Q602 : Multivariate Statistical Analysis
It primarily focuses on the General Linear Model (GLM) and the various forms it takes in the multivariate context. The topics include matrix algebra, data screening, multiple regression, multivariate analysis of variance, discriminant function analysis, logistic regression, principal components analysis, and exploratory factor analysis. In addition, a weekly lab session is provided where students can learn SAS program.
Semester(s) Offered: Spring, Fall
Class time: Monday, Wednesday
Lab time: Friday
Other Contact(s): Zhongxue Chen - firstname.lastname@example.org
Sequence: Q601, Q603, Q611, Q612
Prerequisites: At least two graduate-level statistics courses with a B or higher grade
Algebra Required?: Used for notation/homework
Calculus Required?: User for notation/homework
Day(s) per week offered: Two lectures a week and a computer lab
Recommended follow-up classes: Multilevel modeling, structural equations modeling, categorical data analysis courses
Substantive Orientation: Public Health, Behavioral Sciences
Statistical Orientation: ?
Applied/Theoretical: applied with explanations (not derivations) of statistical foundations.
Software Used: SPSS, SAS
How the software is used: data analysis, data management.
Problem Sets: Yes, most requiring analysis and interpretation of data. Also, data management/manipulation.
Data Analysis: Yes in homework and take-home portion of each exam.
Exams: Yes, there is a mid-term and a final.
Keywords: matrix algebra, data screening, multiple regression, multivariate analysis of variance, discriminant function analysis, logistic regression, principal components analysis, exploratory factor analysis, SAS.