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Categorical Data Analysis, 2nd Edition

Categorical Data Analysis, 2nd Edition

Alan Agresti

ISBN: 978-0-471-45876-0

Mar 2003

734 pages

Select type: E-Book

$136.99

Description

Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Categorical Data Analysis was among those chosen.

A valuable new edition of a standard reference

"A 'must-have' book for anyone expecting to do research and/or applications in categorical data analysis."
Statistics in Medicine on Categorical Data Analysis, First Edition

The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. Responding to new developments in the field as well as to the needs of a new generation of professionals and students, this new edition of the classic Categorical Data Analysis offers a comprehensive introduction to the most important methods for categorical data analysis.

Designed for statisticians and biostatisticians as well as scientists and graduate students practicing statistics, Categorical Data Analysis, Second Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial regression for discrete data with normal regression for continuous data. Adding to the value in the new edition is coverage of:

  • Three new chapters on methods for repeated measurement and other forms of clustered categorical data, including marginal models and associated generalized estimating equations (GEE) methods, and mixed models with random effects
  • Stronger emphasis on logistic regression modeling of binary and multicategory data
  • An appendix showing the use of SAS for conducting nearly all analyses in the book
  • Prescriptions for how ordinal variables should be treated differently than nominal variables
  • Discussion of exact small-sample procedures
  • More than 100 analyses of real data sets to illustrate application of the methods, and more than 600 exercises
  • An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
Preface.

1. Introduction: Distributions and Inference for Categorical Data.

2. Describing Contingency Tables.

3. Inference for Contingency Tables.

4. Introduction to Generalized Linear Models.

5. Logistic Regression.

6. Building and Applying Logistic Regression Models.

7. Logit Models for Multinomial Responses.

8. Loglinear Models for Contingency Tables.

9. Building and Extending Loglinear/Logit Models.

10. Models for Matched Pairs.

11. Analyzing Repeated Categorical Response Data.

12. Random Effects: Generalized Linear Mixed Models for Categorical Responses.

13. Other Mixture Models for Categorical Data*.

14. Asymptotic Theory for Parametric Models.

15. Alternative Estimation Theory for Parametric Models.

16. Historical Tour of Categorical Data Analysis*.

Appendix A. Using Computer Software to Analyze Categorical Data.

Appendix B. Chi-Squared Distribution Values.

References.

Examples Index.

Author Index.

Subject Index.
  • Stronger emphasis on logistic regression modeling of binary and multicategory data
  • A unified generalized linear models approach connecting logistic regression and Poisson and negative binomial regression for discrete data with normal regression for continual data
  • Three new chapters on methods for repeated measurement and other forms of clustered categorical data, including marginal models and associated generalized estimating equations (GEE) methods, and mixed models with random effects
  • An appendix showing the use of SAS for conducting nearly all analyses in the book
  • More than 100 analyses of real data sets to illustrate application of the methods
  • More than 600 exercises
“…the essential reference text for statisticians…comprehensive and readable…” (Statistical Methods in Medical Research, Vol. 14, 2005)

"...careful, thorough, up-to-date volume...the new content and emphases in the second edition are sufficient to justify its purchase even by someone who already owns the first edition." (Journal of the American Statistical Association, June 2004)

"I liked this revised edition and recommend it highly to statisticians and graduate students." (Journal of Statistical Computation & Simulation, March 2004)

"...a highly satisfactory text on methods for categorical response variables...more complete and technical [than the First Edition]..." (IIE Transactions on Quality and Reliability Engineering)

"If you own the 1E, you absolutely need to upgrade to the 2E. If you do any analysis of categorical data, this is an essential desktop reference..." (Technometrics, Vol. 45, No. 1, February 2003)

"...this classic book is substantially modified and expanded..." (International Journal of General Systems, Vol. 32, 2003)

"...it is a total delight reading this book, which should be considered as the current standard textbook for teaching analysis of categorical data." (Pharmaceutical Research, Vol. 20, No. 6, June 2003)

"...written in a highly scientific but vivid style, intelligible for all researchers in that field...simply expressed, grand..." (Zentralblatt Math, 2004)

  • Coverage of methods for repeated measurement data
  • Prescriptions for how ordinal variables should b treated differently than nominal variables
  • Derivations of basic asymptotic and fixed-sample-size inferential methods
  • Discussion of exact small sample procedures
  • Stronger empahsis on logistic regression modeling of binary and multicategory data
  • A unified generalized linear models approach connecting logistic regression and Poisson and negative binomial regression for discrete data with normal regression for continuous data
  • Three new chapters on methods for repeated measurement and other forms of clustered categorical data, including marginal models and associated generalized estimating equations (GEE) methods, and mixed models with random effects
  • An appendix showing the use of SAS for conducting nearly all analyses in the book
  • More than 100 analyses of real data sets to illustrate applictaion of the methods, and more than 600 exercises