Carnegie Mellon University

Multivariate Data Analysis

Course Number: 47755

Researchers frequently collect measurements on several variables simultaneously. Multivariate statistics is focused on the analysis of these simultaneous measurements. It generalizes the ideas of univariate data analysis to create analyses that are more powerful both in a statistical as well as a practical sense. This power comes with the added costs of multivariate notation and computing effort. Since statistical software can readily handle the complex statistical calculations that are necessary, the goal for this course is provide students with the supporting knowledge to interpret these results, select appropriate techniques, and evaluate the strengths and weaknesses of these approaches. The course covers the following topics: multivariate normal distribution, general linear model, multivariate regression, MANOVA, clustering, principal components and factor analysis. This course is specifically designed for graduate students who intend to do empirical research and focuses on how to apply multivariate statistics to large datasets that involve structured numerical data as well as unstructured data like text and social networks.

Degree: PhD
Concentration: Marketing
Academic Year: 2019-2020
Semester(s): Mini 3
Required/Elective: Elective
Units: 6


Lecture: 100min/wk and Recitation: 50min/wk