Categorical Data Analysis, Third Edition delivers an in-depth and unified treatment of statistical methods for analyzing categorical data. This comprehensive text covers both univariate and correlated multivariate responses, seamlessly connecting techniques such as logistic regression with Poisson and negative binomial loglinear models, while drawing parallels to methods used in normal regression. The book places special emphasis on logistic and probit regression approaches for binary, ordinal, and nominal responses in both independent and clustered data settings. New chapters introduce alternative techniques for binary response data - ranging from smoothing and regularization methods to classification via linear discriminant analysis, classification trees, and cluster analysis - while additional sections discuss Bayesian approaches. With over 100 data analyses, more than 600 exercises, and detailed chapter notes referencing a vast contemporary bibliography, this edition stands as an essential resource for statisticians, biostatisticians, and researchers across the biomedical, social, behavioral, and industrial quality control fields.
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