This course will cover the theoretical underpinnings, application and in-depth study of some of the most frequently used statistical computing methods such as generalized linear models, random effects or variance components models, multilevel/mixed models, and Bayesian methods in the area of applied research. Lifetime data models and semi-parametric models will also be covered. Modern methods of estimation will be discussed and links will be made to broader classes of statistical models. Students will learn simulation, bootstrap procedures and cross-validation of regression models. Students will also learn the fundamentals and key concepts of estimation methods, model building, model fitting, and model interpretation.