Preface to First Edition - lib

More documents

Recommendations

Info

A Handbook ofStatisticalAnalysesUsingSECONDEDITIONBrian S. Everitt and Ibrsten HothornCRC PressTaylor & Francis CroupBoca Ra<strong>to</strong>n London New YorkCRC Press is an imprint of theTaylor & Francis Croup, an informa businessA CHAPMAN & HALL BOOK© 2010 by Taylor and Francis Group, LLC
Page 4 and 5: Chapman & Hall/CRCTaylor & Francis
Page 6 and 7: Preface to Second EditionLike the f
Page 8 and 9: document including both the R sourc
Page 10 and 11: 3.6 R output of the paired t-test f
Page 12 and 13: 9.1 Initial tree for the body fat d
Page 14 and 15: 14.4 Regression relationship betwee
Page 16 and 17: List of Tables2.1 USmelanoma data.
Page 18 and 19: 9.1 bodyfat data (package mboost).
Page 20 and 21: Contents1 An Introduction to R 11.1
Page 22 and 23: 13 Analysing Longitudinal Data II 2
Page 24 and 25: © 2010 by Taylor and Francis Group
Page 26 and 27: 4 AN INTRODUCTION TO Rhttp://CRAN.R
Page 28 and 29: 6 AN INTRODUCTION TO RThe output of
Page 30 and 31: 8 AN INTRODUCTION TO RA vector is t
Page 32 and 33: 10 AN INTRODUCTION TO RR> csvForbes
Page 34 and 35: 12 AN INTRODUCTION TO R[1] 1 2 3R>
Page 36 and 37: 14 AN INTRODUCTION TO RFALSE TRUE5
Page 38 and 39: 16 AN INTRODUCTION TO Rto light whe
Page 40 and 41: 18 AN INTRODUCTION TO RR> bcat iqr
Page 42 and 43: 20 AN INTRODUCTION TO RR> plot(log(
Page 44 and 45: 22 AN INTRODUCTION TO RR> tmp tmp$
Page 46 and 47: CHAPTER 2Data Analysis Using Graphi
Page 48 and 49: INITIAL DATA ANALYSIS 27North Carol
Page 50 and 51: ANALYSIS USING R 29for a more compl
Page 52 and 53:
ANALYSIS USING R 31R> plot(mortalit
Page 54 and 55:
ANALYSIS USING R 33R> layout(matrix
Page 56 and 57:
ANALYSIS USING R 35R> barplot(xtabs
Page 58 and 59:
ANALYSIS USING R 37right rectangle
Page 60 and 61:
SUMMARY 39R> xyplot(jitter(log(A_in
Page 62 and 63:
SUMMARY 41Ex. 2.2 Mortality rates p
Page 64 and 65:
SUMMARY 43Ex. 2.4 Flury and Riedwyl
Page 66 and 67:
46 SIMPLE INFERENCETable 3.1:roomwi
Page 68 and 69:
48 SIMPLE INFERENCETable 3.3:water
Page 70 and 71:
50 SIMPLE INFERENCEthe means of two
Page 72 and 73:
52 SIMPLE INFERENCEsmall sample siz
Page 74 and 75:
54 SIMPLE INFERENCER> tapply(roomwi
Page 76 and 77:
56 SIMPLE INFERENCER> t.test(I(widt
Page 78 and 79:
58 SIMPLE INFERENCER> mooringdiff
Page 80 and 81:
60 SIMPLE INFERENCE1 R> nf psymb
Page 82 and 83:
62 SIMPLE INFERENCER> library("vcd"
Page 84 and 85:
64 SIMPLE INFERENCEresiduals and an
Page 86 and 87:
66 CONDITIONAL INFERENCETable 4.1:s
Page 88 and 89:
68 CONDITIONAL INFERENCETable 4.7:a
Page 90 and 91:
70 CONDITIONAL INFERENCEtest statis
Page 92 and 93:
72 CONDITIONAL INFERENCEThe distrib
Page 94 and 95:
74 CONDITIONAL INFERENCE4.3.3 Gastr
Page 96 and 97:
76 CONDITIONAL INFERENCER> cmh_test
Page 98 and 99:
78 CONDITIONAL INFERENCETable 4.8:o
Page 100 and 101:
80 ANALYSIS OF VARIANCEThe data in
Page 102 and 103:
82 ANALYSIS OF VARIANCE5.2 Analysis
Page 104 and 105:
84 ANALYSIS OF VARIANCER> plot.desi
Page 106 and 107:
86 ANALYSIS OF VARIANCER> interacti
Page 108 and 109:
88 ANALYSIS OF VARIANCEA and B ther
Page 110 and 111:
90 ANALYSIS OF VARIANCER> plot(fost
Page 112 and 113:
92 ANALYSIS OF VARIANCER> pairs(mea
Page 114 and 115:
94 ANALYSIS OF VARIANCER> summary(m
Page 116 and 117:
96 ANALYSIS OF VARIANCETable 5.5:st
Page 118 and 119:
98 SIMPLE AND MULTIPLE LINEAR REGRE
Page 120 and 121:
100 SIMPLE AND MULTIPLE LINEAR REGR
Page 122 and 123:
Page 124 and 125:
Page 126 and 127:
Page 128 and 129:
Page 130 and 131:
Page 132 and 133:
Page 134 and 135:
Page 136 and 137:
Page 138 and 139:
118 LOGISTIC REGRESSION AND GENERAL
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
Page 146 and 147:
Page 148 and 149:
Page 150 and 151:
Page 152 and 153:
Page 154 and 155:
Page 156 and 157:
Page 158 and 159:
Page 160 and 161:
140 DENSITY ESTIMATIONTable 8.1:fai
Page 162 and 163:
142 DENSITY ESTIMATIONestimation wo
Page 164 and 165:
144 DENSITY ESTIMATION1 R> rec tri
Page 166 and 167:
146 DENSITY ESTIMATIONR> epa x ep
Page 168 and 169:
148 DENSITY ESTIMATION1 R> data("fa
Page 170 and 171:
150 DENSITY ESTIMATIONR> persp(x =
Page 172 and 173:
152 DENSITY ESTIMATIONR> opar rx
Page 174 and 175:
154 DENSITY ESTIMATIONR> boot.ci(bo
Page 176 and 177:
156 DENSITY ESTIMATIONTable 8.3: ga
Page 178 and 179:
158 DENSITY ESTIMATIONTable 8.5:sch
Page 180 and 181:
CHAPTER 9Recursive Partitioning: Pr
Page 182 and 183:
INTRODUCTION 163Table 9.1:bodyfat d
Page 184 and 185:
ANALYSIS USING R 165large tree usin
Page 186 and 187:
ANALYSIS USING R 167R> plot(as.part
Page 188 and 189:
ANALYSIS USING R 169R> plot(as.part
Page 190 and 191:
ANALYSIS USING R 171R> avg predict
Page 192 and 193:
ANALYSIS USING R 173R> plot(bodyfat
Page 194 and 195:
SUMMARY 175Ex. 9.2 For each possibl
Page 196 and 197:
178 SMOOTHERS AND GENERALISED ADDIT
Page 198 and 199:
Page 200 and 201:
Page 202 and 203:
Page 204 and 205:
Page 206 and 207:
Page 208 and 209:
Page 210 and 211:
Page 212 and 213:
Page 214 and 215:
CHAPTER 11Survival Analysis:Glioma
Page 216 and 217:
Table 11.2:GBSG2 data (package ipre
Page 218 and 219:
SURVIVAL ANALYSIS 2011958). This in
Page 220 and 221:
SURVIVAL ANALYSIS 203can be estimat
Page 222 and 223:
ANALYSIS USING R 205R> data("glioma
Page 224 and 225:
ANALYSIS USING R 207R> data("GBSG2"
Page 226 and 227:
ANALYSIS USING R 209R> plot(GBSG2_z
Page 228 and 229:
SUMMARY 211R> plot(GBSG2_ctree)1pno
Page 230 and 231:
CHAPTER 12Analysing Longitudinal Da
Page 232 and 233:
INTRODUCTION 215Table 12.1:BtheB da
Page 234 and 235:
LINEAR MIXED EFFECTS MODELS 217term
Page 236 and 237:
ANALYSIS USING R 219which is no lon
Page 238 and 239:
ANALYSIS USING R 2211.5m No >6m TAU
Page 240 and 241:
PREDICTION OF RANDOM EFFECTS 223R>
Page 242 and 243:
THE PROBLEM OF DROPOUTS 225Complete
Page 244 and 245:
SUMMARY 227R> bdi plot(1:4, rep(-0
Page 246 and 247:
SUMMARY 229Table 12.2:phosphate dat
Page 248 and 249:
232 ANALYSING LONGITUDINAL DATA IIT
Page 250 and 251:
234 ANALYSING LONGITUDINAL DATA IIf
Page 252 and 253:
236 ANALYSING LONGITUDINAL DATA IIb
Page 254 and 255:
238 ANALYSING LONGITUDINAL DATA II1
Page 256 and 257:
240 ANALYSING LONGITUDINAL DATA IIR
Page 258 and 259:
Page 260 and 261:
Page 262 and 263:
Page 264 and 265:
Page 266 and 267:
250 ANALYSING LONGITUDINAL DATA II1
Page 268 and 269:
CHAPTER 14Simultaneous Inference an
Page 270 and 271:
INTRODUCTION 255Table 14.2 (see Hot
Page 272 and 273:
ANALYSIS USING R 257mate ˆθ, K is
Page 274 and 275:
ANALYSIS USING R 259The function gl
Page 276 and 277:
ANALYSIS USING R 261R> par(mai = pa
Page 278 and 279:
ANALYSIS USING R 263R> plot(ci, xla
Page 280 and 281:
SUMMARY 265R> layout(matrix(1:2, nc
Page 282 and 283:
268 META-ANALYSISTable 15.1:smoking
Page 284 and 285:
270 META-ANALYSISstudies, even when
Page 286 and 287:
272 META-ANALYSISgiven byVar(Ȳ ) =
Page 288 and 289:
274 META-ANALYSISR> summary(smoking
Page 290 and 291:
276 META-ANALYSISnames = rownames(s
Page 292 and 293:
278 META-ANALYSISR> summary(BCG_DSL
Page 294 and 295:
280 META-ANALYSISR> plot(y ~ Latitu
Page 296 and 297:
282 META-ANALYSISR> funnelplot(smok
Page 298 and 299:
284 META-ANALYSISEx. 15.2 The data
Page 300 and 301:
Table 16.1: heptathlon data. Result
Page 302 and 303:
288 PRINCIPAL COMPONENT ANALYSISTo
Page 304 and 305:
290 PRINCIPAL COMPONENT ANALYSISlon
Page 306 and 307:
292 PRINCIPAL COMPONENT ANALYSISPC7
Page 308 and 309:
294 PRINCIPAL COMPONENT ANALYSISR>
Page 310 and 311:
296 PRINCIPAL COMPONENT ANALYSISR>
Page 312 and 313:
CHAPTER 17Multidimensional Scaling:
Page 314 and 315:
Table 17.2:voting data. House of Re
Page 316 and 317:
MULTIDIMENSIONAL SCALING 303from X;
Page 318 and 319:
ANALYSIS USING R 305that they diffe
Page 320 and 321:
ANALYSIS USING R 307ning these poin
Page 322 and 323:
ANALYSIS USING R 309R> x y plot(x
Page 324 and 325:
SUMMARY 311ExercisesEx. 17.1 The da
Page 326 and 327:
Table 17.4:gardenflowers data. Diss
Page 328 and 329:
316 CLUSTER ANALYSISTable 18.1:pott
Page 330 and 331:
318 CLUSTER ANALYSISTable 18.2:plan
Page 332 and 333:
320 CLUSTER ANALYSISthe solution wi
Page 334 and 335:
© 2010 by Taylor and Francis Group
Page 336 and 337:
324 CLUSTER ANALYSISThe clustering
Page 338 and 339:
326 CLUSTER ANALYSISR> pottery_dist
Page 340 and 341:
328 CLUSTER ANALYSISR> data("planet
Page 342 and 343:
330 CLUSTER ANALYSISR> ccent(planet
Page 344 and 345:
332 CLUSTER ANALYSISR> clPairs(plan
Page 346 and 347:
334 CLUSTER ANALYSIS18.4 SummaryClu
Page 348 and 349:
336 BIBLIOGRAPHYBönsch, D., Ledere
Page 350 and 351:
338 BIBLIOGRAPHYDiggle, P. J., Heag
Page 352 and 353:
340 BIBLIOGRAPHYGarcia, A. L., Wagn
Page 354 and 355:
342 BIBLIOGRAPHYKeele, L. J. (2008)
Page 356 and 357:
344 BIBLIOGRAPHYMurray, G. D. and F
Page 358 and 359:
346 BIBLIOGRAPHYSchmid, C. F. (1954
Page 360:
348 BIBLIOGRAPHYWeisberg, S. (2008)
show all

Preface to First Edition - lib

Create successful ePaper yourself

Delete template?

Save as template?