Statistics are often viewed as confusing and complicated, but multivariate data analysis (MVA) methods can be used to amass knowledge simply.

Abstract Multivariate analyses such as principal component analysis were among the first statistical methods employed to extract information from genetic markers.

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 ...

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 ...