ABSTRACT

A quite common approach in multivariate quality control is to at­ tempt to reduce the dimensionality of the data by transforming the origi­ nal p variables into a lower dimensional data set obtained by identifying several meaningful linear combinations of the p dimensions. The con­ struction of specific linear combinations, called principal components, is described here. Case Study 1 will be used to illustrated in the application of DrinciDal comoonents in multivariate quality control.