Biological Data Analysis II — Multivariate Analysis

Catalog description

From molecular biology to ecosystem studies, technology is facilitating collection of large, multivariate biological data sets. Multivariate analyses seek to simplify, summarize, and test hypotheses about these complex data sets. Addresses major issues in multivariate analysis, and will introduce students to common analysis types and visualization approaches. Subjects covered will include: principal components analysis, discriminant analysis, canonical correlation, and redundancy analysis. Additional subjects will be selected by students based on their needs and interests. Prerequisite(s): BIOL 215 with a minimum grade of B (3.), or consent from instructor, or enrollment in the Biological Sciences graduate program.