Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected value of an outcome, dependent, or response variable. It offers a sound introduction to general and generalized linear models using the popular and powerful likelihood techniques. The authors enable a clear comparison between general and generalized linear models and cover Gaussian-based hierarchical models and hierarchical generalized linear models. They illustrate the methods with many real-world examples and use R throughout to solve the problems. Ancillaries are available on the book's website.
