Principle Components for Diagnosing Dispersion in Multivariate Statistical Process Control

Authors

Terrance E. MurphyFollow
Mary McShane-Vaughn
Kwok Leung-Tsui

Abstract

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.

Recommended Citation

Murphy, Terrance E.; McShane-Vaughn, Mary; and Leung-Tsui, Kwok (2009) "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