Official Description: Lecture—3 hours; discussion—1 hour. Prerequisite: course 130B, and preferably course 131B. Multivariate normal distribution; Mahalanobis distance; sampling distributions of the mean vector and covariance matrix; Hotelling’s T2; simultaneous inference; one-way MANOVA; discriminant analysis; principal components; canonical correlation; factor analysis. Intensive use of computer analyses and real data sets. GE credit: QL, SE.—III. (III.)

Instructors: This is taught by Duncan Temple-Lang

Other info and comments: Let us know in the comments

Software Used: Primarily R, with a few other complementary tools

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