A Comparison of Methods for Univariate and Multivariate Acceptance Sampling by Variables

Abstract

We investigate acceptance-sampling methods for univariate and multivariate normal data in which the quality of the process relative to specification limits is measured by an estimate of the proportion nonconforming, and the mean and variance are unknown. A maximum likelihood method is developed, and we compare it with existing approaches to acceptance sampling. This method is applied to a problem involving government regulation of the gas industry in Canada. The justification for basing national and international standards on an approach based on the minimum variance unbiased estimator of the proportion nonconforming is examined.

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