STA 315 PROBABILITY AND STAT MODELS

A modern introduction to probability theory that develops a foundation in statistical thinking, understanding of randomness, and uncertainty.  Topics include sample space and events, laws of probability, conditional probability and independence, random variables from discrete and continuous families, simulation techniques including bootstrapping and Markov chain Monte Carlo methods, theoretical methods in linear regression models, and likelihood estimation. A variety of applications and examples are explored using R.