Department of Statistics

Recent Instructors Monahan, John	ST 552	Linear Models and Variance Components
	Course Description Theory of estimation and testing in full and non-full rank linear models. Normal theory distributional properties. Least squares principle and the Gauss-Markoff theorem. Estimability and properties of best linear unbiased estimators. General linear hypothesis. Application of dummy variable methods to elementary classification models for balanced and unbalanced data. Analysis of covariance. Variance components estimation for balanced data. Course Syllabus Examples of the gneral linear model Review of Linear Algebra Generalized Inverses and Solving Equations Projections and Solutions to Normal Eqns Linear Least Squares Estimability Gauss-Markov Theorem Generalized Least Squares Multivariate normal distribution Central and non-,X2 and F Distribution of quadratic forms General Linear Hypothesis Simultaneous confidence intervals Random effects vs fixed Distributional results for variance components Asymptotics Course Prerequisites MA405 and ST521 Course Corequisites ST522 Recent Textbooks A Primer on Linear Models by John Monahan