(3) Introduction to statistical modeling and analysis with some applications to big data. Topics include: linear models-regression, analysis of variance, multiple comparisions, shrinkage methods, and model checking; dimension reduction methods; discriminant analysis and clustering; and Bayesian modeling and computation. NOTE: Some knowledge of programming useful but not required. COREQUISITE: MATH 4614 or MATH 4636 Add to Portfolio (opens a new window)
