Description
Book SynopsisThis new edition discusses classical linear models from a matrix algebra perspective, making the subject easily accessible to readers encountering linear models for the first time. It provides a solid foundation from which to explore the literature and interpret correctly the output of computer packages and brings together a number of approaches.
Trade Review"This indeed clearly written book will do great service for advanced undergraduate and also for PhD students." (
International Statistical Review, December 2008)
"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)
Table of ContentsPreface.
1. Introduction.
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.
16. Analysis-of-Covariance.
17. Linear Mixed Models.
18. Additional Models.
Appendix A. Answers and Hits to the Problems.
References.
Index.
