Abstract
Quantitative measurement is an accepted ideal, but pass–fail inspection remains a fact of life, even in high-technology industries. For pass–fail data, variance components do not separate gauge and material variation. This article focuses on maximum likelihood estimation of conditional misclassification rates, with and without reference evaluations to anchor the analysis. Likelihood-based confidence intervals and testing for reproducibility effects are also discussed.
Published Version
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