testsFinding
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Significance tests 209against the a
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Significance tests 2111. Identify H
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Significance tests 213simple random
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Significance tests 2158.5 On a numb
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Significance tests 217> mpg =c(11.4
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Significance tests 2198.16 We can p
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Significance tests 221Sign test for
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Significance tests 223parameters. A
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Significance tests 225A natural tes
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Significance tests 227audience at t
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Significance tests 229If the two va
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Significance tests 231Figure 8.5 De
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Significance tests 233H 0 :µ x =µ
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Significance tests 235Figure 8.6 Tw
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Significance tests 237group n sechi
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Chapter 9Goodness of fitIn this cha
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Goodness of fit 241This gives the t
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Goodness of fit 243The function ret
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Goodness of fit 2459.3 A package of
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Goodness of fit 247This is the squa
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Goodness of fit 249We now have all
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Goodness of fit 251whether any diff
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Goodness of fit 253Table 9.10 Accid
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Goodness of fit 255theoretical dens
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Goodness of fit 257D = 0.0745, p-va
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Goodness of fit 259A consequence is
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Goodness of fit 261( 78.69) ( 55.65
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Goodness of fit 2639.20 The rivers
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Linear regression 265response ~ pre
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Linear regression 26710.1.4 Using l
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Linear regression 269Extractor func
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Linear regression 271should be appr
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Linear regression 273The scale-loca
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Linear regression 275Figure 10.5 Fo
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Linear regression 277the simulation
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Linear regression 279Confidence int
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Linear regression 281Signif. codes:
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Linear regression 283Figure 10.7 Re
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Linear regression 285home in 1970.
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Linear regression 287Let Y be a res
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Linear regression 289Coefficients:(
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Linear regression 291+ tot=a+ for(i
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Linear regression 293Y i =β 0 +β
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Linear regression 295Call:lm(formul
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Linear regression 297Let y t be the
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Analysis of variance 299Figure 11.1
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Analysis of variance 301> SSE=(5-1)
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Analysis of variance 303163.2 173.3
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Analysis of variance 305can be perf
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Analysis of variance 307Repeat with
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Analysis of variance 309> dvalues i
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Analysis of variance 313> ewr.out=s
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Analysis of variance 31511.11 The T
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Analysis of variance 31911.24 The d
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Analysis of variance 321Signif. cod
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Analysis of variance 323factors, al
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Analysis of variance 325(Intercept)
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Chapter 12Two extensions of the lin
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Two extensions of the linear model
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Appendix AGetting, installing, and
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Appendix A 345A.1.4 Installing from
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Appendix BGraphical user interfaces
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Appendix B 350Figure B.2 Multi-docu
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Appendix B 352If you forget to inst
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Appendix CTeaching with RUsing R in
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Appendix DMore on graphics with RTh
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Appendix D 358A device is set up wi
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Appendix D 360Adding a box around t
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Appendix D 362> x = seq(−2, 2, le
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Appendix D 364D.2 Creating new grap
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Appendix D 366Figure D.3 Per-capita
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Appendix EProgramming in ROne of R
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Appendix E 371arg1, arg2, arg3When
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Appendix E 373freedman-diaconis, sc
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Appendix E 375return(summary(x))sum
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Appendix E 377In this example varna
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Appendix E 379editor and ESS extend
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Appendix E 381[1] 26> size(data.fra
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Appendix E 383"[.String" = function
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Appendix E 385})We need to use the
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Appendix E 387For our String class
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Appendix E 389Now, instances of the
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Appendix E 391old.x=xx=x—(x^2—s
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Index 393heat.colors(), 378rainbow(
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Index 395pch=, 86, 378type=, 60, 86
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Index 397command line, 411>, 5confi
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Index 399extra sum of squares, 307f
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Index 401robust statistic, 193sampl
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Using R for Introductory Statistics : John Verzani

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