MATH 392 - Mathematical Statistics

Theories of statistical inference, including maximum likelihood estimation and Bayesian inference. Topics may be drawn from the following: large sample properties of estimates, linear models, multivariate analysis, empirical Bayes estimation, and statistical computing.

Unit(s): 1
Group Distribution Requirement(s): Distribution Group III
Prerequisite(s): MATH 141 and MATH 391
Instructional Method: Lecture-conference
Grading Mode: Letter grading (A-F)
Group Distribution Learning Outcome(s):

Use and evaluate quantitative data or modeling, or use logical/mathematical reasoning to evaluate, test or prove statements.
Given a problem or question, formulate a hypothesis or conjecture, design an experiment, and collect data or use mathematical reasoning to test or validate it.