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HBOK 1-444

Title Statistics Roundtable: Detecting Dependent Observations in Multivariate Statistical Process Control
Author(s) Robert L. Mason and John C. Young
Source Quality Progress
Topic Performance Measurement

Data Analysis

Abstract Since we must consider many variables at the same time when monitoring a multivariate process, detection of data dependencies between and among the observation vectors is not straightforward. The test procedures that exist for checking for the non-randomness of the observation vectors are more complicated than those used with the corresponding univariate procedures and require knowledge of multivariate analysis.  The best approach for checking for data dependencies is software on multivariate procedures that will perform the necessary computations. If you lack such tools, it is recommended, at a minimum, you apply the graphic methods of univariate analysis (for example, time-sequence plots) to the individual variables of the multivariate observation vector to detect the various forms of autocorrelation that denote data dependencies. This simple visual approach should be of great value.
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Reference Code 1-444

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