MSA 8190 - Statistical Foundations for Analytics

3 Credit Hours
Description
The course covers basic probability and mathematical statistical theory, and provides a basic introduction to linear models, with an eye on application. The course starts with a primer on linear algebra, discussing the solution of linear equation systems, the rank of a matrix, determinants, eigenanalysis, and diagonalization; and basic probability theory, including probability spaces, dependence, random variables, (conditional) expectations, and sampling. It continues with the introduction of discrete and continuous distributions, and basic statistical theory of estimation and inference. Topics include consistency, unbiasedness, efficiency, maximum likelihood estimation, central limit theorem, confidence intervals, and hypothesis testing.