Foundations of Biostatistics II: Statistical Regression

Graduate

We cover the theory and applications of statistical regression, as practiced in biomedical research . We present statistical inference (estimation and hypothesis testing) for linear models, generalized linear models ( e.g. logistic and Poisson regression), longitudinal models for repeated measurements, and models for times-to-event (survival analysis). The course emphasizes the primary goals of regression, which are (i) prediction and (ii) causal inference. It also introduces penalized regression for large numbers of predictors and methods for missing data in regression. T he statistical software R is used for applications. (1 unit)