1. A process for multivariate data analysis comprising the steps of: using a computer in conjunction with a Gram-Schmidt orthogonalization process to determine normal Gram-Schmidt vectors corresponding to observable normal values of a plurality of normal datum where at least one of said plurality of normal datum has zero standard deviation; computing abnormal Gram-Schmidt vectors corresponding to observable abnormal values of a plurality of abnormal datum; computing a signal to noise ratio for said normal Gram-Schmidt vectors and said abnormal Gram-Schmidt vectors to obtain abnormal predicted values; and using said abnormal predicted values for a future prediction.