Introduction to Categorical Data Analysis

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INDEX OF EXAMPLES 347 cigarettes, marijuana, and alcohol, 209–215, 224, 226–228, 269, 320, 337 clinical trial (generic), 10 clinical trial for curing infection, 129–130, 322 clinical trial for fungal infections, 154–156 clinical trial for insomnia, 285–287, 295, 310–311 clinical trial for lung cancer, 199 clinical trial for ulcers, 294, 311–313 coffee market share, 253–254, 257–258, 262, 338–339 cola taste testing, 273 college (whether attend) and predictors, 133 colon cancer and diet, 268–269 computer chip defects, 93–94 condoms and education, 130–131 coronary risk and obese children, 296 credit cards and income, 92–93, 123 credit scoring, 166–167 cross-over study, 268, 291–292, 320, 324 death penalty, and racial items, 49–53, 63, 125–126, 135–136, 235 death rates in Maine and South Carolina, 63 depression, 277–279, 281–282, 291, 296, 307–309, 340–341 developmental toxicity, 191–193 diabetes and MI for Navajo Indians, 251 diagnoses of carcinoma, 260–264 diagnostic tests, 23–24, 55 diet and colon cancer, 268–269 drug use (Dayton), 209–215, 224, 226–228, 269, 320 educational aspirations and income, 62 elderly drivers and accidents, 95 environmental opinions, 18, 238, 244–250, 255–256, 259–260, 271, 300–301 esophageal cancer, 131–132 extramarital sex and premarital sex, 270–271, 321 fish hatching, 322–323 gender gap and political party identification, 37–40, 333 ghosts, belief in, 58 Government spending, 238–239 grade retention of students, 315–316 graduate admissions at Berkeley, 168, 237 graduate admissions at Florida, 149–150, 321 graduation rates for athletes, 133–134 greenhouse effect and car pollution, 271–272 grouped and ungrouped data, 167 gun homicide rate, 55 happiness and income, 59, 198 happiness and marital status, 203 happiness and religion, 200 health government spending, 238–239 heart attacks and aspirin, 26–28, 30–32, 57–58, 64 heart attacks and smoking, 32–33, 57, 97 heart catheterization, 56–57 heart disease and blood pressure, 151–152 heart disease and snoring, 69–73, 85–86, 92, 106, 123, 334 heaven and hell, 18, 266–267, 318 HIV and early AZT, 111–112, 146, 336 homicide rates, 55, 95, 324 horseshoe crabs and satellites, 75–82, 101–109, 116–121, 124, 133, 138–147, 163, 172, 334–335 incontinence, 171 infant malformation and alcohol, 42–44, 91–92, 169 infection, clinical trials, 129–130, 154–156, 322 insomnia, clinical trial, 285, 295, 310–311 job discrimination, 159 job satisfaction and income, 193–196, 197, 200, 202 job satisfaction and several predictors, 134–135 journal prestige rankings, 273 kyphosis and age, 124 law enforcement spending, 238 lung cancer and chemotherapies, 199 lung cancer meta analysis, 168
348 INDEX OF EXAMPLES lung cancer and smoking, 54, 56, 57, 58–59, 168 lymphocytic infiltration, 121–122, 165–166 mammogram sensitivity/specificity, 23–24, 55 marijuana, cigarette, and alcohol use, 209–215, 224, 226–228, 269, 320, 337 marital happiness and income, 198–199 MBTI personality test, 165 mental impairment, life events, and SES, 186–187, 337 merit pay and race, 127 meta analysis of albumin, 134, 322 meta analysis of heart disease and aspirin, 57 meta analysis of lung cancer and smoking, 168 mice and developmental toxicity, 191–193 migraine headaches, 268 missing people, 167 mobility, residential, 270, 272 motor vehicle accident rates, 95 multiple sclerosis diagnoses, 272 murder and race, 57, 95 murder rates and race and gender, 64 Myers–Briggs personality test (MBTI), 127–128, 164–165, 235–236 myocardial infarction and aspirin, 26–28, 30–32, 57–58, 64 myocardial infarction and diabetes, 251 myocardial infarction and smoking, 32–34 NCAA athlete graduation rates, 133–134 obese children, 296 oral contraceptive use, 128–129 osteosarcoma, 170–171 passive smoking and lung cancer, 49 pathologist diagnoses, 260–264 penicillin protecting rabbits, 169–170 personality tests and smoking, 165 pig farmers, 292–294 political ideology by party affiliation and gender, 182–185, 188–191 political ideology and religious preference, 200 political party and gender, 37–40 political party and race, 59–60 political views, religious attendance, sex, and birth control, 240 pregnancy of girls in Florida counties, 318–319 premarital sex and birth control, 228–232, 337–338 premarital sex and extramarital sex, 270–271, 321 Presidential approval rating, 125 Presidential election, 90–91 Presidential voting and income, 196 Presidential voting, busing, and race, 233–234 primary dysmenorrhea, 291–292 promotion discrimination, 159 prostate cancer, 55 psychiatric diagnosis and drugs, 60–61 psyllium and cholesterol, 201, 271 radiation therapy and cancer, 62–63 recycle or drive less, 255–256, 339–340 recycle and pesticide use, 271 religious attendance and happiness, 200 religious attendance and birth control, 240, 242 religious fundamentalism and education, 61 religious mobility, 269–270 residential mobility, 270, 272 respiratory illness and maternal smoking, 288–289 Russian roulette (Graham Greene), 17–18 sacrifices for environment, 244–250, 300, 338 seat belt and injury or death, 201–202, 215–223, 238 sex opinions, 228–232, 240, 270–271, 321 sexual intercourse, frequency, 95 sexual intercourse and gender and race, 133–134 Shaq O’Neal free throw shooting, 319 silicon wafer defects, 93–94 smoking and lung cancer, 54, 56, 57, 58–59, 168 smoking and myocardial infarction, 32–34 snoring and heart disease, 32–33, 57, 97 soccer attendance and arrests, 96
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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 367: Index of Examples abortion, opinion
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Page 375 and 376: 354 SUBJECT INDEX Loglinear models
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Introduction to Categorical Data Analysis

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