An introduction to categorical data analysis:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley-Interscience
2007
|
| Ausgabe: | 2. ed. |
| Schriftenreihe: | Wiley series in probability and statistics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Previous ed.: 1996 Includes bibliographical references and index |
| Beschreibung: | XVII, 372 S. graph. Darst. |
| ISBN: | 0471226181 9780471226185 |
Internformat
MARC
| LEADER | 00000nam a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV022469771 | ||
| 003 | DE-604 | ||
| 005 | 20110408 | ||
| 007 | t | ||
| 008 | 070619s2007 xxud||| |||| 00||| eng d | ||
| 010 | |a 2006042138 | ||
| 020 | |a 0471226181 |c cloth |9 0-471-22618-1 | ||
| 020 | |a 9780471226185 |9 978-0-471-22618-5 | ||
| 035 | |a (OCoLC)71812851 | ||
| 035 | |a (DE-599)BVBBV022469771 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 044 | |a xxu |c US | ||
| 049 | |a DE-898 |a DE-19 |a DE-91G |a DE-N2 |a DE-945 |a DE-355 |a DE-473 |a DE-703 |a DE-11 |a DE-824 |a DE-188 |a DE-91 | ||
| 050 | 0 | |a QA278 | |
| 082 | 0 | |a 519.5/35 | |
| 082 | 0 | |a 519.5 | |
| 084 | |a CM 4000 |0 (DE-625)18951: |2 rvk | ||
| 084 | |a MR 2100 |0 (DE-625)123488: |2 rvk | ||
| 084 | |a QH 234 |0 (DE-625)141549: |2 rvk | ||
| 084 | |a SK 800 |0 (DE-625)143256: |2 rvk | ||
| 084 | |a SK 830 |0 (DE-625)143259: |2 rvk | ||
| 084 | |a SK 840 |0 (DE-625)143261: |2 rvk | ||
| 084 | |a MAT 627f |2 stub | ||
| 100 | 1 | |a Agresti, Alan |d 1947- |e Verfasser |0 (DE-588)141458062 |4 aut | |
| 245 | 1 | 0 | |a An introduction to categorical data analysis |c Alan Agresti |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a Hoboken, NJ |b Wiley-Interscience |c 2007 | |
| 300 | |a XVII, 372 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Wiley series in probability and statistics | |
| 500 | |a Previous ed.: 1996 | ||
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Analyse multivariée | |
| 650 | 4 | |a Análisis multivariado | |
| 650 | 7 | |a Data-analyse |2 gtt | |
| 650 | 7 | |a Kwalitatieve gegevens |2 gtt | |
| 650 | 4 | |a Çok değişkenli analiz | |
| 650 | 4 | |a Multivariate analysis | |
| 650 | 0 | 7 | |a Datenanalyse |0 (DE-588)4123037-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Kontingenztafelanalyse |0 (DE-588)4201359-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Qualitative Daten |0 (DE-588)4176592-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Multivariate Analyse |0 (DE-588)4040708-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Kategoriale Daten |0 (DE-588)4327512-6 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Kategoriale Daten |0 (DE-588)4327512-6 |D s |
| 689 | 0 | 1 | |a Multivariate Analyse |0 (DE-588)4040708-1 |D s |
| 689 | 0 | |8 1\p |5 DE-604 | |
| 689 | 1 | 0 | |a Qualitative Daten |0 (DE-588)4176592-8 |D s |
| 689 | 1 | 1 | |a Kontingenztafelanalyse |0 (DE-588)4201359-8 |D s |
| 689 | 1 | |8 2\p |5 DE-604 | |
| 689 | 2 | 0 | |a Datenanalyse |0 (DE-588)4123037-1 |D s |
| 689 | 2 | |8 3\p |5 DE-604 | |
| 856 | 4 | 2 | |m Digitalisierung UB Bamberg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015677281&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-015677281 | ||
| 883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 883 | 1 | |8 2\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 883 | 1 | |8 3\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
Datensatz im Suchindex
| _version_ | 1804136556489867264 |
|---|---|
| adam_text | Contents
Preface
to the Second Edition
xv
1.
Introduction
1
1.1
Categorical Response Data,
1
1.1.1
Response/Explanatory Variable Distinction,
2
1.1.2
Nominal/Ordinal Scale Distinction,
2
1.1.3
Organization of this Book,
3
1.2
Probability Distributions for Categorical Data,
3
1.2.1
Binomial Distribution,
4
1.2.2
Multinomial Distribution,
5
1.3
Statistical Inference for a Proportion,
6
1.3.1
Likelihood Function and Maximum Likelihood Estimation,
6
1.3.2
Significance Test About a Binomial Proportion,
8
1.3.3
Example: Survey Results on Legalizing Abortion,
8
1.3.4
Confidence Intervals for a Binomial Proportion,
9
1.4
More on Statistical Inference for Discrete Data,
11
1.4.1 Wald,
Likelihood-Ratio, and Score Inference,
11
1.4.2 Wald,
Score, and Likelihood-Ratio Inference for
Binomial Parameter,
12
1.4.3
Small-Sample Binomial Inference,
13
1.4.4
Small-Sample Discrete Inference is Conservative,
14
1.4.5
Inference Based on the Mid f-value,
15
1.4.6
Summary,
16
Problems,
16
2.
Contingency Tables
21
2.1
Probability Structure for Contingency Tables,
21
2.1.1
Joint, Marginal, and Conditional Probabilities,
22
2.1.2
Example: Belief in Afterlife,
22
VI
CONTENTS
2.1.3
Sensitivity and Specificity in Diagnostic Tests,
23
2.1.4
Independence,
24
2.1.5
Binomial and Multinomial Sampling,
25
2.2
Comparing Proportions in Two-by-Two Tables,
25
2.2.1
Difference of Proportions,
26
2.2.2
Example: Aspirin and Heart Attacks,
26
2.2.3
Relative Risk,
27
2.3
The Odds Ratio,
28
2.3.1
Properties of the Odds Ratio,
29
2.3.2
Example: Odds Ratio for Aspirin Use and Heart Attacks,
30
2.3.3
Inference for Odds Ratios and Log Odds Ratios,
30
2.3.4
Relationship Between Odds Ratio and Relative Risk,
32
2.3.5
The Odds Ratio Applies in Case-Control Studies,
32
2.3.6
Types of Observational Studies,
34
2.4
Chi-Squared Tests of Independence,
34
2.4.1
Pearson Statistic and the Chi-Squared Distribution,
35
2.4.2
Likelihood-Ratio Statistic,
36
2.4.3
Tests of Independence,
36
2.4.4
Example: Gender Gap in Political Affiliation,
37
2.4.5
Residuals for Cells in a Contingency Table,
38
2.4.6
Partitioning Chi-Squared,
39
2.4.7
Comments About Chi-Squared Tests,
40
2.5
Testing Independence for Ordinal Data,
41
2.5.1
Linear Trend Alternative to Independence,
41
2.5.2
Example: Alcohol Use and Infant Malformation,
42
2.5.3
Extra Power with Ordinal Tests,
43
2.5.4
Choice of Scores,
43
2.5.5
Trend Tests for
/
χ
2
and
2
x J
Tables,
44
2.5.6
Nominal-Ordinal Tables,
45
2.6
Exact Inference for Small Samples,
45
2.6.1
Fisher s Exact Test for
2
x
2
Tables,
45
2.6.2
Example: Fisher s Tea Taster,
46
2.6.3
P-values and Conservatism for Actual f(Type I Error),
47
2.6.4
Small-Sample Confidence Interval for Odds Ratio,
48
2.7
Association in Three-Way Tables,
49
2.7.1
Partial Tables,
49
2.7.2
Conditional Versus Marginal Associations: Death
Penalty Example,
49
2.7.3
Simpson s Paradox,
51
2.7.4
Conditional and Marginal Odds Ratios,
52
2.7.5
Conditional Independence Versus Marginal Independence,
53
2.7.6
Homogeneous Association,
54
Problems,
55
CONTENTS
Vil
3.
Generalized Linear Models
65
3.1
Components of a Generalized Linear Model,
66
3.1.1
Random Component,
66
3.1.2
Systematic Component,
66
3.1.3
Link Function,
66
3.1.4
Normal GLM,
67
3.2
Generalized Linear Models for Binary Data,
68
3.2.1
Linear Probability Model,
68
3.2.2
Example: Snoring and Heart Disease,
69
3.2.3
Logistic Regression Model,
70
3.2.4
Probit
Regression Model,
72
3.2.5
Binary Regression and Cumulative Distribution
Functions,
72
3.3
Generalized Linear Models for Count Data,
74
3.3.1
Poisson
Regression,
75
3.3.2
Example: Female Horseshoe Crabs and their Satellites,
75
3.3.3
Overdispersion: Greater Variability than Expected,
80
3.3.4
Negative Binomial Regression,
81
3.3.5
Count Regression for Rate Data,
82
3.3.6
Example: British Train Accidents over Time,
83
3.4
Statistical Inference and Model Checking,
84
3.4.1
Inference about Model Parameters,
84
3.4.2
Example: Snoring and Heart Disease Revisited,
85
3.4.3
The Deviance,
85
3.4.4
Model Comparison Using the Deviance,
86
3.4.5
Residuals Comparing Observations to the Model Fit,
87
3.5
Fitting Generalized Linear Models,
88
3.5.1
The Newton-Raphson Algorithm Fits GLMs,
88
3.5.2 Wald,
Likelihood-Ratio, and Score Inference Use the
Likelihood Function,
89
3.5.3
Advantages of GLMs,
90
Problems,
90
4.
Logistic Regression
99
4.1
Interpreting the Logistic Regression Model,
99
4.1.1
Linear Approximation Interpretations,
100
4.1.2
Horseshoe Crabs: Viewing and Smoothing a Binary
Outcome,
101
4.1.3
Horseshoe Crabs: Interpreting the Logistic Regression
Fit,
101
4.1.4
Odds Ratio Interpretation,
104
VIU
CONTENTS
4.1.5
Logistic
Regression
with Retrospective Studies,
105
4.1.6
Normally Distributed X Implies Logistic Regression
forF,
105
4.2
Inference for Logistic Regression,
106
4.2.1
Binary Data can be Grouped or Ungrouped,
106
4.2.2
Confidence Intervals for Effects,
106
4.2.3
Significance Testing,
107
4.2.4
Confidence Intervals for Probabilities,
108
4.2.5
Why Use a Model to Estimate Probabilities?,
108
4.2.6
Confidence Intervals for Probabilities: Details,
108
4.2.7
Standard Errors of Model Parameter Estimates,
109
4.3
Logistic Regression with Categorical Predictors,
110
4.3.1
Indicator Variables Represent Categories of Predictors,
110
4.3.2
Example:
AZT
Use and AIDS, 111
4.3.3
ANOVA-Type Model Representation of Factors,
113
4.3.4
The Cochran-Mantel-Haenszel Test for
2
χ
2
x Ä
Contingency Tables,
114
4.3.5
Testing the Homogeneity of Odds Ratios,
115
4.4
Multiple Logistic Regression,
115
4.4.1
Example: Horseshoe Crabs with Color and Width
Predictors,
116
4.4.2
Model Comparison to Check Whether a Term is Needed,
118
4.4.3
Quantitative Treatment of Ordinal Predictor,
118
4.4.4
Allowing Interaction,
119
4.5
Summarizing Effects in Logistic Regression,
120
4.5.1
Probability-Based Interpretations,
120
4.5.2
Standardized Interpretations,
121
Problems,
121
5.
Building and Applying Logistic Regression Models
137
5.1
Strategies in Model Selection,
137
5.1.1
How Many Predictors Can You Use?,
138
5.1.2
Example: Horseshoe Crabs Revisited,
138
5.1.3
Stepwise Variable Selection Algorithms,
139
5.1.4
Example: Backward Elimination for Horseshoe Crabs,
140
5.1.5
AIC, Model Selection, and the Correct Model,
141
5.1.6
Summarizing Predictive Power: Classification Tables,
142
5.1.7
Summarizing Predictive Power: ROC Curves,
143
5.1.8
Summarizing Predictive Power: A Correlation,
144
5.2
Model Checking,
144
5.2.1
Likelihood-Ratio Model Comparison Tests,
144
5.2.2
Goodness of Fit and the Deviance,
145
CONTENTS
IX
5.2.3
Checking Fit: Grouped Data, Ungrouped Data, and
Continuous Predictors,
146
5.2.4
Residuals for Logit Models,
147
5.2.5
Example: Graduate Admissions at University of Florida,
149
5.2.6
Influence Diagnostics for Logistic Regression,
150
5.2.7
Example: Heart Disease and Blood Pressure,
151
5.3
Effects of Sparse Data,
152
5.3.1
Infinite Effect Estimate: Quantitative Predictor,
152
5.3.2
Infinite Effect Estimate: Categorical Predictors,
153
5.3.3
Example: Clinical Trial with Sparse Data,
154
5.3.4
Effect of Small Samples on X2 and G2 Tests,
156
5.4
Conditional Logistic Regression and Exact Inference,
157
5.4.1
Conditional Maximum Likelihood Inference,
157
5.4.2
Small-Sample Tests for Contingency Tables,
158
5.4.3
Example: Promotion Discrimination,
159
5.4.4
Small-Sample Confidence Intervals for Logistic
Parameters and Odds Ratios,
159
5.4.5
Limitations of Small-Sample Exact Methods,
160
5.5
Sample Size and Power for Logistic Regression,
160
5.5.1
Sample Size for Comparing Two Proportions,
161
5.5.2
Sample Size in Logistic Regression,
161
5.5.3
Sample Size in Multiple Logistic Regression,
162
Problems,
163
6.
Multicategory Logit Models
173
6.1
Logit Models for Nominal Responses,
173
6.1.1
Baseline-Category Logits,
173
6.1.2
Example: Alligator Food Choice,
174
6.1.3
Estimating Response Probabilities,
176
6.1.4
Example: Belief in Afterlife,
178
6.1.5
Discrete Choice Models,
179
6.2
Cumulative Logit Models for Ordinal Responses,
180
6.2.1
Cumulative Logit Models with Proportional Odds
Property,
180
6.2.2
Example: Political Ideology and Party Affiliation,
182
6.2.3
Inference about Model Parameters,
184
6.2.4
Checking Model Fit,
184
6.2.5
Example: Modeling Mental Health,
185
6.2.6
Interpretations Comparing Cumulative Probabilities,
187
6.2.7
Latent Variable Motivation,
187
6.2.8
Invariance
to Choice of Response Categories,
189
6.3
Paired-Category Ordinal Logits,
189
CONTENTS
6.3.1 Adjacent-Categories Logits, 190
6.3.2
Example: Political Ideology Revisited,
190
6.3.3
Continuation-Ratio Logits,
191
6.3.4
Example: A Developmental
Toxicity
Study,
191
6.3.5
Overdispersion in Clustered Data,
192
6.4
Tests of Conditional Independence,
193
6.4.1
Example: Job Satisfaction and Income,
193
6.4.2
Generalized Cochran-Mantel-Haenszel Tests,
194
6.4.3
Detecting Nominal-Ordinal Conditional Association,
195
6.4.4
Detecting Nominal-Nominal Conditional Association,
196
Problems,
196
7. Loglinear
Models for Contingency Tables
7.1 Loglinear
Models for Two-Way and Three-Way Tables,
204
204
. 1 Loglinear
Model of Independence for Two-Way Table,
205
.2
Interpretation of Parameters in Independence Model,
205
.3
Saturated Model for Two-Way Tables,
206
.4 Loglinear
Models for Three-Way Tables,
208
.5
Two-Factor Parameters Describe Conditional
Associations,
209
7.1.6
Example: Alcohol, Cigarette, and Marijuana Use,
209
7.2
Inference for
Loglinear
Models,
212
7.2.1
Chi-Squared Goodness-of-Fit Tests,
212
7.2.2 Loglinear
Cell Residuals,
213
7.2.3
Tests about Conditional Associations,
214
7.2.4
Confidence Intervals for Conditional Odds Ratios,
214
7.2.5 Loglinear
Models for Higher Dimensions,
215
7.2.6
Example: Automobile Accidents and Seat Belts,
215
7.2.7
Three-Factor Interaction,
218
7.2.8
Large Samples and Statistical vs Practical Significance,
218
7.3
The Loglinear-Logistic Connection,
219
7.3.1
Using Logistic Models to Interpret
Loglinear
Models,
219
7.3.2
Example: Auto Accident Data Revisited,
220
7.3.3
Correspondence Between
Loglinear
and Logistic Models,
221
7.3.4
Strategies in Model Selection,
221
7.4
Independence Graphs and Collapsibility,
223
7.4.1
Independence Graphs,
223
7.4.2
Collapsibility Conditions for Three-Way Tables,
224
7.4.3
Collapsibility and Logistic Models,
225
7.4.4
Collapsibility and Independence Graphs for Multiway
Tables,
225
7.4.5
Example: Model Building for Student Drug Use,
226
7.4.6
Graphical Models,
228
CONTENTS Xl
7.5
Modeling
Ordinal
Associations,
228
7.5.1
Linear-by-Linear
Association
Model,
229
7.5.2
Example: Sex Opinions,
230
7.5.3
Ordinal Tests of Independence,
232
Problems,
232
8.
Models for Matched Pairs
244
8.1
Comparing Dependent Proportions,
245
8.1.1
McNemar Test Comparing Marginal Proportions,
245
8.1.2
Estimating Differences of Proportions,
246
8.2
Logistic Regression for Matched Pairs,
247
8.2.1
Marginal Models for Marginal Proportions,
247
8.2.2
Subject-Specific and Population-Averaged Tables,
248
8.2.3
Conditional Logistic Regression for Matched-Pairs,
249
8.2.4
Logistic Regression for Matched Case-Control Studies,
250
8.2.5
Connection between McNemar and
Cochran-Mantel-Haenszel Tests,
252
8.3
Comparing Margins of Square Contingency Tables,
252
8.3.1
Marginal Homogeneity and Nominal Classifications,
253
8.3.2
Example: Coffee Brand Market Share,
253
8.3.3
Marginal Homogeneity and Ordered Categories,
254
8.3.4
Example: Recycle or Drive Less to Help Environment?,
255
8.4
Symmetry and Quasi-Symmetry Models for Square Tables,
256
8.4.1
Symmetry as a Logistic Model,
257
8.4.2
Quasi-Symmetry,
257
8.4.3
Example: Coffee Brand Market Share Revisited,
257
8.4.4
Testing Marginal Homogeneity Using Symmetry and
Quasi-Symmetry,
258
8.4.5
An Ordinal Quasi-Symmetry Model,
258
8.4.6
Example: Recycle or Drive Less?,
259
8.4.7
Testing Marginal Homogeneity Using Symmetry and
Ordinal Quasi-Symmetry,
259
8.5
Analyzing Rater Agreement,
260
8.5.1
Cell Residuals for Independence Model,
261
8.5.2
Quasi-independence Model,
261
8.5.3
Odds Ratios Summarizing Agreement,
262
8.5.4
Quasi-Symmetry and Agreement Modeling,
263
8.5.5
Kappa Measure of Agreement,
264
8.6
Bradley-Terry Model for Paired Preferences,
264
8.6.1
The Bradley-Terry Model,
265
8.6.2
Example: Ranking Men Tennis Players,
265
Problems,
266
Xli CONTENTS
9.
Modeling Correlated, Clustered Responses
276
9.1
Marginal Models Versus Conditional Models,
277
9.1.1
Marginal Models for a Clustered Binary Response,
277
9.1.2
Example: Longitudinal Study of Treatments for
Depression,
277
9.1.3
Conditional Models for a Repeated Response,
279
9.2
Marginal Modeling: The GEE Approach,
279
9.2.1
Quasi-Likelihood Methods,
280
9.2.2
Generalized Estimating Equation Methodology: Basic
Ideas,
280
9.2.3
GEE for Binary Data: Depression Study,
281
9.2.4
Example: Teratology Overdispersion,
283
9.2.5
Limitations of GEE Compared with ML,
284
9.3
Extending GEE: Multinomial Responses,
285
9.3.1
Marginal Modeling of a Clustered Multinomial Response,
285
9.3.2
Example: Insomnia Study,
285
9.3.3
Another Way of Modeling Association with GEE,
287
9.3.4
Dealing with Missing Data,
287
9.4
Transitional Modeling, Given the Past,
288
9.4.1
Transitional Models with Explanatory Variables,
288
9.4.2
Example: Respiratory Illness and Maternal Smoking,
288
9.4.3
Comparisons that Control for Initial Response,
289
9.4.4
Transitional Models Relate to
Loglinear
Models,
290
Problems,
290
10.
Random Effects: Generalized Linear Mixed Models
297
10.1
Random Effects Modeling of Clustered Categorical Data,
297
10.1.1
The Generalized Linear Mixed Model,
298
10.1.2
A Logistic GLMM for Binary Matched Pairs,
299
10.1.3
Example: Sacrifices for the Environment Revisited,
300
10.1.4
Differing Effects in Conditional Models and Marginal
Models,
300
10.2
Examples of Random Effects Models for Binary Data,
302
10.2.1
Small-Area Estimation of Binomial Probabilities,
302
10.2.2
Example: Estimating Basketball Free Throw Success,
303
10.2.3
Example: Teratology Overdispersion Revisited,
304
10.2.4
Example: Repeated Responses on Similar Survey Items,
305
10.2.5
Item Response Models: The
Rasch
Model,
307
10.2.6
Example: Depression Study Revisited,
307
10.2.7
Choosing Marginal or Conditional Models,
308
10.2.8
Conditional Models: Random Effects Versus Conditional
ML,
309
CONTENTS XIH
10.3
Extensions
to Multinomial Responses or Multiple Random Effect
Terms,
310
10.3.1
Example: Insomnia Study Revisited,
310
10.3.2
Bivariate Random Effects and Association Heterogeneity,
311
10.4
Multilevel (Hierarchical) Models,
313
10.4.1
Example: Two-Level Model for Student Advancement,
314
10.4.2
Example: Grade Retention,
315
10.5
Model Fitting and Inference for GLMMS,
316
10.5.1
Fitting GLMMs,
316
10.5.2
Inference for Model Parameters and Prediction,
317
Problems,
318
11.
A Historical Tour of Categorical Data Analysis
325
11.1
The Pearson-Yule Association Controversy,
325
11.2
R. A. Fisher s Contributions,
326
11.3
Logistic Regression,
328
11.4
Multiway Contingency Tables and
Loglinear
Models,
329
11.5
Final Comments,
331
Appendix A: Software for Categorical Data Analysis
332
Appendix B: Chi-Squared Distribution Values
343
Bibliography
344
Index of Examples
346
Subject Index
350
Brief Solutions to Some Odd-Numbered Problems
357
|
| adam_txt |
Contents
Preface
to the Second Edition
xv
1.
Introduction
1
1.1
Categorical Response Data,
1
1.1.1
Response/Explanatory Variable Distinction,
2
1.1.2
Nominal/Ordinal Scale Distinction,
2
1.1.3
Organization of this Book,
3
1.2
Probability Distributions for Categorical Data,
3
1.2.1
Binomial Distribution,
4
1.2.2
Multinomial Distribution,
5
1.3
Statistical Inference for a Proportion,
6
1.3.1
Likelihood Function and Maximum Likelihood Estimation,
6
1.3.2
Significance Test About a Binomial Proportion,
8
1.3.3
Example: Survey Results on Legalizing Abortion,
8
1.3.4
Confidence Intervals for a Binomial Proportion,
9
1.4
More on Statistical Inference for Discrete Data,
11
1.4.1 Wald,
Likelihood-Ratio, and Score Inference,
11
1.4.2 Wald,
Score, and Likelihood-Ratio Inference for
Binomial Parameter,
12
1.4.3
Small-Sample Binomial Inference,
13
1.4.4
Small-Sample Discrete Inference is Conservative,
14
1.4.5
Inference Based on the Mid f-value,
15
1.4.6
Summary,
16
Problems,
16
2.
Contingency Tables
21
2.1
Probability Structure for Contingency Tables,
21
2.1.1
Joint, Marginal, and Conditional Probabilities,
22
2.1.2
Example: Belief in Afterlife,
22
VI
CONTENTS
2.1.3
Sensitivity and Specificity in Diagnostic Tests,
23
2.1.4
Independence,
24
2.1.5
Binomial and Multinomial Sampling,
25
2.2
Comparing Proportions in Two-by-Two Tables,
25
2.2.1
Difference of Proportions,
26
2.2.2
Example: Aspirin and Heart Attacks,
26
2.2.3
Relative Risk,
27
2.3
The Odds Ratio,
28
2.3.1
Properties of the Odds Ratio,
29
2.3.2
Example: Odds Ratio for Aspirin Use and Heart Attacks,
30
2.3.3
Inference for Odds Ratios and Log Odds Ratios,
30
2.3.4
Relationship Between Odds Ratio and Relative Risk,
32
2.3.5
The Odds Ratio Applies in Case-Control Studies,
32
2.3.6
Types of Observational Studies,
34
2.4
Chi-Squared Tests of Independence,
34
2.4.1
Pearson Statistic and the Chi-Squared Distribution,
35
2.4.2
Likelihood-Ratio Statistic,
36
2.4.3
Tests of Independence,
36
2.4.4
Example: Gender Gap in Political Affiliation,
37
2.4.5
Residuals for Cells in a Contingency Table,
38
2.4.6
Partitioning Chi-Squared,
39
2.4.7
Comments About Chi-Squared Tests,
40
2.5
Testing Independence for Ordinal Data,
41
2.5.1
Linear Trend Alternative to Independence,
41
2.5.2
Example: Alcohol Use and Infant Malformation,
42
2.5.3
Extra Power with Ordinal Tests,
43
2.5.4
Choice of Scores,
43
2.5.5
Trend Tests for
/
χ
2
and
2
x J
Tables,
44
2.5.6
Nominal-Ordinal Tables,
45
2.6
Exact Inference for Small Samples,
45
2.6.1
Fisher's Exact Test for
2
x
2
Tables,
45
2.6.2
Example: Fisher's Tea Taster,
46
2.6.3
P-values and Conservatism for Actual f(Type I Error),
47
2.6.4
Small-Sample Confidence Interval for Odds Ratio,
48
2.7
Association in Three-Way Tables,
49
2.7.1
Partial Tables,
49
2.7.2
Conditional Versus Marginal Associations: Death
Penalty Example,
49
2.7.3
Simpson's Paradox,
51
2.7.4
Conditional and Marginal Odds Ratios,
52
2.7.5
Conditional Independence Versus Marginal Independence,
53
2.7.6
Homogeneous Association,
54
Problems,
55
CONTENTS
Vil
3.
Generalized Linear Models
65
3.1
Components of a Generalized Linear Model,
66
3.1.1
Random Component,
66
3.1.2
Systematic Component,
66
3.1.3
Link Function,
66
3.1.4
Normal GLM,
67
3.2
Generalized Linear Models for Binary Data,
68
3.2.1
Linear Probability Model,
68
3.2.2
Example: Snoring and Heart Disease,
69
3.2.3
Logistic Regression Model,
70
3.2.4
Probit
Regression Model,
72
3.2.5
Binary Regression and Cumulative Distribution
Functions,
72
3.3
Generalized Linear Models for Count Data,
74
3.3.1
Poisson
Regression,
75
3.3.2
Example: Female Horseshoe Crabs and their Satellites,
75
3.3.3
Overdispersion: Greater Variability than Expected,
80
3.3.4
Negative Binomial Regression,
81
3.3.5
Count Regression for Rate Data,
82
3.3.6
Example: British Train Accidents over Time,
83
3.4
Statistical Inference and Model Checking,
84
3.4.1
Inference about Model Parameters,
84
3.4.2
Example: Snoring and Heart Disease Revisited,
85
3.4.3
The Deviance,
85
3.4.4
Model Comparison Using the Deviance,
86
3.4.5
Residuals Comparing Observations to the Model Fit,
87
3.5
Fitting Generalized Linear Models,
88
3.5.1
The Newton-Raphson Algorithm Fits GLMs,
88
3.5.2 Wald,
Likelihood-Ratio, and Score Inference Use the
Likelihood Function,
89
3.5.3
Advantages of GLMs,
90
Problems,
90
4.
Logistic Regression
99
4.1
Interpreting the Logistic Regression Model,
99
4.1.1
Linear Approximation Interpretations,
100
4.1.2
Horseshoe Crabs: Viewing and Smoothing a Binary
Outcome,
101
4.1.3
Horseshoe Crabs: Interpreting the Logistic Regression
Fit,
101
4.1.4
Odds Ratio Interpretation,
104
VIU
CONTENTS
4.1.5
Logistic
Regression
with Retrospective Studies,
105
4.1.6
Normally Distributed X Implies Logistic Regression
forF,
105
4.2
Inference for Logistic Regression,
106
4.2.1
Binary Data can be Grouped or Ungrouped,
106
4.2.2
Confidence Intervals for Effects,
106
4.2.3
Significance Testing,
107
4.2.4
Confidence Intervals for Probabilities,
108
4.2.5
Why Use a Model to Estimate Probabilities?,
108
4.2.6
Confidence Intervals for Probabilities: Details,
108
4.2.7
Standard Errors of Model Parameter Estimates,
109
4.3
Logistic Regression with Categorical Predictors,
110
4.3.1
Indicator Variables Represent Categories of Predictors,
110
4.3.2
Example:
AZT
Use and AIDS, 111
4.3.3
ANOVA-Type Model Representation of Factors,
113
4.3.4
The Cochran-Mantel-Haenszel Test for
2
χ
2
x Ä'
Contingency Tables,
114
4.3.5
Testing the Homogeneity of Odds Ratios,
115
4.4
Multiple Logistic Regression,
115
4.4.1
Example: Horseshoe Crabs with Color and Width
Predictors,
116
4.4.2
Model Comparison to Check Whether a Term is Needed,
118
4.4.3
Quantitative Treatment of Ordinal Predictor,
118
4.4.4
Allowing Interaction,
119
4.5
Summarizing Effects in Logistic Regression,
120
4.5.1
Probability-Based Interpretations,
120
4.5.2
Standardized Interpretations,
121
Problems,
121
5.
Building and Applying Logistic Regression Models
137
5.1
Strategies in Model Selection,
137
5.1.1
How Many Predictors Can You Use?,
138
5.1.2
Example: Horseshoe Crabs Revisited,
138
5.1.3
Stepwise Variable Selection Algorithms,
139
5.1.4
Example: Backward Elimination for Horseshoe Crabs,
140
5.1.5
AIC, Model Selection, and the "Correct" Model,
141
5.1.6
Summarizing Predictive Power: Classification Tables,
142
5.1.7
Summarizing Predictive Power: ROC Curves,
143
5.1.8
Summarizing Predictive Power: A Correlation,
144
5.2
Model Checking,
144
5.2.1
Likelihood-Ratio Model Comparison Tests,
144
5.2.2
Goodness of Fit and the Deviance,
145
CONTENTS
IX
5.2.3
Checking Fit: Grouped Data, Ungrouped Data, and
Continuous Predictors,
146
5.2.4
Residuals for Logit Models,
147
5.2.5
Example: Graduate Admissions at University of Florida,
149
5.2.6
Influence Diagnostics for Logistic Regression,
150
5.2.7
Example: Heart Disease and Blood Pressure,
151
5.3
Effects of Sparse Data,
152
5.3.1
Infinite Effect Estimate: Quantitative Predictor,
152
5.3.2
Infinite Effect Estimate: Categorical Predictors,
153
5.3.3
Example: Clinical Trial with Sparse Data,
154
5.3.4
Effect of Small Samples on X2 and G2 Tests,
156
5.4
Conditional Logistic Regression and Exact Inference,
157
5.4.1
Conditional Maximum Likelihood Inference,
157
5.4.2
Small-Sample Tests for Contingency Tables,
158
5.4.3
Example: Promotion Discrimination,
159
5.4.4
Small-Sample Confidence Intervals for Logistic
Parameters and Odds Ratios,
159
5.4.5
Limitations of Small-Sample Exact Methods,
160
5.5
Sample Size and Power for Logistic Regression,
160
5.5.1
Sample Size for Comparing Two Proportions,
161
5.5.2
Sample Size in Logistic Regression,
161
5.5.3
Sample Size in Multiple Logistic Regression,
162
Problems,
163
6.
Multicategory Logit Models
173
6.1
Logit Models for Nominal Responses,
173
6.1.1
Baseline-Category Logits,
173
6.1.2
Example: Alligator Food Choice,
174
6.1.3
Estimating Response Probabilities,
176
6.1.4
Example: Belief in Afterlife,
178
6.1.5
Discrete Choice Models,
179
6.2
Cumulative Logit Models for Ordinal Responses,
180
6.2.1
Cumulative Logit Models with Proportional Odds
Property,
180
6.2.2
Example: Political Ideology and Party Affiliation,
182
6.2.3
Inference about Model Parameters,
184
6.2.4
Checking Model Fit,
184
6.2.5
Example: Modeling Mental Health,
185
6.2.6
Interpretations Comparing Cumulative Probabilities,
187
6.2.7
Latent Variable Motivation,
187
6.2.8
Invariance
to Choice of Response Categories,
189
6.3
Paired-Category Ordinal Logits,
189
CONTENTS
6.3.1 Adjacent-Categories Logits, 190
6.3.2
Example: Political Ideology Revisited,
190
6.3.3
Continuation-Ratio Logits,
191
6.3.4
Example: A Developmental
Toxicity
Study,
191
6.3.5
Overdispersion in Clustered Data,
192
6.4
Tests of Conditional Independence,
193
6.4.1
Example: Job Satisfaction and Income,
193
6.4.2
Generalized Cochran-Mantel-Haenszel Tests,
194
6.4.3
Detecting Nominal-Ordinal Conditional Association,
195
6.4.4
Detecting Nominal-Nominal Conditional Association,
196
Problems,
196
7. Loglinear
Models for Contingency Tables
7.1 Loglinear
Models for Two-Way and Three-Way Tables,
204
204
. 1 Loglinear
Model of Independence for Two-Way Table,
205
.2
Interpretation of Parameters in Independence Model,
205
.3
Saturated Model for Two-Way Tables,
206
.4 Loglinear
Models for Three-Way Tables,
208
.5
Two-Factor Parameters Describe Conditional
Associations,
209
7.1.6
Example: Alcohol, Cigarette, and Marijuana Use,
209
7.2
Inference for
Loglinear
Models,
212
7.2.1
Chi-Squared Goodness-of-Fit Tests,
212
7.2.2 Loglinear
Cell Residuals,
213
7.2.3
Tests about Conditional Associations,
214
7.2.4
Confidence Intervals for Conditional Odds Ratios,
214
7.2.5 Loglinear
Models for Higher Dimensions,
215
7.2.6
Example: Automobile Accidents and Seat Belts,
215
7.2.7
Three-Factor Interaction,
218
7.2.8
Large Samples and Statistical vs Practical Significance,
218
7.3
The Loglinear-Logistic Connection,
219
7.3.1
Using Logistic Models to Interpret
Loglinear
Models,
219
7.3.2
Example: Auto Accident Data Revisited,
220
7.3.3
Correspondence Between
Loglinear
and Logistic Models,
221
7.3.4
Strategies in Model Selection,
221
7.4
Independence Graphs and Collapsibility,
223
7.4.1
Independence Graphs,
223
7.4.2
Collapsibility Conditions for Three-Way Tables,
224
7.4.3
Collapsibility and Logistic Models,
225
7.4.4
Collapsibility and Independence Graphs for Multiway
Tables,
225
7.4.5
Example: Model Building for Student Drug Use,
226
7.4.6
Graphical Models,
228
CONTENTS Xl
7.5
Modeling
Ordinal
Associations,
228
7.5.1
Linear-by-Linear
Association
Model,
229
7.5.2
Example: Sex Opinions,
230
7.5.3
Ordinal Tests of Independence,
232
Problems,
232
8.
Models for Matched Pairs
244
8.1
Comparing Dependent Proportions,
245
8.1.1
McNemar Test Comparing Marginal Proportions,
245
8.1.2
Estimating Differences of Proportions,
246
8.2
Logistic Regression for Matched Pairs,
247
8.2.1
Marginal Models for Marginal Proportions,
247
8.2.2
Subject-Specific and Population-Averaged Tables,
248
8.2.3
Conditional Logistic Regression for Matched-Pairs,
249
8.2.4
Logistic Regression for Matched Case-Control Studies,
250
8.2.5
Connection between McNemar and
Cochran-Mantel-Haenszel Tests,
252
8.3
Comparing Margins of Square Contingency Tables,
252
8.3.1
Marginal Homogeneity and Nominal Classifications,
253
8.3.2
Example: Coffee Brand Market Share,
253
8.3.3
Marginal Homogeneity and Ordered Categories,
254
8.3.4
Example: Recycle or Drive Less to Help Environment?,
255
8.4
Symmetry and Quasi-Symmetry Models for Square Tables,
256
8.4.1
Symmetry as a Logistic Model,
257
8.4.2
Quasi-Symmetry,
257
8.4.3
Example: Coffee Brand Market Share Revisited,
257
8.4.4
Testing Marginal Homogeneity Using Symmetry and
Quasi-Symmetry,
258
8.4.5
An Ordinal Quasi-Symmetry Model,
258
8.4.6
Example: Recycle or Drive Less?,
259
8.4.7
Testing Marginal Homogeneity Using Symmetry and
Ordinal Quasi-Symmetry,
259
8.5
Analyzing Rater Agreement,
260
8.5.1
Cell Residuals for Independence Model,
261
8.5.2
Quasi-independence Model,
261
8.5.3
Odds Ratios Summarizing Agreement,
262
8.5.4
Quasi-Symmetry and Agreement Modeling,
263
8.5.5
Kappa Measure of Agreement,
264
8.6
Bradley-Terry Model for Paired Preferences,
264
8.6.1
The Bradley-Terry Model,
265
8.6.2
Example: Ranking Men Tennis Players,
265
Problems,
266
Xli CONTENTS
9.
Modeling Correlated, Clustered Responses
276
9.1
Marginal Models Versus Conditional Models,
277
9.1.1
Marginal Models for a Clustered Binary Response,
277
9.1.2
Example: Longitudinal Study of Treatments for
Depression,
277
9.1.3
Conditional Models for a Repeated Response,
279
9.2
Marginal Modeling: The GEE Approach,
279
9.2.1
Quasi-Likelihood Methods,
280
9.2.2
Generalized Estimating Equation Methodology: Basic
Ideas,
280
9.2.3
GEE for Binary Data: Depression Study,
281
9.2.4
Example: Teratology Overdispersion,
283
9.2.5
Limitations of GEE Compared with ML,
284
9.3
Extending GEE: Multinomial Responses,
285
9.3.1
Marginal Modeling of a Clustered Multinomial Response,
285
9.3.2
Example: Insomnia Study,
285
9.3.3
Another Way of Modeling Association with GEE,
287
9.3.4
Dealing with Missing Data,
287
9.4
Transitional Modeling, Given the Past,
288
9.4.1
Transitional Models with Explanatory Variables,
288
9.4.2
Example: Respiratory Illness and Maternal Smoking,
288
9.4.3
Comparisons that Control for Initial Response,
289
9.4.4
Transitional Models Relate to
Loglinear
Models,
290
Problems,
290
10.
Random Effects: Generalized Linear Mixed Models
297
10.1
Random Effects Modeling of Clustered Categorical Data,
297
10.1.1
The Generalized Linear Mixed Model,
298
10.1.2
A Logistic GLMM for Binary Matched Pairs,
299
10.1.3
Example: Sacrifices for the Environment Revisited,
300
10.1.4
Differing Effects in Conditional Models and Marginal
Models,
300
10.2
Examples of Random Effects Models for Binary Data,
302
10.2.1
Small-Area Estimation of Binomial Probabilities,
302
10.2.2
Example: Estimating Basketball Free Throw Success,
303
10.2.3
Example: Teratology Overdispersion Revisited,
304
10.2.4
Example: Repeated Responses on Similar Survey Items,
305
10.2.5
Item Response Models: The
Rasch
Model,
307
10.2.6
Example: Depression Study Revisited,
307
10.2.7
Choosing Marginal or Conditional Models,
308
10.2.8
Conditional Models: Random Effects Versus Conditional
ML,
309
CONTENTS XIH
10.3
Extensions
to Multinomial Responses or Multiple Random Effect
Terms,
310
10.3.1
Example: Insomnia Study Revisited,
310
10.3.2
Bivariate Random Effects and Association Heterogeneity,
311
10.4
Multilevel (Hierarchical) Models,
313
10.4.1
Example: Two-Level Model for Student Advancement,
314
10.4.2
Example: Grade Retention,
315
10.5
Model Fitting and Inference for GLMMS,
316
10.5.1
Fitting GLMMs,
316
10.5.2
Inference for Model Parameters and Prediction,
317
Problems,
318
11.
A Historical Tour of Categorical Data Analysis
325
11.1
The Pearson-Yule Association Controversy,
325
11.2
R. A. Fisher's Contributions,
326
11.3
Logistic Regression,
328
11.4
Multiway Contingency Tables and
Loglinear
Models,
329
11.5
Final Comments,
331
Appendix A: Software for Categorical Data Analysis
332
Appendix B: Chi-Squared Distribution Values
343
Bibliography
344
Index of Examples
346
Subject Index
350
Brief Solutions to Some Odd-Numbered Problems
357 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Agresti, Alan 1947- |
| author_GND | (DE-588)141458062 |
| author_facet | Agresti, Alan 1947- |
| author_role | aut |
| author_sort | Agresti, Alan 1947- |
| author_variant | a a aa |
| building | Verbundindex |
| bvnumber | BV022469771 |
| callnumber-first | Q - Science |
| callnumber-label | QA278 |
| callnumber-raw | QA278 |
| callnumber-search | QA278 |
| callnumber-sort | QA 3278 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | CM 4000 MR 2100 QH 234 SK 800 SK 830 SK 840 |
| classification_tum | MAT 627f |
| ctrlnum | (OCoLC)71812851 (DE-599)BVBBV022469771 |
| dewey-full | 519.5/35 519.5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/35 519.5 |
| dewey-search | 519.5/35 519.5 |
| dewey-sort | 3519.5 235 |
| dewey-tens | 510 - Mathematics |
| discipline | Soziologie Psychologie Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Soziologie Psychologie Mathematik Wirtschaftswissenschaften |
| edition | 2. ed. |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02942nam a2200733zc 4500</leader><controlfield tag="001">BV022469771</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20110408 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">070619s2007 xxud||| |||| 00||| eng d</controlfield><datafield tag="010" ind1=" " ind2=" "><subfield code="a">2006042138</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">0471226181</subfield><subfield code="c">cloth</subfield><subfield code="9">0-471-22618-1</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780471226185</subfield><subfield code="9">978-0-471-22618-5</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)71812851</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV022469771</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">aacr</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">xxu</subfield><subfield code="c">US</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-898</subfield><subfield code="a">DE-19</subfield><subfield code="a">DE-91G</subfield><subfield code="a">DE-N2</subfield><subfield code="a">DE-945</subfield><subfield code="a">DE-355</subfield><subfield code="a">DE-473</subfield><subfield code="a">DE-703</subfield><subfield code="a">DE-11</subfield><subfield code="a">DE-824</subfield><subfield code="a">DE-188</subfield><subfield code="a">DE-91</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA278</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">519.5/35</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">519.5</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">CM 4000</subfield><subfield code="0">(DE-625)18951:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">MR 2100</subfield><subfield code="0">(DE-625)123488:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">QH 234</subfield><subfield code="0">(DE-625)141549:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SK 800</subfield><subfield code="0">(DE-625)143256:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SK 830</subfield><subfield code="0">(DE-625)143259:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SK 840</subfield><subfield code="0">(DE-625)143261:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">MAT 627f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Agresti, Alan</subfield><subfield code="d">1947-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)141458062</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">An introduction to categorical data analysis</subfield><subfield code="c">Alan Agresti</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">2. ed.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Hoboken, NJ</subfield><subfield code="b">Wiley-Interscience</subfield><subfield code="c">2007</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">XVII, 372 S.</subfield><subfield code="b">graph. Darst.</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Wiley series in probability and statistics</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Previous ed.: 1996</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and index</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Analyse multivariée</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Análisis multivariado</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Data-analyse</subfield><subfield code="2">gtt</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Kwalitatieve gegevens</subfield><subfield code="2">gtt</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Çok değişkenli analiz</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multivariate analysis</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Datenanalyse</subfield><subfield code="0">(DE-588)4123037-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Kontingenztafelanalyse</subfield><subfield code="0">(DE-588)4201359-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Qualitative Daten</subfield><subfield code="0">(DE-588)4176592-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Multivariate Analyse</subfield><subfield code="0">(DE-588)4040708-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Kategoriale Daten</subfield><subfield code="0">(DE-588)4327512-6</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Kategoriale Daten</subfield><subfield code="0">(DE-588)4327512-6</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Multivariate Analyse</subfield><subfield code="0">(DE-588)4040708-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="8">1\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="1" ind2="0"><subfield code="a">Qualitative Daten</subfield><subfield code="0">(DE-588)4176592-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2="1"><subfield code="a">Kontingenztafelanalyse</subfield><subfield code="0">(DE-588)4201359-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2=" "><subfield code="8">2\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="2" ind2="0"><subfield code="a">Datenanalyse</subfield><subfield code="0">(DE-588)4123037-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="2" ind2=" "><subfield code="8">3\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Bamberg</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015677281&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-015677281</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">2\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">3\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield></record></collection> |
| id | DE-604.BV022469771 |
| illustrated | Illustrated |
| index_date | 2024-07-02T17:44:06Z |
| indexdate | 2024-07-09T20:58:17Z |
| institution | BVB |
| isbn | 0471226181 9780471226185 |
| language | English |
| lccn | 2006042138 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015677281 |
| oclc_num | 71812851 |
| open_access_boolean | |
| owner | DE-898 DE-BY-UBR DE-19 DE-BY-UBM DE-91G DE-BY-TUM DE-N2 DE-945 DE-355 DE-BY-UBR DE-473 DE-BY-UBG DE-703 DE-11 DE-824 DE-188 DE-91 DE-BY-TUM |
| owner_facet | DE-898 DE-BY-UBR DE-19 DE-BY-UBM DE-91G DE-BY-TUM DE-N2 DE-945 DE-355 DE-BY-UBR DE-473 DE-BY-UBG DE-703 DE-11 DE-824 DE-188 DE-91 DE-BY-TUM |
| physical | XVII, 372 S. graph. Darst. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Wiley-Interscience |
| record_format | marc |
| series2 | Wiley series in probability and statistics |
| spelling | Agresti, Alan 1947- Verfasser (DE-588)141458062 aut An introduction to categorical data analysis Alan Agresti 2. ed. Hoboken, NJ Wiley-Interscience 2007 XVII, 372 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley series in probability and statistics Previous ed.: 1996 Includes bibliographical references and index Analyse multivariée Análisis multivariado Data-analyse gtt Kwalitatieve gegevens gtt Çok değişkenli analiz Multivariate analysis Datenanalyse (DE-588)4123037-1 gnd rswk-swf Kontingenztafelanalyse (DE-588)4201359-8 gnd rswk-swf Qualitative Daten (DE-588)4176592-8 gnd rswk-swf Multivariate Analyse (DE-588)4040708-1 gnd rswk-swf Kategoriale Daten (DE-588)4327512-6 gnd rswk-swf Kategoriale Daten (DE-588)4327512-6 s Multivariate Analyse (DE-588)4040708-1 s 1\p DE-604 Qualitative Daten (DE-588)4176592-8 s Kontingenztafelanalyse (DE-588)4201359-8 s 2\p DE-604 Datenanalyse (DE-588)4123037-1 s 3\p DE-604 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015677281&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Agresti, Alan 1947- An introduction to categorical data analysis Analyse multivariée Análisis multivariado Data-analyse gtt Kwalitatieve gegevens gtt Çok değişkenli analiz Multivariate analysis Datenanalyse (DE-588)4123037-1 gnd Kontingenztafelanalyse (DE-588)4201359-8 gnd Qualitative Daten (DE-588)4176592-8 gnd Multivariate Analyse (DE-588)4040708-1 gnd Kategoriale Daten (DE-588)4327512-6 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4201359-8 (DE-588)4176592-8 (DE-588)4040708-1 (DE-588)4327512-6 |
| title | An introduction to categorical data analysis |
| title_auth | An introduction to categorical data analysis |
| title_exact_search | An introduction to categorical data analysis |
| title_exact_search_txtP | An introduction to categorical data analysis |
| title_full | An introduction to categorical data analysis Alan Agresti |
| title_fullStr | An introduction to categorical data analysis Alan Agresti |
| title_full_unstemmed | An introduction to categorical data analysis Alan Agresti |
| title_short | An introduction to categorical data analysis |
| title_sort | an introduction to categorical data analysis |
| topic | Analyse multivariée Análisis multivariado Data-analyse gtt Kwalitatieve gegevens gtt Çok değişkenli analiz Multivariate analysis Datenanalyse (DE-588)4123037-1 gnd Kontingenztafelanalyse (DE-588)4201359-8 gnd Qualitative Daten (DE-588)4176592-8 gnd Multivariate Analyse (DE-588)4040708-1 gnd Kategoriale Daten (DE-588)4327512-6 gnd |
| topic_facet | Analyse multivariée Análisis multivariado Data-analyse Kwalitatieve gegevens Çok değişkenli analiz Multivariate analysis Datenanalyse Kontingenztafelanalyse Qualitative Daten Multivariate Analyse Kategoriale Daten |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015677281&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT agrestialan anintroductiontocategoricaldataanalysis |