The Q3 update also expands existing PCA and PLS multivariate models to extend the benefits of advanced analytics efforts beyond the data experts and across the organization.

R software will be used in this course. This course covers: Differences between multivariate analysis and univariate analysis Differences between dimension reduction and clustering Principle Component ...

Principal component analysis (PCA) is an important tool for dimension reduction in multivariate analysis. Regularized PCA methods, such as sparse PCA and functional PCA, have been developed to ...

A common objective in exploratory multivariate analysis is to identify a subset of the variables which conveys the main features of the whole sample. Analysis of a well-known multivariate data set ...