Abstract

The development and application of the traditional univariate and multivariate quality control charts are distribution-based following the assumption that the process outputs come from some specified distribution(normal for continuous measurements and binomial or poison for attribute data).However, in many quality/process control applications, these distributional assumptions are either not satisfied or there is simply lack of enough information to verify the assumptions and this condition drops the performance of the charts. Yet, many researchers and quality practitioners always resort to their use probably because of unfamiliarity of the nonparametric alternatives. This paper aims to show the nature and extent of non-normality on these traditional X-bar charts and Hotelling’s χ2 control chart.

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