Modelling Frequency and Count Data

Categorical data analysis is a special area of generalised linear models, which has become the most important area of statistical applications in many disciplines, from medicine to social sciences. This text presents the standard models and many newly developed ones in a language which can be immediately applied in many modern statistical packages such as GLIM, GENSTAT, S-Plus, as well as SAS and LISP-STAT. The book is structure around the distinction between independent events occurring to different individuals, resulting in frequencies, and repeated events occurring to the same individuals, yielding counts. The book demonstates that much of modern statistics can be seen as special cases of categorical data models; both generalized linear models and proportional hazards models can be fitted as log linear models. More specialized topics such as Markov chains, overdispersion and random effects, are also covered.

"Modelling frequency and count data is an excellent text for a master's level course in applied statistics or as a reference for any applied statistician. An introductory statistics course and some knowledge of log-linear and logistic regression models is [sic] assumed. Theoretical details are kept
to a minimum but underlying concepts are clearly demonstrated. This text provides a brief, but broad, introduction to a wide variety of models. Every model is introduced by a real data example . . . and followed through from beginning to end in terms of both analysis and model evaluation; an
attraction for both students and applied statisticians working on real world problems. . . . In summary, I would recommend this text strongly to any statistician doing applied work as well as a text for a course in categorical data analysis. It is both a comprehensive and practical survey of models
for frequency and count data."--Statistical Methods in Medical Research
"Modelling frequency and count data is an excellent text for a master's level course in applied statistics or as a reference for any applied statistician. An introductory statistics course and some knowledge of log-linear and logistic regression models is [sic] assumed. Theoretical details are kept
to a minimum but underlying concepts are clearly demonstrated. This text provides a brief, but broad, introduction to a wide variety of models. Every model is introduced by a real data example . . . and followed through from beginning to end in terms of both analysis and model evaluation; an
attraction for both students and applied statisticians working on real world problems. . . . In summary, I would recommend this text strongly to any statistician doing applied work as well as a text for a course in categorical data analysis. It is both a comprehensive and practical survey of models
for frequency and count data."--Statistical Methods in Medical Research
"Modelling frequency and count data is an excellent text for a master's level course in applied statistics or as a reference for any applied statistician. An introductory statistics course and some knowledge of log-linear and logistic regression models is [sic] assumed. Theoretical details are kept to a minimum but underlying concepts are clearly demonstrated. This text provides a brief, but broad, introduction to a wide variety of models. Every model is introduced by a real data example . . . and followed through from beginning to end in terms of both analysis and model evaluation; an attraction for both students and applied statisticians working on real world problems. . . . In summary, I would recommend this text strongly to any statistician doing applied work as well as a text for a course in categorical data analysis. It is both a comprehensive and practical survey of models for frequency and count data."--Statistical Methods in Medical Research
"Modelling frequency and count data is an excellent text for a master's level course in applied statistics or as a reference for any applied statistician. An introductory statistics course and some knowledge of log-linear and logistic regression models is [sic] assumed. Theoretical details are kept to a minimum but underlying concepts are clearly demonstrated. This text provides a brief, but broad, introduction to a wide variety of models. Every model is introduced by a real data example . . . and followed through from beginning to end in terms of both analysis and model evaluation; an attraction for both students and applied statisticians working on real world problems. . . . In summary, I would recommend this text strongly to any statistician doing applied work as well as a text for a course in categorical data analysis. It is both a comprehensive and practical survey of models for frequency and count data."--Statistical Methods in Medical Research