Categorical data analysis is a special area of generalized 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 that 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,...

This text is aimed at students in medicine, biology and the social sciences as well as those planning to specialize in applied statistics. It covers the basics of the design and analysis of surveys and experiments and provides an understanding of the basic principles of modeling and inference. Practical advice is provided on how to design a study, collect data, record observations accurately, detect errors, construct appropriate models, and interpret the results. The text contains many illustrative examples and exercises relating statistical principles to research. A companion web site is...

This introduction to ways of modelling a wide variety of phenomena that occur over time is accessible to anyone with a basic knowledge of statistical ideas. J.K. Lindsey concentrates on tractable models involving simple processes for which explicit probability models, hence likelihood functions, can be specified. (These models are the most useful in statistical applications modelling empirical data.) Examples are drawn from physical, biological and social sciences, to show how the book's underlying ideas can be applied, and data sets and R code are supplied for them. Author resource page:...

This introduction to ways of modelling a wide variety of phenomena that occur over time is accessible to anyone with a basic knowledge of statistical ideas. J.K. Lindsey concentrates on tractable models involving simple processes for which explicit probability models, hence likelihood functions, can be specified. (These models are the most useful in statistical applications modelling empirical data.) Examples are drawn from physical, biological and social sciences, to show how the book's underlying ideas can be applied, and data sets and R code are supplied for them. Author resource page:...