2. Matrix Algebra.
3. Random Vectors and Matrices.
4. Multivariate Normal Distribution.
5. Distribution of Quadratic Forms in y.
6. Simple Linear Regression.
7. Multiple Regression: Estimation.
8. Multiple Regression: tests of Hypotheses and Confidence Intervals.
9. Multiple Regression: Model Validation and Diagnostics.
10. Multiple Regression: random x's.
11. Multiple Regression: Bayesian Inference.
12. Analysis-of-Variance Models.
13. One-Way Analysis-of-Variance: balanced Case.
14. Two-Way Analysis-of Variance: Balanced Case.
15. Analysis-of-Variance: The Cell Means Model for Unbalanced Data.
17. Linear Mixed Models.
18. Additional Models.
Appendix A. Answers and Hits to the Problems.
- The book contains easy-to-read proofs and clear explanations of concepts and procedures. Extensive class testing in the first edition has flushed-out errors, inconsistencies, and extraneous subject matter.
- Advanced topics such as mixed and generalized linear models, Bayesian linear models, geometry of least squares, and logistic and nonlinear regression are included in order to show the breadth and depth of the subject matter.
"This indeed clearly written book will do great service for advanced undergraduate and also for PhD students." (International Statistical Review, Dec 2008)
"This well-written book represents various topics on linear models with great clarity in an easy-to-understand style." (CHOICE, Aug 2008)
- Special topics such as multiple regression with random x's and the effect of each variable on R(2) are included.
- Real data sets in most examples are showcased throughout the book and available as downloadable files at an .ftp site.
- Numerous theoretical and applied problems are incorporated, with selected answers placed in an appendix, to challenge the brightest of readers.
- A thorough review of the requisite matrix algebra is added in an appendix for transitional purposes.
- Graphs, charts, tables, SAS output, as well as extensive references, are all provided as pedagogical tools to better understand the material.