Do you have data that is not normally distributed and don't know how to analyze it using generalized linear models (GLM)? Beginning with a discussion of fundamental statistical modeling concepts in a multiple regression framework, the authors of An Introduction to Generalized Linear Models, extend these concepts to GLM and demonstrate the similarity of various regression models to GLM. Each procedure is illustrated using real life data sets. The book provides an accessible but thorough introduction to GLM, exponential family distribution, and maximum likelihood estimation; includes discussion on checking model adequacy and description on how to use SAS to fit GLM; and describes the connection between survival analysis and GLM. It is an ideal text for social science researchers who do not have a strong statistical background, but would like to learn more advanced techniques having taken an introductory course covering regression analysis.

Fundamentals of Generalized Linear Modeling

Chapter 3 discussed a generalized linear model with which we are all familiar, the classical multiple regression model. This chapter indicates how this model can be generalized to other situations in which the dependent variable is discrete, nonnormally distributed, and its variance depends on its mean.

Generalized linear models involve predicting the conditional mean or some ...

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