Using R for Introductory Statistics : John Verzani

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Index 395pch=, 86, 378type=, 60, 86xlab=, 43usageboxplots, 127density plots, 60model <strong>for</strong>mula, 95scatterplot, 82polygon(), 380prop, test(), 187, 220alt=, 220p=, 220qqline(), 80qqnorm(), 80qqplot(), 80quantile(), 50range(), 48rbind(), 70, 261read.csv(), 114read.table(), 28, 114recordPlot(), 78replayPlot(), 78rep(), 14, 318, 319, 349rm(), 21rownames(), 71rug(), 60, 155sample(), 143, 147, 182, 250sapply(), 124, 177scale(), 52, 125, 209scan(), 27scatter. smooth(), 101sd(), 49seq(), 14setClass(), 401setwd(), 28Shapiro.test(), 272show(), 401smooth.spline(), 101source(), 27, 173, 396split(), 129stack (), 129, 317, 318stem(), 42stepAIC(), 309, 348stripchart(), 42subset (), 119, 304select=, 119subset=, 119, 348summary(), 52, 293, 347
Index 396supsmu(), 101switch(), 384t.test(), 193, 223paired=TRUE, 242table(), 32text(), 65title(), 59t(), 75unlist(), 140unstack(), 129update(), 304url(), 29var.test(), 248var(), 49which(), 18while(), 174, 395wilcox.test(), 208with(), 25xtabs(), 127, 261, 340xyplot(), 133command line+, 7;, 6density(), 60help(), 11adjusted R 2 , 292alternative hypothesis, 214ANOVA, 313assignment, 7Bernoulli random variable, 150binary variable, 343binomial random variable, 150bootstrap sample, 176boxplot, 64built-in data sets, 24c.d.f., 146central limit theorem, 161class, 398class attribute, 136, 388, 397coefficient of determination, 292coefficient of variation, 55
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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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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
Page 356 and 357: Appendix A 345A.1.4 Installing from
Page 359 and 360: Appendix BGraphical user interfaces
Page 361 and 362: Appendix B 350Figure B.2 Multi-docu
Page 363 and 364: Appendix B 352If you forget to inst
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
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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 408 and 409: Index 397command line, 411>, 5confi
Page 410 and 411: Index 399extra sum of squares, 307f
Page 412 and 413: Index 401robust statistic, 193sampl
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