This course is designed to provide students with a working knowledge of the basic concepts underlying the most important multivariate techniques, with an overview of actual applications in various fields, and with experience in actually using such techniques on a problem of their own choosing. The course will address both the underlying mathematics and problems of applications. As such, a reasonable level of competence in both statistics and mathematics is needed.
As an introductory multivariate statistical analysis course designed for undergraduate students, the aim of the course is to introduce a variety of standard statistical methods used to analyze multivariate data, emphasizing the implementation and interpretations of these methods. Topics covered include overview of multivariate methods, examining data such as model assumptions, outlier detection and missing value management, exploratory factor analysis and principal component analysis, classification/discrimination, linear and multiple regression as well as cluster analysis, structural equations modeling and canonical correlation analysis.
- Teacher: Miraluna Herrera