Theory of Linear Models

Course Description

This is a core course in the biostatistics PhD program. It is designed to provide students with a detailed knowledge and rigorous understanding of statistical theory that underlies linear models analysis. A unified framework is presented for understanding statistical methods that students learn in the applied core courses of the Biostatistics master¿s program; including simple and multiple linear regression, analysis of variance, analysis of covariance, and repeated measures and longitudinal data analysis. The purpose of the course is to provide students with a deep and unified understanding of these methods, and their proper uses, by demonstrating that they fit into the general linear model framework and by presenting theoretical underpinnings that are common to all. Students will learn that a wide range of statistical problems can be formulated in a general way through the specification of linear models, and that these problems can be solved by applying the theories of parameter estimation and general linear hypothesis testing learned in this course. The practical value of this theoretical knowledge is communicated through discussions of applied problems. As a preliminary to learning the theory of linear models students are taught prerequisite topics in basic and advanced matrix algebra and multivariate normal distribution theory.

Code

STA 641

Credits

3

Subject

Biostatistics

Programs That Include This Course

• Doctoral Doctor of Philosophy in Biostatistics