Short description Linear Models in Statistics, Second 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. It brings together a number of approaches to regression and analysis of variance from which more experienced practitioners will also benefit. With an emphasis on broad coverage of essential topics, this book clearly and carefully develops the basic theory of regression and analysis of variance, illustrating it with examples from a wide range of disciplines.
From the contents Preface.
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.
