Using R for Introductory Statistics : John Verzani

More documents

Recommendations

Info

Confidence intervals 205−18 282sample estimates:difference in location146.5The 90% confidence interval, [−18,282], contains a value of ().This example would be improved if we had matched or paired data—that is, thesalaries for the same set of CEOs in the year 2000 and 2002—as thenFigure 7.8 Densities of 2000 and2002 CEO compensations indicatingsimilarly shaped distributions withpossible shiftdifferences in the sampling would be minimized. If that case is appropriate, then addingthe argument paired=TRUE to wilcox. test() computes a confidence interval based on thesigned-rank statistic.7.6.4 Problems7.29 The commuter revisited: the commuter records 20 commute times to gauge hermedian travel time. The data has a sample median of 24 and is summarized in this stemand-leafdiagram:> stem(commutes)2 | 11122334444442 | 55693 | 113
Using R for introductory statistics 206If the data is appropriate for t.test(), use that to give a 90% confidence interval for themean. Otherwise use wilcox.test() (perhaps after a transform) to give a confidenceinterval for the median.7.30 The data set cabinet (UsingR) contains information on the amount of money eachmember of President Bush’s cabinet saved due to the passing of a 2003 tax bill. This isstored in the variable named est .tax. savings. Find a confidence interval for the medianamount.7.31 The data set u2 (UsingR) contains song lengths for several albums by the bandU2. How might you interpret the lengths as a sample from a larger population? Usewilcox.test() to construct a 95% confidence interval for the difference of populationmeans between the album October and the album The Joshua Tree.7.32 The data set cfb (UsingR) contains a sampling of the data contained in the Surveyof Consumer Finances. For the variables AGE and INCOME find 95% confidenceintervals for the median.7.33 We can simulate the signed-rank distribution and see that it applies for anysymmetric distribution regardless of tail length. The following will simulate thedistribution for n=20 using normal data.> n = 20;m=250 # 250 samples> res = c() # the results> for(i in 1:m) {+ x = rnorm(n)+ res[i]=sum(rank(abs(x))[x>0]) # only add positivevalues+ }This can be plotted with> hist(res,prob=TRUE)> x = 40:140> lines(x,dsignrank(x,n)) # density-like, butdiscrete.If you change the line x=rnorm (x) to x=rt (n, df=2), the underlying distribution will belong tailed, and short tailed if you change it to x=runif (n, −1,1) Do both, and thencompare all three samples. Are they different or the same? What happens if you useskewed data, such as x=rexp (n)−1?
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 10 and 11:
Web accompanimentsThe home page for
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
Page 50 and 51:
Univariate data 392.5 Web developer
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
Page 58 and 59:
Univariate data 47numbers first and
Page 60 and 61:
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
Page 76 and 77:
Univariate data 652.3.4 Problems2.3
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 112 and 113:
Bivariate data 101produce a scatter
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
Page 160 and 161:
Describing populations 149variance
Page 162 and 163:
Describing populations 151> res = r
Page 164 and 165:
Describing populations 153> hist(re
Page 166 and 167: Describing populations 155more woul
Page 168 and 169: Describing populations 157Figure 5.
Page 170 and 171: Describing populations 159use the n
Page 172 and 173: Chapter 6SimulationOne informal des
Page 174 and 175: Simulation 163Figure 6.2 Quantile-n
Page 176 and 177: Simulation 1656.4 Defining a functi
Page 178 and 179: Simulation 1676.4.3 The function bo
Page 180 and 181: Simulation 169> summary(res.25)Min.
Page 182 and 183: Simulation 1716.5.2 The geometric d
Page 184 and 185: Simulation 173> xbarstar = c()> for
Page 186 and 187: Simulation 1756.3 For what value of
Page 188 and 189: Simulation 177This function will do
Page 190 and 191: Confidence intervals 179Figure 7.1
Page 192 and 193: Confidence intervals 181which, when
Page 194 and 195: Confidence intervals 183In R this b
Page 196 and 197: Confidence intervals 185alternative
Page 198 and 199: Confidence intervals 1877.3 Confide
Page 200 and 201: Confidence intervals 189> zstar = q
Page 202 and 203: Confidence intervals 191correlates
Page 204 and 205: Confidence intervals 193In general,
Page 206 and 207: Confidence intervals 195contains th
Page 208 and 209: Confidence intervals 197distributed
Page 210 and 211: Confidence intervals 199mean of x m
Page 212 and 213: Confidence intervals 201in Table 7.
Page 214 and 215: Confidence intervals 203data is mor
Page 218 and 219: Chapter 8Significance testsFinding
Page 220 and 221: Significance tests 209against the a
Page 222 and 223: Significance tests 2111. Identify H
Page 224 and 225: Significance tests 213simple random
Page 226 and 227: Significance tests 2158.5 On a numb
Page 228 and 229: Significance tests 217> mpg =c(11.4
Page 230 and 231: Significance tests 2198.16 We can p
Page 232 and 233: Significance tests 221Sign test for
Page 234 and 235: Significance tests 223parameters. A
Page 236 and 237: Significance tests 225A natural tes
Page 238 and 239: Significance tests 227audience at t
Page 240 and 241: Significance tests 229If the two va
Page 242 and 243: Significance tests 231Figure 8.5 De
Page 244 and 245: Significance tests 233H 0 :µ x =µ
Page 246 and 247: Significance tests 235Figure 8.6 Tw
Page 248 and 249: Significance tests 237group n sechi
Page 250 and 251: Chapter 9Goodness of fitIn this cha
Page 252 and 253: Goodness of fit 241This gives the t
Page 254 and 255: Goodness of fit 243The function ret
Page 256 and 257: Goodness of fit 2459.3 A package of
Page 258 and 259: Goodness of fit 247This is the squa
Page 260 and 261: Goodness of fit 249We now have all
Page 262 and 263: Goodness of fit 251whether any diff
Page 264 and 265: Goodness of fit 253Table 9.10 Accid
Page 266 and 267:
Goodness of fit 255theoretical dens
Page 268 and 269:
Goodness of fit 257D = 0.0745, p-va
Page 270 and 271:
Goodness of fit 259A consequence is
Page 272 and 273:
Goodness of fit 261( 78.69) ( 55.65
Page 274 and 275:
Goodness of fit 2639.20 The rivers
Page 276 and 277:
Linear regression 265response ~ pre
Page 278 and 279:
Linear regression 26710.1.4 Using l
Page 280 and 281:
Linear regression 269Extractor func
Page 282 and 283:
Linear regression 271should be appr
Page 284 and 285:
Linear regression 273The scale-loca
Page 286 and 287:
Linear regression 275Figure 10.5 Fo
Page 288 and 289:
Linear regression 277the simulation
Page 290 and 291:
Linear regression 279Confidence int
Page 292 and 293:
Linear regression 281Signif. codes:
Page 294 and 295:
Linear regression 283Figure 10.7 Re
Page 296 and 297:
Linear regression 285home in 1970.
Page 298 and 299:
Linear regression 287Let Y be a res
Page 300 and 301:
Linear regression 289Coefficients:(
Page 302 and 303:
Linear regression 291+ tot=a+ for(i
Page 304 and 305:
Linear regression 293Y i =β 0 +β
Page 306 and 307:
Linear regression 295Call:lm(formul
Page 308 and 309:
Linear regression 297Let y t be the
Page 310 and 311:
Analysis of variance 299Figure 11.1
Page 312 and 313:
Analysis of variance 301> SSE=(5-1)
Page 314 and 315:
Analysis of variance 303163.2 173.3
Page 316 and 317:
Analysis of variance 305can be perf
Page 318 and 319:
Analysis of variance 307Repeat with
Page 320 and 321:
Analysis of variance 309> dvalues i
Page 322 and 323:
Page 324 and 325:
Analysis of variance 313> ewr.out=s
Page 326 and 327:
Analysis of variance 31511.11 The T
Page 328 and 329:
Page 330 and 331:
Analysis of variance 31911.24 The d
Page 332 and 333:
Analysis of variance 321Signif. cod
Page 334 and 335:
Analysis of variance 323factors, al
Page 336 and 337:
Analysis of variance 325(Intercept)
Page 338 and 339:
Chapter 12Two extensions of the lin
Page 340 and 341:
Two extensions of the linear model
Page 342 and 343:
Page 344 and 345:
Page 346 and 347:
Page 348 and 349:
Page 350 and 351:
Page 352 and 353:
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
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
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
Page 412 and 413:
Index 401robust statistic, 193sampl
show all

Using R for Introductory Statistics : John Verzani

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?