Regression I: Linear Regression and Modeling

Catalog description

Regression models are one of the most important statistical techniques used in public health. This course focuses on data analysis that use linear regression models for continuous outcomes. The first part of this course introduces simple and multiple linear regressions, principles of ordinary least square regression models, model assumptions, and inferences about model parameters. The second part of the course focus on important practical matters, such as prediction, variable selection, moderated effects, and mediation. These two parts together provide the foundations for more advanced statistics modeling. Examples are drawn from broad areas of public health research. All the analyses will be taught and performed using Stata and/or R statistical software.