This book describes how generalized linear modelling procedures can be used for statistical modelling in many different fields, without becoming lost in problems of statistical inference. Many student, even in relatively advanced statistics courses, do not have an overview whereby they can see that the three areas, linear normal categorical, and survival models, have much in common. The author shows the unity of many of the commonly used models and provides the reader with a taste of many different areas, such as survival models, time series, and spatial analysis, and of their unity. This book should appeal to applied statisticians and to scientists having a basic grounding in modern statistics. With the many exercises at the end of the chapters, it should constitute an excellent text for teaching applied statistics students and non- statistics majors the fundamental uses of statistical modelling. The reader is assumed to have knowledge of basic statistical princi! ples, whether from a Bayesian, frequentist, or direct likelihood point of view, being familiar at least with the analysis of the simpler normal linear models, regression and ANOVA.

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