We provide an easily implemented procedure to help data analysts systematically diagnose which quality characteristics may be driving the dispersion of a multivariate process out of control. Multivariate statistical process control commonly uses Hotelling's T2 statistic to indicate when a multivariate observation goes out-of-control. Several techniques currently exist that accurately diagnose which specific variables are driving the T2 statistic out-of-control. For subgroups of independently and identically distributed multivariate normal observations, we advocate decomposing the overall T2 into independent T2 statistics for separate monitoring of location and dispersion. We propose a procedure based on principle components to diagnose the specific variables responsible for driving subgroup dispersion out-of-control. The procedure is demonstrated on a publicly available data-set.
Murphy, Terrance E.
"Principle Components for Diagnosing Dispersion in Multivariate Statistical Process Control,"
Georgia Journal of Science, Vol. 67, No. 2, Article 1.
Available at: https://digitalcommons.gaacademy.org/gjs/vol67/iss2/1