I instructed students on fitting and interpreting regression models used to analyze continuous, binary, and time-to-event data, often observed in the context of outcomes in clinical trials and other health sciences studies. Linear, logistic, and proportional hazards regression models were covered at an introductory level. In the process, students learned the assumptions required to make valid inferences on model parameters, and techniques to diagnose potential violations of these assumptions. Furthermore, students learned how to fit these models and create diagnostic plots in R. In the final project, students gained experience formulating their own scientific questions from a dataset, applying the appropriate model, and conducting hypothesis tests and other appropriate statistical analyses to answer their scientific questions.