Categorical Data Analysis, (Wiley Series in Probability and Mathematical Statistics, Applied Probability and Statistics)

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9780471853015: Categorical Data Analysis, (Wiley Series in Probability and Mathematical Statistics, Applied Probability and Statistics)

Categorical Data Analysis describes the most important methods, offering a unified presentation of modeling using generalized linear models and emphasizing loglinear and logit modeling techniques. Contributions of noted statisticians (Pearson, Yule, Fisher, Neyman, Cochran), whose pioneering efforts set the pace for the evolution of modern methods, are examined as well.

Special features of the book include:

  • Coverage of methods for repeated measurement data, which have become increasingly important in biomedical applications
  • Prescriptions for how ordinal variables should be treated differently than nominal variables
  • Derivations of basic asymptotic and fixed-sample-size inferential methods
  • Discussion of exact small sample procedures
  • More than 40 examples of analyses of "real" data sets, including: aspirin use and heart disease; job satisfaction and income; seat belt use and injuries in auto accidents; and predicting outcomes of baseball games
  • More than 400 exercises to facilitate interpretation and application of methods

Categorical Data Analysis also contains an appendix that describes the use of computer software currently available for performing the analyses presented in the book. A comprehensive bibliography and notes and the end of each chapter round out the work, making it a complete, invaluable reference for statisticians, biostatisticians and professional researchers.

"synopsis" may belong to another edition of this title.

From the Publisher:

Summarizes methods used for the analysis of categorical data, including many recently developed techniques. The emphasis is on loglinear and logit modeling techniques, which share many features with linear model methods for continuous variables. Incorporated into the exposition is interesting historical information (and controversies) on the development of categorical data analysis. Chapters 1-7 cover bivariate categorical data and loglinear and logit model building; chapters 8-11 discuss applications and methods; chapters 12 and 13 address theoretical foundations.

From the Back Cover:

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

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