Objective : Introduce some linear model theory that have been used for modern statisticians Contents: 1. Matrix algebra 2. Random Vectors and Matrices 3. Multivariate Normal Distribution 4. Distribution of Quadratic Forms in y 5. Simple Linear Regression 6. Multiple Regression: Estimation 7. Multiple Regression: Tests of Hypotheses and Confidence Intervals 8. Multiple Regression: Model Validation and Diagnostics 9. Multiple Regression: Random x’s 10.Multiple Regression: Bayesian Inference 11.Analysis-of-Variance Models 12.One-Way Analysis-of-Variance: Balanced Case 13.Two-Way Analysis-of-Variance: Balanced Case 14.Analysis-of-Variance: The Cell Means Model for Unbalanced Data 15.Analysis-of-Covariance 16.Linear Mixed Models

a. Linear Models in Statistics 2nd by Rencher, A.C. and Schaalje, G. B.
b. Plane Answers to Complex Questions: The Theory of Linear Models by Ronald Christensen