Introduction to Categorical Data Analysis

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INDEX OF EXAMPLES 349 soccer odds (World Cup), 56 sore throat and surgery, 132 space shuttle and O-ring failure, 123–124 strokes and aspirin, 57–58 student advancement, 314–316 taxes higher or cut living standards, 244–250, 337–338 tea tasting, 46–48, 334 teenage birth control and religious attendance, 242 teenage crime, 60 tennis players, male, 264–266, 340 tennis players, female, 273–274 teratology, 283–284, 304–305 Titanic, 56 toxicity study, 191–193 trains and collisions, 83–84, 96–97 ulcers, clinical trial, 294, 311–313 vegetarianism, 18–19 veterinary information, sources of, 292–294 World Cup, 56
Subject Index Adjacent categories logit, 190–191, 337 Agreement, 260–264 Agresti–Coull confidence interval, 10 AIC (Akaike information criterion), 141–142 Alternating logistic regression, 287 Autoregressive correlation structure, 281 Backward elimination, 139–141, 226, 335 Baseline-category logits, 173–179, 194, 336 Bayesian methods, 17, 317, 331 Bernoulli variable, 4 Binary data, 25–26 dependent proportions, 244–252 grouped vs. ungrouped, 106, 110, 146–147, 148, 167 independent proportions, 26–27 matched pairs, 244–266 models, 68–73, 99–121, 137–162, 334–336 Binomial distribution, 4–5, 13–16, 25 comparing proportions, 26–27, 161, 244–252 confidence interval, 9–10, 20 and GLMs, 68–73 independent binomial sampling, 26 likelihood function, 6–7, 107, 152–153, 155, 157, 280, 316, 318 and logistic regression, 70–71, 99 mean and standard deviation, 4 overdispersion, 192–193, 280, 283–284, 304–305 proportion, inference for, 8–16, 17 residuals, 148 significance tests, 8 small-sample inference, 13–16 Bradley–Terry model, 264–266, 340 Breslow–Day test, 115 Canonical link, 67 Case-control study, 32–24, 105, 250–252, 328 Categorical data analysis, 1–342 Category choice, 189 Chi-squared distribution: df, 35–36, 62, 327 mean and standard deviation, 35 reproductive property, 62 table of percentage points, 343 Chi-squared statistics: independence, 34–38, 61, 333 invariance to category ordering, 40–41 likelihood-ratio, 36. See also Likelihood-ratio statistic linear trend, 41–45, 195 normal components, 62 partitioning, 39–40, 62, 329 Pearson, 35. See also Pearson chi-squared statistic and sparse data, 40, 156–157 An Introduction to Categorical Data Analysis, Second Edition. By Alan Agresti Copyright © 2007 John Wiley & Sons, Inc. 350
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www.dbeBooks.com - An Ebook Library
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An Introduction to Categorical Data
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Copyright © 2007 by John Wiley & S
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vi CONTENTS 2.1.3 Sensitivity and S
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viii CONTENTS 4.1.5 Logistic Regres
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x CONTENTS 6.3.1 Adjacent-Categorie
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xii CONTENTS 9. Modeling Correlated
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Preface to the Second Edition In re
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PREFACE TO THE SECOND EDITION xvii
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2 INTRODUCTION sciences (e.g., cate
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4 INTRODUCTION models for continuou
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6 INTRODUCTION The multinomial is a
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8 INTRODUCTION precise, in terms of
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10 INTRODUCTION that are judged pla
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12 INTRODUCTION In this text, we us
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14 INTRODUCTION 1.4.4 Small-Sample
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16 INTRODUCTION For small samples,
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18 INTRODUCTION the outcome y equal
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20 INTRODUCTION a. Calculate the lo
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22 CONTINGENCY TABLES Suppose there
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24 CONTINGENCY TABLES Figure 2.1. T
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26 CONTINGENCY TABLES compare two g
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28 CONTINGENCY TABLES It can be any
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30 CONTINGENCY TABLES The sample od
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32 CONTINGENCY TABLES For Table 2.3
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34 CONTINGENCY TABLES the binomial
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36 CONTINGENCY TABLES The df value
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38 CONTINGENCY TABLES Table 2.5. Cr
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40 CONTINGENCY TABLES that combines
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42 CONTINGENCY TABLES Large values
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44 CONTINGENCY TABLES when the data
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46 CONTINGENCY TABLES n11 equals P(
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48 CONTINGENCY TABLES the observed
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50 CONTINGENCY TABLES Table 2.10. D
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52 CONTINGENCY TABLES Figure 2.4. P
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54 CONTINGENCY TABLES marginal tabl
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56 CONTINGENCY TABLES 2.4 A newspap
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58 CONTINGENCY TABLES a. Construct
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60 CONTINGENCY TABLES Table 2.14. D
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62 CONTINGENCY TABLES a. Show that
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64 CONTINGENCY TABLES 2.36 Give a
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66 GENERALIZED LINEAR MODELS 3.1 CO
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68 GENERALIZED LINEAR MODELS The ne
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70 GENERALIZED LINEAR MODELS Figure
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72 GENERALIZED LINEAR MODELS fitted
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74 GENERALIZED LINEAR MODELS 3.3 GE
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76 Table 3.2. Number of Crab Satell
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82 GENERALIZED LINEAR MODELS with S
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84 GENERALIZED LINEAR MODELS Simila
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86 GENERALIZED LINEAR MODELS provid
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88 GENERALIZED LINEAR MODELS 3.5 FI
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90 GENERALIZED LINEAR MODELS Table
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92 GENERALIZED LINEAR MODELS 37 obs
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94 GENERALIZED LINEAR MODELS Table
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96 GENERALIZED LINEAR MODELS 3.18 T
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98 GENERALIZED LINEAR MODELS 3.22 T
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100 LOGISTIC REGRESSION The logisti
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102 LOGISTIC REGRESSION of observat
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104 LOGISTIC REGRESSION for crabs i
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106 LOGISTIC REGRESSION that P(Y =
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108 LOGISTIC REGRESSION −2(L0 −
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110 LOGISTIC REGRESSION The estimat
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112 LOGISTIC REGRESSION Table 4.4.
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114 LOGISTIC REGRESSION 4.3.4 The C
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116 LOGISTIC REGRESSION 4.4.1 Examp
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118 LOGISTIC REGRESSION 4.4.2 Model
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120 LOGISTIC REGRESSION 1.2, based
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122 LOGISTIC REGRESSION activity of
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128 LOGISTIC REGRESSION 0 = no) and
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132 LOGISTIC REGRESSION b. In Table
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136 LOGISTIC REGRESSION c. The lack
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138 BUILDING AND APPLYING LOGISTIC
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174 MULTICATEGORY LOGIT MODELS are
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176 MULTICATEGORY LOGIT MODELS By e
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178 MULTICATEGORY LOGIT MODELS 6.1.
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180 MULTICATEGORY LOGIT MODELS 6.2
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182 MULTICATEGORY LOGIT MODELS Then
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184 MULTICATEGORY LOGIT MODELS Like
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186 MULTICATEGORY LOGIT MODELS Tabl
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188 MULTICATEGORY LOGIT MODELS only
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190 MULTICATEGORY LOGIT MODELS 6.3.
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194 MULTICATEGORY LOGIT MODELS With
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196 MULTICATEGORY LOGIT MODELS high
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CHAPTER 7 Loglinear Models for Cont
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206 LOGLINEAR MODELS FOR CONTINGENC
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216 Table 7.9. Injury (I) by Gender
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CHAPTER 8 Models for Matched Pairs
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246 MODELS FOR MATCHED PAIRS are nu
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248 MODELS FOR MATCHED PAIRS An alt
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250 MODELS FOR MATCHED PAIRS This c
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252 MODELS FOR MATCHED PAIRS only a
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254 MODELS FOR MATCHED PAIRS Table
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258 MODELS FOR MATCHED PAIRS from t
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260 MODELS FOR MATCHED PAIRS likeli
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262 MODELS FOR MATCHED PAIRS the ad
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264 MODELS FOR MATCHED PAIRS 8.5.5
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266 MODELS FOR MATCHED PAIRS ˆβ4
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268 MODELS FOR MATCHED PAIRS 8.7 Re
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270 MODELS FOR MATCHED PAIRS b. The
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CHAPTER 9 Modeling Correlated, Clus
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278 MODELING CORRELATED, CLUSTERED
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Introduction to Categorical Data Analysis

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