Lecture, three hours; discussion, one hour. Requisites: course 291A, doctoral standing, or consent of instructor. Focus on linear statistical models and other analytic techniques to examine complex relationships and comparisons. Approach primarily from applications and interpretation perspective. Students evaluate statistical/analytical results from research literature, analyze data using quantitative multivariate techniques, and interpret results. Introduction of concepts and interpretation of broad range of multivariate statistical and analytical approaches, including regression (linear and logistic regression and survival models), factor analysis, multi-factor and repeated measures analysis of variance, mixed effects models, machine-learning analytic techniques for big data, and other selected approaches. Students utilize several of approaches to analyze research data. Letter grading.