Multivariate process control problems are inherently more difficult than univariate problems. It is not always clear what type of multivariate statistic should be used, and the most statistically powerful techniques do not indicate the cause(s) of a signal. On the other hand, separate controls on the individual variables are more easily interpretable but may be substantially less powerful, particularly in the face of appreciable correlation between the measures. Previous research has demonstrated the effectiveness of methods that capitalize on the likely nature of a departure from control. If only one variable is likely to undergo a shift in mean or variance then charting of each variable adjusted by regression for all others is particularly effective.
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