Introduction to mathematical concepts and methods essential for multivariate statistical analysis. Areas may include basic matrix algebra, eigenvalues and eigenvectors, quadratic forms, vector and matrix differentiation, unconstrained optimization, constrained optimization, and applications in multivariate statistical analysis. Three lecture hours a week for one semester. Statistics and Data Sciences 381 and Statistics and Scientific Computation 381 may not both be counted. Prerequisite: Graduate standing and a course in statistics.