This course will cover repeated measures models. The main focus will be implementation of such models and interpretation of the output and briefly covering why we need repeated measures models. The experimental design principles appropriate for such models will not be covered in detail. In this context we will discuss what is a "random effect" and why it is called a "random effect". The primary focus will be for the researcher to understand when he should be thinking about a random effects model. The course will also talk briefly about what is a hierarchical model and why they are the obvious choice of modelers in some cases. The concepts will be explained almost wholly through examples in SAS or in R and using plots. It will be open for questions and discussions at the end. Feel free to ask questions specific to you research and enquire if your data will benefit from a hierarchical structure or a random effects model.