Introduction to Categorical Data Analysis

More documents

Recommendations

Info

6.2 CUMULATIVE LOGIT MODELS FOR ORDINAL RESPONSES 183 Table 6.8. Computer Output (SAS) for Cumulative Logit Model with Political Ideology <strong>Data</strong> <strong>Analysis</strong> of Maximum Likelihood Estimates Standard Wald Parameter DF Estimate Error Chi-Square Pr > ChiSq Intercept 1 1 −2.4690 0.1318 350.8122
184 MULTICATEGORY LOGIT MODELS Likewise, substituting ˆα2, ˆα3, and ˆα4 for Democrats yields P(Y ˆ ≤ 2) = 0.38, P(Y ˆ ≤ 3) = 0.77, and P(Y ˆ ≤ 4) = 0.89. Category probabilities are differences of cumulative probabilities. For example, the estimated probability that a Democrat is moderate (category 3) is ˆπ3 = ˆ P(Y = 3) = ˆ P(Y ≤ 3) − ˆ P(Y ≤ 2) = 0.39 6.2.3 Inference about Model Parameters For testing independence (H0: β = 0), Table 6.8 reports that the likelihood-ratio statistic is 58.6 with df = 1. This gives extremely strong evidence of an association (P
Page 1 and 2:
www.dbeBooks.com - An Ebook Library
Page 4:
An Introduction to Categorical Data
Page 7 and 8:
Copyright © 2007 by John Wiley & S
Page 9 and 10:
vi CONTENTS 2.1.3 Sensitivity and S
Page 11 and 12:
viii CONTENTS 4.1.5 Logistic Regres
Page 13 and 14:
x CONTENTS 6.3.1 Adjacent-Categorie
Page 15 and 16:
xii CONTENTS 9. Modeling Correlated
Page 18 and 19:
Preface to the Second Edition In re
Page 20:
PREFACE TO THE SECOND EDITION xvii
Page 23 and 24:
2 INTRODUCTION sciences (e.g., cate
Page 25 and 26:
4 INTRODUCTION models for continuou
Page 27 and 28:
6 INTRODUCTION The multinomial is a
Page 29 and 30:
8 INTRODUCTION precise, in terms of
Page 31 and 32:
10 INTRODUCTION that are judged pla
Page 33 and 34:
12 INTRODUCTION In this text, we us
Page 35 and 36:
14 INTRODUCTION 1.4.4 Small-Sample
Page 37 and 38:
16 INTRODUCTION For small samples,
Page 39 and 40:
18 INTRODUCTION the outcome y equal
Page 41 and 42:
20 INTRODUCTION a. Calculate the lo
Page 43 and 44:
22 CONTINGENCY TABLES Suppose there
Page 45 and 46:
24 CONTINGENCY TABLES Figure 2.1. T
Page 47 and 48:
26 CONTINGENCY TABLES compare two g
Page 49 and 50:
28 CONTINGENCY TABLES It can be any
Page 51 and 52:
30 CONTINGENCY TABLES The sample od
Page 53 and 54:
32 CONTINGENCY TABLES For Table 2.3
Page 55 and 56:
34 CONTINGENCY TABLES the binomial
Page 57 and 58:
36 CONTINGENCY TABLES The df value
Page 59 and 60:
38 CONTINGENCY TABLES Table 2.5. Cr
Page 61 and 62:
40 CONTINGENCY TABLES that combines
Page 63 and 64:
42 CONTINGENCY TABLES Large values
Page 65 and 66:
44 CONTINGENCY TABLES when the data
Page 67 and 68:
46 CONTINGENCY TABLES n11 equals P(
Page 69 and 70:
48 CONTINGENCY TABLES the observed
Page 71 and 72:
50 CONTINGENCY TABLES Table 2.10. D
Page 73 and 74:
52 CONTINGENCY TABLES Figure 2.4. P
Page 75 and 76:
54 CONTINGENCY TABLES marginal tabl
Page 77 and 78:
56 CONTINGENCY TABLES 2.4 A newspap
Page 79 and 80:
58 CONTINGENCY TABLES a. Construct
Page 81 and 82:
60 CONTINGENCY TABLES Table 2.14. D
Page 83 and 84:
62 CONTINGENCY TABLES a. Show that
Page 85 and 86:
64 CONTINGENCY TABLES 2.36 Give a
Page 87 and 88:
66 GENERALIZED LINEAR MODELS 3.1 CO
Page 89 and 90:
68 GENERALIZED LINEAR MODELS The ne
Page 91 and 92:
70 GENERALIZED LINEAR MODELS Figure
Page 93 and 94:
72 GENERALIZED LINEAR MODELS fitted
Page 95 and 96:
74 GENERALIZED LINEAR MODELS 3.3 GE
Page 97 and 98:
76 Table 3.2. Number of Crab Satell
Page 99 and 100:
Page 101 and 102:
Page 103 and 104:
82 GENERALIZED LINEAR MODELS with S
Page 105 and 106:
84 GENERALIZED LINEAR MODELS Simila
Page 107 and 108:
86 GENERALIZED LINEAR MODELS provid
Page 109 and 110:
88 GENERALIZED LINEAR MODELS 3.5 FI
Page 111 and 112:
90 GENERALIZED LINEAR MODELS Table
Page 113 and 114:
92 GENERALIZED LINEAR MODELS 37 obs
Page 115 and 116:
94 GENERALIZED LINEAR MODELS Table
Page 117 and 118:
96 GENERALIZED LINEAR MODELS 3.18 T
Page 119 and 120:
98 GENERALIZED LINEAR MODELS 3.22 T
Page 121 and 122:
100 LOGISTIC REGRESSION The logisti
Page 123 and 124:
102 LOGISTIC REGRESSION of observat
Page 125 and 126:
104 LOGISTIC REGRESSION for crabs i
Page 127 and 128:
106 LOGISTIC REGRESSION that P(Y =
Page 129 and 130:
108 LOGISTIC REGRESSION −2(L0 −
Page 131 and 132:
110 LOGISTIC REGRESSION The estimat
Page 133 and 134:
112 LOGISTIC REGRESSION Table 4.4.
Page 135 and 136:
114 LOGISTIC REGRESSION 4.3.4 The C
Page 137 and 138:
116 LOGISTIC REGRESSION 4.4.1 Examp
Page 139 and 140:
118 LOGISTIC REGRESSION 4.4.2 Model
Page 141 and 142:
120 LOGISTIC REGRESSION 1.2, based
Page 143 and 144:
122 LOGISTIC REGRESSION activity of
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
128 LOGISTIC REGRESSION 0 = no) and
Page 151 and 152:
Page 153 and 154: 132 LOGISTIC REGRESSION b. In Table
Page 155 and 156: 134 LOGISTIC REGRESSION Table 4.20.
Page 157 and 158: 136 LOGISTIC REGRESSION c. The lack
Page 159 and 160: 138 BUILDING AND APPLYING LOGISTIC
Page 195 and 196: 174 MULTICATEGORY LOGIT MODELS are
Page 197 and 198: 176 MULTICATEGORY LOGIT MODELS By e
Page 199 and 200: 178 MULTICATEGORY LOGIT MODELS 6.1.
Page 201 and 202: 180 MULTICATEGORY LOGIT MODELS 6.2
Page 203: 182 MULTICATEGORY LOGIT MODELS Then
Page 207 and 208: 186 MULTICATEGORY LOGIT MODELS Tabl
Page 209 and 210: 188 MULTICATEGORY LOGIT MODELS only
Page 211 and 212: 190 MULTICATEGORY LOGIT MODELS 6.3.
Page 215 and 216: 194 MULTICATEGORY LOGIT MODELS With
Page 217 and 218: 196 MULTICATEGORY LOGIT MODELS high
Page 225 and 226: CHAPTER 7 Loglinear Models for Cont
Page 227 and 228: 206 LOGLINEAR MODELS FOR CONTINGENC
Page 237 and 238: 216 Table 7.9. Injury (I) by Gender
Page 255 and 256:
234 LOGLINEAR MODELS FOR CONTINGENC
Page 257 and 258:
Page 259 and 260:
Page 261 and 262:
Page 263 and 264:
Page 265 and 266:
CHAPTER 8 Models for Matched Pairs
Page 267 and 268:
246 MODELS FOR MATCHED PAIRS are nu
Page 269 and 270:
248 MODELS FOR MATCHED PAIRS An alt
Page 271 and 272:
250 MODELS FOR MATCHED PAIRS This c
Page 273 and 274:
252 MODELS FOR MATCHED PAIRS only a
Page 275 and 276:
254 MODELS FOR MATCHED PAIRS Table
Page 277 and 278:
Page 279 and 280:
258 MODELS FOR MATCHED PAIRS from t
Page 281 and 282:
260 MODELS FOR MATCHED PAIRS likeli
Page 283 and 284:
262 MODELS FOR MATCHED PAIRS the ad
Page 285 and 286:
264 MODELS FOR MATCHED PAIRS 8.5.5
Page 287 and 288:
266 MODELS FOR MATCHED PAIRS ˆβ4
Page 289 and 290:
268 MODELS FOR MATCHED PAIRS 8.7 Re
Page 291 and 292:
270 MODELS FOR MATCHED PAIRS b. The
Page 293 and 294:
Page 295 and 296:
Page 297 and 298:
CHAPTER 9 Modeling Correlated, Clus
Page 299 and 300:
278 MODELING CORRELATED, CLUSTERED
Page 301 and 302:
Page 303 and 304:
Page 305 and 306:
Page 307 and 308:
Page 309 and 310:
Page 311 and 312:
Page 313 and 314:
Page 315 and 316:
Page 317 and 318:
Page 319 and 320:
298 RANDOM EFFECTS: GENERALIZED LIN
Page 321 and 322:
Page 323 and 324:
Page 325 and 326:
Page 327 and 328:
Page 329 and 330:
Page 331 and 332:
Page 333 and 334:
Page 335 and 336:
Page 337 and 338:
Page 339 and 340:
Page 341 and 342:
Page 343 and 344:
Page 345 and 346:
Page 347 and 348:
326 A HISTORICAL TOUR OF CATEGORICA
Page 349 and 350:
Page 351 and 352:
Page 353 and 354:
Appendix A: Software for Categorica
Page 355 and 356:
334 APPENDIX A: SOFTWARE FOR CATEGO
Page 357 and 358:
Page 359 and 360:
Page 361 and 362:
Page 363 and 364:
Page 365 and 366:
Bibliography Agresti, A. (2002). Ca
Page 367 and 368:
Index of Examples abortion, opinion
Page 369 and 370:
348 INDEX OF EXAMPLES lung cancer a
Page 371 and 372:
Subject Index Adjacent categories l
Page 373 and 374:
352 SUBJECT INDEX Exact inference (
Page 375 and 376:
354 SUBJECT INDEX Loglinear models
Page 377 and 378:
356 SUBJECT INDEX Residuals binomia
Page 379 and 380:
358 BRIEF SOLUTIONS TO SOME ODD-NUM
Page 381 and 382:
Page 383 and 384:
Page 385 and 386:
Page 387 and 388:
Page 389 and 390:
Page 391 and 392:
Page 393:
show all

Introduction to Categorical Data Analysis

Create successful ePaper yourself

Delete template?

Save as template?