The University of Texas at Austin 3 credits Statistics and Data Sciences
An exploration of practical applications of the projection approach to linear models, building from a review of essential linear algebra concepts to the theory of linear models from a projection-based perspective. Introduction to Bayesian ideas. Additional topics include analysis of variance, generalized linear models, and variable selection techniques. Statistics and Data Sciences 387 and Statistics and Scientific Computation 387 may not both be counted. Three lecture hours a week for one semester. Pre/Corequisites: Graduate standing; Economics 392M (Topic 19: Probability and Statistics), Statistics and Data Sciences 384 (or Statistics and Scientific Computation 384), or the equivalent; and basic coding skills in R, Matlab, or Stata.
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