This new edition of the successful multi-disciplinary text Statistical Modelling in GLIM takes into account new developments in both statistical software and statistical modeling. Including three new chapters on mixture and random effects models, it provides a comprehensive treatment of the theory of statistical modeling with generalized linear models with an emphasis on applications to practical problems and an expanded discussion of statistical theory. A wide range of case studies is also provided, using the normal, binomial Poisson, multinominal, gamma, exponential and Weibull distributions. This book is ideal for graduates and research students in applied statistics and a wide range of quantitative disciplines, including biology, medicine, and the social sciences. Professional statisticians at all levels will also find it an invaluable desktop companion.

About the Author

Murray Aitkin is at School of Mathematics and Statistics, University of Newcastle. Brian Francis is at Assistant Director, Centre for Applied Statistics, Lancaster University. John Hinde is at the National University of Ireland.

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