Probability and Statistical Inference
Basic laws of probability—random events, independence and dependence, expectations, Bayes theorem. Discrete and continuous random variables, density, and distribution functions. Binomial and normal models for observational data. Introduction to maximum likelihood estimation and Bayesian inference. One- and two-sample mean problems, simple linear regression, multiple linear regression with two explanatory variables. Applications in economics, quantitative social sciences, and natural sciences emphasized. Not open to students who have taken 100-level or higher Statistical Science course. Recommended prerequisite: Mathematics 21 or equivalent.