PROBABILITY/STAT INFER

STA 111

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. Instructor: Banks or Mukherjee

Day / Time: 

F 11:45 AM-01:00 PM

Location: 

Social Sciences 139

Instructor: 

Banks, David

Section: 

03L