— Multivariate Data Analysis

Prerequisites & course information

This version has ended; see updated course, below. Course Description: Multivariate normal distribution; Mahalanobis distance; sampling distributions of the mean vector and covariance matrix; Hotellings T2; simultaneous inference; one-way MANOVA; discriminant analysis; principal components; canonical correlation; factor analysis. Intensive use of computer analyses and real data sets. Learning Activities: Lecture 3 hour(s), Discussion 1 hour(s). Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL). STA 135 — Multivariate Data Analysis (4 units) Course Description: Multivariate normal distribution; Mahalanobis distance; sampling distributions of the mean vector and covariance matrix; Hotellings T2; simultaneous inference; one-way MANOVA; discriminant analysis; principal components; canonical correlation; factor analysis. Intensive use of computer analyses and real data sets. Prerequisite(s): ( STA 130B C- or better or STA 131B C- or better); ( MAT 022A C- or better or MAT 027A C- or better or MAT 067 C- or better); MAT 167 recommended. Learning Activities: Lecture 3 hour(s), Discussion 1 hour(s). Enrollment Restriction(s): Pass One restricted to Statistics & Data Science majors and Statistics & Biostatistics graduate students. Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL). This course version is effective from, and including: Fall Quarter 2026. Learning Activities: Lecture 3 hour(s), Discussion 1 hour(s). Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL). STA 135 — Multivariate Data Analysis (4 units) Course Description: Multivariate normal distribution; Mahalanobis distance; sampling distributions of the mean vector and covariance matrix; Hotellings T2; simultaneous inference; one-way MANOVA; discriminant analysis; principal components; canonical correlation; factor analysis. Intensive use of computer analyses and real data sets. Prerequisite(s): ( STA 130B C- or better or STA 131B C- or better); ( MAT 022A C- or better or MAT 027A C- or better or MAT 067 C- or better); MAT 167 recommended. Learning Activities: Lecture 3 hour(s), Discussion 1 hour(s). Enrollment Restriction(s): Pass One restricted to Statistics & Data Science majors and Statistics & Biostatistics graduate students. Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL). This course version is effective from, and including: Fall Quarter 2026.