Alvin C. Rencher, PhD, is Professor of Statistics at Brigham Young University. Dr. Rencher is a Fellow of the American Statistical Association and the author of Methods of Multivariate Analysis and Multivariate Statistical Inference and Applications, both published by Wiley.G. Bruce Schaalje, PhD, is Professor of Statistics at Brigham Young University. He has authored over 120 journal articles in his areas of research interest, which include mixed linear models, small sample inference, and design of experiments.

"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)

• 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.16. Analysis-of-Covariance.17. Linear Mixed Models.18. Additional Models.Appendix A. Answers and Hits to the Problems.References.Index.

Alvin C. Rencher, PhD, is Professor of Statistics at Brigham Young University. Dr. Rencher is a Fellow of the American Statistical Association and the author of Methods of Multivariate Analysis and Multivariate Statistical Inference and Applications, both published by Wiley.G. Bruce Schaalje, PhD, is Professor of Statistics at Brigham Young University. He has authored over 120 journal articles in his areas of research interest, which include mixed linear models, small sample inference, and design of experiments.

"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)

• 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.16. Analysis-of-Covariance.17. Linear Mixed Models.18. Additional Models.Appendix A. Answers and Hits to the Problems.References.Index.