This course introduces the theory of linear regression and uses it as a vehicle to investigate the mathematics behind applied statistics. The theory combines probability theory and linear algebra to arrive at commonly used results in statistics. The theory helps students understand the assumptions on which these results are based and decide what to do when these assumptions are not met, as it usually the case in applied statistics. Satisfies the proof-based requirement in the mathematics major.
Prerequisites: MATH 375 or equivalent.