Providing a wide-ranging introduction to the use of linear models in analyzing data, this text presents a vector space and projections approach to the subject. The topics covered include ANOVA, estimation, hypothesis testing, multiple comparison, regression analysis, and experimental design. Also covered are: testing for lack of fit; models with singular covariance matrices; variance component estimation; best linear prediction; colinearity; and variable selection.
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This textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The authors emphasis is on providing a unified treatment of linear models, including analysis of variance models and regression models, based on projections, orthogonality, and other vector space ideas. Every chapter comes with numerous exercises and examples that make it ideal for a graduate- level course. All of the standard topics are covered in depth. In addition, the book covers topics that are not usually treated at this level, but which are important in their own right. The author, Ronald Christensen, is a Professor of Statistics at the University of New Mexico.
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