Statistics 305A: Applied Statistics (Linear Models and More)
PhD course, Stanford University, Department of Statistics
Autumn 2022
Course description
This course is about the linear model, and it is mainly a course about applied statistics, using the linear model to illustrate important concepts. The rough structure will be as follows: together, we work through linear models in increasing order of complexity, and we will develop concomitant statistical ideas along the way to understand how to use the models and solve problems with them. Important for us will be both the positive aspects–that is, how to solve problems–and the negative, that is, when models may fail and what we should be careful of.
In regression we work primarily with real-valued responses. The main tool for regression is the linear model, in all its glory. This ranges from humble one-sample T tests to more elaborate methods–that we will see are perhaps not so so elaborate–like splines and wavelets. The focus of the course is the problems more than the tools, as the computational aspects are essentially solved; the challenge in statistics is to connect methods to problems correctly.