disciplines, from medicine to social science. Written for advanced undergraduates, but also useful for statisticians and research workers, this text presents the standard models as well as many newly developed ones in a language which can be applied in many modern statistical packages such as GLIM, GENMSTAT, S-Plus, and SAS and LISP-STAT. Structured around the distinction between independent events occurring to different individuals and repeated events occurring to the same individuals, the book demonstrates that much of modern statistics can be seen as special cases of categorical data models. Other topics covered include Markov chains, overdispersion, and random effects. Introductory Statistics offers a radical new approach to teaching the subject. Statistics, the author observes, is the study of variability, both systematic and random. Random variability is most easily described by a histogram; systematic variability is described by changes in the shape of histograms. A natural way to study these two types of variability is through log linear and logistic models. Once these basic principles of statistical modelling have been grasped, the problem of inference can be introduced through the likelihood function. Common approaches to calibrating the likelihood functions through significance tests and Bayes theorem are briefly presented. The reader is then introduced to more sophisticated statistical models, including parametric distributions, which are classified in three groups: discrete, normal and duration distributions. With this groundwork, linear regression and ANOVA models are introduced as special cases that describe how histograms change. The text concludes with selected topics in dependent data. Accessible to non-mathematicians, the book assumes little prior knowledge, although later chapters deal with fairly advanced models. It includes many practical examples and will serve as an excellent first introduction for both undergraduate and graduate students.

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