A Prediction Interval Approach to Defining Variables Sampling Plans for Finite Lots Required to be of High Quality:Single Sampling for Gaussian Processes.

摘要

A prediction-interval approach and an assumption of a Gaussian manufacturing process are used to derive a variables sampling plan applicable to finite lots required to be of very high quality. Values tabulated for calculating acceptance regions are such that, with high probability, accepted lots will have zero defects. Further, tables are given for selecting sample size for a specified combination of lot size and acceptable quality level. Comparisons show that, for a fixed risk level, substantial savings in required sample size can be effected over those specified by comparable hypergeometric sampling plans. Lots ranging in size from 5 to 100 are considered. Bounds for the probability of accepting a lot with a fixed number of defects and coming from a prescribed manufacturing process are derived and prove to be very tight. Not only are these bounds useful in defining the required sample size in the sampling plan context, but the method of derivation has application to other areas of sampling, e.g. from truncated or stratified distributions.