Regression Analysis

3 credits · Graduate credit

The course covers concepts and methods for regression analysis and applications. It covers both linear and generalized linear regression models. Topics for linear models include estimation, inference, interpretation of results, diagnostics, robust procedures, weighting and transformations, and model selection; more advanced techniques (such as splines, principal components analysis, and shrinkage estimators including ridge regression and Lasso) will also be introduced. Topics for generalized linear models include logistic regression and Poisson regression. While there will be some theory, the emphasis will be on applications and data analysis.