EPID743: Applied Linear Regression
- Graduate level
- Summer term(s) for residential students;
- 1 credit hour(s) for residential students;
- Instructor(s): Staff (Residential);
- Last offered Summer 2016
- Prerequisites: Intro Epidemiology and Biostatistics and Perm. Instr
- Description: This course is an introduction to the most powerful analysis technique in statistics: linear regression. This course discusses the applications of linear regression models to medical research and public health data. We will focus on the two major goals of linear models: (1) Explanation: the estimation of associations, and (2) Prediction: the use of models to predict subject outcomes, as with diagnostic tests. Specific topics include graphical exploratory data analysis, assumptions behind simple and multiple linear regression, use of categorical explanatory variables, identification of appropriate transformations of explanatory and/or outcome variables, assessment of predictor/outcome associations through hypothesis testing, identification of confounding and effect modification, assessment of model fit, and model selection techniques.