ABSTRACT
This chapter describes the essential topics in such an introductory course, advanced topics that could go into a second or third course in statistics, and a few tips regarding best practices for teaching such a course. A standard introduction to statistics includes exploratory data analysis, visualizations, an overview of common statistical models, a dash of probability theory, simulation and resampling, confidence intervals, inference, linear models, and causality. The chapter discusses analogous tests for multivariate linear regression. It looks at a randomization-based approach to inference, which is useful for test statistics that fail to be well-approximated by the Central Limit Theorem. The chapter also discusses more advanced techniques for regression, how to factor in more than one explanatory variable, and what to do when regression assumptions fail to be satisfied.
