Introduction to Statistics

MATH 260 is where probability theory turns into actual inference: you learn how to draw defensible conclusions about a population from a finite, noisy sample, and how to quantify how wrong you might be. Expect weekly problem sets that move from deriving estimators by hand (method of moments, MLE) to building confidence intervals, running hypothesis tests, and fitting regression models, often verified in MINITAB. It assumes you've already done MATH 230-style probability, and it's the toolkit that quietly underlies everything later — econometrics, machine learning electives, and any thesis work that touches data.