Introduction to Categorical Data Analysis

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BIBLIOGRAPHY 345 Hosmer, D. W. and S. Lemeshow (2000). Applied Logistic Regression, 2nd edn. New York: Wiley. Lloyd, C. J. (1999). Statistical Analysis of Categorical Data. New York: Wiley. Mehta, C. R. and N. R. Patel (1995). Exact logistic regression: Theory and examples. Statist. Med., 14: 2143–2160. Molenberghs, G. and G. Verbeke (2005). Models for Discrete Longitudinal Data. New York: Springer. O’Hagan, A. and J. Forster (2004). Kendall’s Advanced Theory of Statistics: Bayesian Inference. London: Arnold. Raudenbush, S. and A. Bryk (2002). Hierarchical Linear Models, 2nd edn. Thousand Oaks, CA: Sage. Santner, T. J. and D. E. Duffy (1989). The Statistical Analysis of Discrete Data. Berlin: Springer. Simonoff, J. S. (2003). Analyzing Categorical Data. New York: Springer. Snijders, T. A. B. and R. J. Bosker (1999). Multilevel Analysis. London: Sage. Stokes, M. E., C. S. Davis, and G. G. Koch (2000). Categorical Data Analysis Using the SAS System, 2nd edn. Cary, NC: SAS Institute. Whittaker, J. (1990). Graphical Models in Applied Multivariate Statistics. New York: Wiley.
Index of Examples abortion, opinion on legalization, 8–9, 129, 294–295, 305–307, 341 accident rates, 83–84, 95, 96 admissions into graduate school, Berkeley, 168, 237 admissions into graduate school, Florida, 149–150, 321 adverse events, 311–313 afterlife, belief in, 21, 22–23, 25, 178–179, 206–207, 232–233 AIDS, opinion items, 233–234, 267 albumin in critically ill patients, 134, 322 alcohol, cigarette, and marijuana use, 209–215, 224, 226–228, 269, 320, 337 alcohol and infant malformation, 42–44, 91–92, 169 alligator food choice, 174–177, 197, 336 arrests and soccer attendance, 96 aspirin and heart disease, 26–28, 30–32, 57–58, 64 astrology, belief in, 58 automobile accidents in Florida, 238 automobile accidents in Maine, 202, 215–221, 240 automobile accident rates for elderly men and women, 95 AZT and AIDS, 111–112, 146, 336 baseball and complete games, 123 basketball free throws, 303–304, 318, 319, 341 birth control opinion and political, religious, sex items, 240 birth control and premarital sex, 228–232, 337–338 birth control and religious attendance, 242 breast cancer, 56, 60, 62, 125 breast cancer and mammograms, 23–24 British train accidents, 83–84, 96–97 Bush, George W., approval ratings, 125 Bush–Gore Presidential election, 90–91 busing, opinion, and other racial items, 233–234 butterfly ballot, 90–91 cancer of larynx and radiation therapy, 62–63 cancer remission and labelling index, 121–122, 165–166 carcinoma, diagnoses, 260–264 cereal and cholesterol, 201, 271 Challenger space shuttle and O-rings, 123–124 chemotherapy and lung cancer, 199 cholesterol and cereal, 201, 271 cholesterol and heart disease, 106, 162 cholesterol and psyllium, 201, 271 An Introduction to Categorical Data Analysis, Second Edition. By Alan Agresti Copyright © 2007 John Wiley & Sons, Inc. 346
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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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Page 315 and 316: 294 MODELING CORRELATED, CLUSTERED
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Page 365: Bibliography Agresti, A. (2002). Ca
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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
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Introduction to Categorical Data Analysis

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