Buch, Englisch, 400 Seiten, Format (B × H): 164 mm x 239 mm, Gewicht: 628 g
Buch, Englisch, 400 Seiten, Format (B × H): 164 mm x 239 mm, Gewicht: 628 g
Reihe: Wiley Series in Probability and Statistics
ISBN: 978-0-471-22618-5
Verlag: Wiley
Praise for the First Edition
"This is a superb text from which to teach categorical data analysis, at a variety of levels. [t]his book can be very highly recommended."
--Short Book Reviews
"Of great interest to potential readers is the variety of fields that are represented in the examples: health care, financial, government, product marketing, and sports, to name a few."
--Journal of Quality Technology
"Alan Agresti has written another brilliant account of the analysis of categorical data."
--The Statistician
The use of statistical methods for categorical data is ever increasing in today's world. An Introduction to Categorical Data Analysis, Second Edition provides an applied introduction to the most important methods for analyzing categorical data. This new edition summarizes methods that have long played a prominent role in data analysis, such as chi-squared tests, and also places special emphasis on logistic regression and other modeling techniques for univariate and correlated multivariate categorical responses.
This Second Edition features:
* Two new chapters on the methods for clustered data, with an emphasis on generalized estimating equations (GEE) and random effects models
* A unified perspective based on generalized linear models
* An emphasis on logistic regression modeling
* An appendix that demonstrates the use of SAS(r) for all methods
* An entertaining historical perspective on the development of the methods
* Specialized methods for ordinal data, small samples, multicategory data, and matched pairs
* More than 100 analyses of real data sets and nearly 300 exercises
Written in an applied, nontechnical style, the book illustrates methods using a wide variety of real data, including medical clinical trials, drug use by teenagers, basketball shooting, horseshoe crab mating, environmental opinions, correlates of happiness, and much more.
An Introduction to Categorical Data Analysis, Second Edition is an invaluable tool for social, behavioral, and biomedical scientists, as well as researchers in public health, marketing, education, biological and agricultural sciences, and industrial quality control.
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
- Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
Weitere Infos & Material
Preface to the Second Edition.
1. Introduction.
1.1 Categorical Response Data.
1.2 Probability Distributions for Categorical Data.
1.3 Statistical Inference for a Proportion.
1.4 More on Statistical Inference for Discrete Data.
Problems.
2. Contingency Tables.
2.1 Probability Structure for Contingency Tables.
2.2 Comparing Proportions in Two-by-Two Tables.
2.3 The Odds Ratio.
2.4 Chi-Squared Tests of Independence.
2.5 Testing Independence for Ordinal Data.
2.6 Exact Inference for Small Samples.
2.7 Association in Three-Way Tables.
Problems.
3. Generalized Linear Models.
3.1 Components of a Generalized Linear Model.
3.2 Generalized Linear Models for Binary Data.
3.3 Generalized Linear Models for Count Data.
3.4 Statistical Inference and Model Checking.
3.5 Fitting Generalized Linear Models.
Problems.
4. Logistic Regression.
4.1 Interpreting the Logistic Regression Model.
4.2 Inference for Logistic Regression.
4.3 Logistic Regression with Categorical Predictors.
4.4 Multiple Logistic Regression.
4.5 Summarizing Effects in Logistic Regression.
Problems.
5. Building and Applying Logistic Regression Models.
5.1 Strategies in Model Selection.
5.2 Model Checking.
5.3 Effects of Sparse Data.
5.4 Conditional Logistic Regression and Exact Inference.
5.5 Sample Size and Power for Logistic Regression.
Problems.
6. Multicategory Logit Models.
6.1 Logit Models for Nominal Responses.
6.2 Cumulative Logit Models for Ordinal Responses.
6.3 Paired-Category Ordinal Logits.
6.4 Tests of Conditional Independence.
Problems.
7. Loglinear Models for Contingency Tables.
7.1 Loglinear Models for Two-Way and Three-Way Tables.
7.2 Inference for Loglinear Models.
7.3 The Loglinear-Logistic Connection.
7.4 Independence Graphs and Collapsibility.
7.5 Modeling Ordinal Associations.
Problems.
8. Models for Matched Pairs.
8.1 Comparing Dependent Proportions.
8.2 Logistic Regression for Matched Pairs.
8.3 Comparing Margins of Square Contingency Tables.
8.4 Symmetry and Quasi-Symmetry Models for Square Tables.
8.5 Analyzing Rater Agreement.
8.6 Bradley-Terry Model for Paired Preferences.
Problems.
9. Modeling Correlated, Clustered Responses.
9.1 Marginal Models Versus Conditional Models.
9.2 Marginal Modeling: The GEE Approach.
9.3 Extending GEE: Multinomial Responses.
9.4 Transitional Modeling, Given the Past.
Problems.
10. Random Effects: Generalized Linear Mixed Models.
10.1 Random Effects Modeling of Clustered Categorical Data.
10.2 Examples of Random Effects Models for Binary Data.
10.3 Extensions to Multinomial Responses or Multiple Random Effect Terms.
10.4 Multilevel (Hierarchical) Models.
10.5 Model Fitting and Inference for GLMMS.
Problems.
11. A Historical Tour of Categorical Data Analysis.
11.1 The Pearson-Yule Association Controversy.
11.2 R. A. Fisher's Contributions.
11.3 Logistic Regression.
11.4 Multiway Contingency Tables and Loglinear Models.
11.5 Final Comments.
Appendix A: Software for Categorical Data Analysis.
Appendix B: Chi-Squared Distribution Values.
Bibliography.
Index of Examples.
Subject Index.
Brief Solutions to Some Odd-Numbered Problems.
