This course will introduce non-parametric smoothing methods, such as splines, locally weighted polynomial regression (LOESS) and generalized additive models (GAM), and focus on continuous environmental exposure variables.  It will also deal with analysis of multi-level data including analyses of longitudinal data and complex sampling data, and time-series analysis that are widely used in environmental epidemiology.  The course will cover how to handle limits of detection in environmental exposure data.  It will provide an opportunity to analyze actual population data to learn how to model environmental epidemiological data, and is designed particularly for students who pursue environmental epidemiologic research.  The course will consist of lectures and hands-on practices in computer labs, homework assignments and final projects.  R, a free software environment for statistical computing and graphics, will be used.