Intended for a first course in linear models at either the upper undergraduate or beginning graduate level, "Introduction to Linear Models and Statistical Inference" provides a basic introduction to probability distribution theory and statistical inference. It includes descriptive methods for building models with an emphasis on linear regression, variance, and covariance. In an effort to extend reader comprehension and intrigue, there is a general discussion of analysis of model fit and modern robust techniques at the end of the book. The exercises are a mix of both the theoretical and the practical; some are marked as requiring calculus, linear algebra, or computer skills. The text utilizes output from MINITAB to illustrate many of the examples. An appendix introduces the reader to MINITAB. The text includes an introduction to matrix algebra in an appendix for those readers who have a weak background in the topic. Optional sections are included at chapter ends for use in courses where the integration of linear algebra techniques is desired. The sections can be omitted without loss of continuity. The text can serve as a first course in general statistics for students with some mathematical background at the first-year graduate level or as a second course for those readers pursuing a more quantitative emphasis in the social or natural sciences at the undergraduate level. Actual data from readily available (interdisciplinary) sources is used both in - text and on an author - maintained web site. Both intuitive and mathematical explanations are given in an effort to balance the overall treatment and comprehension.
