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Abstract:
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1. Introduction -- 2. Projection Matrices and Vector Space Theory -- 3. Least Squares Theory -- 4. Distribution Theory -- 5. Helmert Matrices and Orthogonal Relationships -- 6. Further Discussions of ANOVA -- 7. Residual Analysis: Diagnostics and Robustness -- 8. Models That Include Variance Components -- 9. Likelihood Approaches -- 10. Uncorrelated Residuals Formed from the Linear Model -- 11. Further inferential questions relating to ANOVA.
"Linear Models explores the theory of linear models and the dynamic relationships that these models have with Analysis of Variance (ANOVA), experimental design, and random and mixed-model effects. This one-of-a-kind book emphasizes an approach that clearly explains the distribution theory of linear models and experimental design starting from basic mathematical concepts in linear algebra. This book is a valuable book for courses on linear models at the upper-undergraduate and graduate levels. It is also a
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