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Since the linear model forms the groundwork for most applied statistics, a course on the theory of the linear model is often required in most graduate statistics programs. A Primer on Linear Models presents a concise yet complete foundation for understanding basic linear models. Designed for a one-semester graduate course, this textbook begins with a practical discussion of basic algebra and geometry concepts as they apply to the linear model. The book then proceeds to an in-depth treatment of more advanced topics such as the Gauss-Markov model. The text also includes exercises of various levels of difficulty and features the constant use of non full-rank design matrices.