Using R for Introductory Statistics : John Verzani

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Web accompanimentsThe home page for this book ishttp://www.math.csi.cuny.edu/UsingROn this page you will find solutions to selected homework problems (a full solutionsmanual for instructors is available from the publisher), a list of errata, and anaccompanying package containing data sets and a few functions used in the text. TheUsingR package contains data sets collected from various places. Consult the help pageof a data set for proper attribution. The package needs to be installed on your computerprior to usage. If your computer has an internet connection, the command> install.packages("UsingR")will fetch the package from CRAN, R’s warehouse of add-on packages, and install it. Thecommand library (UsingR) will load the package for use.If for some reason this fails, the package can be retrieved from this book’s home pagewith the commands> where="http://www.math.csi.cuny.edu/UsingR"> install.packages("UsingR",contriburl=where)Finally, if that fails, the package can be downloaded from the home page and installedmanually as described in Chapter 1.Using RThe R software is obtained from the Comprehensive R Archive Network (CRAN), whichmay be reached from the main R web site http://www.r-project/. org. Some basic detailsfor installation appear in Appendix A and more detail is on the CRAN website. This bookwas written to reflect the changes introduced by version 2.0.0 of R. R has approximatelytwo new major releases per year (the second number in the version number). Despite theaddition of numerous improvements with each new version, the maintainers of R do avery careful job with the upgrades to R. Minor bug fixes appear in maintenance versions(the third number). It is recommended that you upgrade your installation to keep pacewith these changes to R, although these new releases may affect some of the details givenin this text.AcknowledgmentsThe author has many people to thank for this project. First, the numerous contributors tothe R software and especially the core members for their seemingly tireless efforts inproducing this excellent software. Next, the editorial staff at Chapman Hall/CRC was
great. In particular I can’t thank an anonymous reviewer enough for his thorough andthoughtful comments and recommendations. As well, thanks go to several people whohave contacted me regarding the “simpleR” notes. Finally, the author wishes to thank hiseagle-eyed wife and the rest of his family, for their constant love, help, and supportduring this project.
Page 2: Using R for Introductory Statistics
Page 5 and 6: This edition published in the Taylo
Page 8 and 9: PrefaceWhat is R?R is a computer la
Page 12 and 13: Chapter 1Data1.1 What is data?When
Page 14 and 15: Data 3Journal of Economics that leg
Page 16 and 17: Data 5When R starts, it searches fo
Page 18 and 19: Data 71.2.3 AssignmentIt is often c
Page 20 and 21: Data 9Giving data vectors named ent
Page 22 and 23: Data 11> x = c(2,3,5,7,11)> xbar =
Page 24 and 25: Data 13Simple sequences A sequence
Page 26 and 27: Data 153. Find the differences of t
Page 28 and 29: Data 17> ebay[−1] # all but the f
Page 30 and 31: Data 19empty vector if i=0x[c (2, 3
Page 32 and 33: Data 21Many R functions have an arg
Page 34 and 35: Data 231.4 Reading in other sources
Page 36 and 37: Data 25A convenient method, which r
Page 38 and 39: Data 27Using source () to read in R
Page 40 and 41: Data 291.4.4 Problems1.20 The built
Page 42 and 43: Chapter 2Univariate dataIn statisti
Page 44 and 45: Univariate data 33The table() funct
Page 46 and 47: Univariate data 35There are names o
Page 48 and 49: Univariate data 37Why are pie chart
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Page 52 and 53: Univariate data 4121:Read 20 items>
Page 54 and 55: Univariate data 43compare different
Page 56 and 57: Univariate data 45Figure 2.8 The me
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Univariate data 49> var(test.scores
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Univariate data 51As with the quant
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Univariate data 532.13 Can you copy
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Univariate data 55Comment on any pa
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Univariate data 57> hist(waiting) #
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Univariate data 59Figure 2.13 Frequ
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Univariate data 61Figure 2.15 Galax
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Univariate data 63Figure 2.17 Amoun
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Univariate data 652.3.4 Problems2.3
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Chapter 3Bivariate dataThis chapter
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Bivariate data 69> colnames(x) = c(
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Bivariate data 71unbuckled 56 8 64b
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Bivariate data 73Figure 3.1 Segment
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Bivariate data 75interested in comp
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Bivariate data 77> stripchart(list(
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Bivariate data 79Figure 3.5 Six qqn
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Bivariate data 81Figure 3.6 Assesse
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Bivariate data 83> plot(height, wei
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Bivariate data 85correlation only i
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Bivariate data 873.17 The data set
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Bivariate data 89Figure 3.10 Predic
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Bivariate data 91That is, the y-val
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Bivariate data 93■ Example 3.6: K
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Bivariate data 95[1] 13 50> florida
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Bivariate data 97Just like the mean
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Bivariate data 99Figure 3.16 Temper
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Bivariate data 101produce a scatter
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Multivariate data 10310 Y N Y N Nan
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Multivariate data 105Figure 4.1 Tax
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Multivariate data 107> plot(gestati
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Multivariate data 109Make the above
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Multivariate data 111One difference
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Multivariate data 113Accessing a da
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Multivariate data 115Figure 4.4 Sca
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Multivariate data 117To illustrate,
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Multivariate data 119mtcars[[’mpg
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Multivariate data 121We can apply f
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Multivariate data 123appropriate. T
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Multivariate data 125pickup 70 71 5
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Multivariate data 1272 42 stomach
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Multivariate data 129make several p
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Multivariate data 131+ subset=(wt !
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Multivariate data 133When a factor
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Multivariate data 135Coercion is th
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Multivariate data 137[1,] 1 3 5 7[2
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Describing populations 1395.1.1 Dis
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Describing populations 141This is a
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Describing populations 1435.1.3 Sam
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Describing populations 1455.7 Toss
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Describing populations 147In R the
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Describing populations 149variance
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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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