(3) Introduction to statistical modeling and anlysis with some applications to big data. Topics include: linear models - regression, analysis of variance, multiple comparisons, shrinkage methods, and model checking; dimension reduction methods; discriminant analysis and clustering; and Bayesian modeling and computation. Some knowledge of programming is useful but not required. COREQUISITE(S): MATH 6614 or MATH 6636.
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