Probability and Statistical Inference


Basic laws of probability‚ÄĒrandom 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. Prerequisites: MATH21 or equivalent. Not open to students who have credit for another Statistics 100-level course. One course.

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