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

Objective : Introduction some linear model theory that have been used for modern statistics

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

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