Using R for Introductory Statistics : John Verzani

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Appendix B 351Figure B.3 The R console in theCocoa GUI for Mac OS XFigure B.4 Package manager forCocoa GUI in Mac OS XB.3 RcdmrThe tcltk package can be loaded into R, which allows R to use the GUI elementsprovided by the tcltk widget collection. This set of widgets is available for the platformsR supports.The Rcmdr package by John Fox uses these bindings to provide an easy to learn anduseful interface to many common functions used in data analysis. It is designed for newand casual users of R, like the target audience of this book.Rcmdr is installed easily, as it resides on CRAN, though it requires many otherpackages for functionality such as that provided by the car package. Installing thesepackages can be done automatically with the command:> install.packages("Rcmdr", dependencies=TRUE)
Appendix B 352If you forget to install all the dependencies, the first thing Rcmdr will do when run is askif you want them to be installed.Rcmdr is started by loading the package with the command> library(Rcmdr)Figure B.5 Workspace browser forCocoa GUI in Mac OS XThis opens the main window, as in Figure B.6, with a menu bar and a log window. If youare using the Windows GUI, Rcmdr works better in the single-document interface (SDI).The default setting for the GUI is to use the multiple-document interface (MDI). To makethe change is done by setting the option for SDI, not MDI, using the File: : Options menuitem.Once running, the R session may be interacted with by means of the menu bar and thesubsequent selection of commands.Figure B.6 Main Rcmdr windowIn particular, first the user defines the active data set. This is done either with the Data::Data in Packages…menu item or the Data: : Active Data Set…one. Once a data set isselected, Rcmdr ensures that the variable names become available to the other functions.For example, creating a histogram of a univariate variable in the active data set is donefrom the Graphs: : Histogram…menu (Figure B.7).
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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
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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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Page 328 and 329: Analysis of variance 317Figure 11.6
Page 330 and 331: Analysis of variance 31911.24 The d
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Page 338 and 339: Chapter 12Two extensions of the lin
Page 340 and 341: Two extensions of the linear model
Page 354 and 355: Appendix AGetting, installing, and
Page 356 and 357: Appendix A 345A.1.4 Installing from
Page 359 and 360: Appendix BGraphical user interfaces
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Page 365 and 366: Appendix CTeaching with RUsing R in
Page 367 and 368: Appendix DMore on graphics with RTh
Page 369 and 370: Appendix D 358A device is set up wi
Page 371 and 372: Appendix D 360Adding a box around t
Page 373 and 374: Appendix D 362> x = seq(−2, 2, le
Page 375 and 376: Appendix D 364D.2 Creating new grap
Page 377 and 378: Appendix D 366Figure D.3 Per-capita
Page 380 and 381: Appendix EProgramming in ROne of R
Page 382 and 383: Appendix E 371arg1, arg2, arg3When
Page 384 and 385: Appendix E 373freedman-diaconis, sc
Page 386 and 387: Appendix E 375return(summary(x))sum
Page 388 and 389: Appendix E 377In this example varna
Page 390 and 391: Appendix E 379editor and ESS extend
Page 392 and 393: Appendix E 381[1] 26> size(data.fra
Page 394 and 395: Appendix E 383"[.String" = function
Page 396 and 397: Appendix E 385})We need to use the
Page 398 and 399: Appendix E 387For our String class
Page 400 and 401: Appendix E 389Now, instances of the
Page 402 and 403: Appendix E 391old.x=xx=x—(x^2—s
Page 404 and 405: Index 393heat.colors(), 378rainbow(
Page 406 and 407: Index 395pch=, 86, 378type=, 60, 86
Page 408 and 409: Index 397command line, 411>, 5confi
Page 410 and 411: Index 399extra sum of squares, 307f
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Index 401robust statistic, 193sampl
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