Scientific Methods for Health Sciences: Linear Models

Course Number: HS 852
Credit(s): 4
Prerequisite(s): HS 851 or permission of instructor

This course introduces commonly used linear, generalized linear, and linear mixed models to graduate students who need to understand research reports/scientific papers, analyze empirical data, or interpret their results. The topics covered by this course include SAS tutorial, SAS Graphics, simple linear regression, multiple linear regression, analysis of variance (ANOVA), analysis of covariance (ANCOVA), nonparametric regression, logistic regression, multinominal logistic regression, Poisson regression, generalized linear model, generalized estimating equations (GEE), and linear mixed models. The emphasis of the curriculum is on the practical aspect of the statistical methods with the mathematical models and computation introduced at a minimal technical level. Students will learn to be users of these statistical methods through real data examples, hands on experiences, and critique of scientific papers published in their subject fields. HS852 is a 4 credit hour course (3 lectures + 1 lab/discussion).