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Abstract
When test measurements of items differ from the true product values because of random measurement error it is possible to reject satisfactory items and to accept inferior ones. Confidence bounds for these misclassification probabilities are obtained, assuming that the true product values and measurement errors are independently distributed normal variates. Both joint and conditional probabilities are investigated. These probabilities are functions of the proportion of measurement error variability (relative to the total variance). Methods are presented for situations where this proportion is known or is estimated from sample data. Tables are provided to simplify the computations required.
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Notes on contributors
Robert W. Mee
Dr. Mee is an Assistant Professor at the University of South Alabama.
D. B. Owen
Dr. Owen is a University Distinguished Professor at Southern Methodist University. He is a Senior member of ASQC and is also an ASQC Certified Reliability Engineer.
Jyh-Cherng Shyu
Dr. Shyu is an actuary for American Family Life Assurance of Columbus.
