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Confidence Intervals for Proportions and Poisson Means

Attribute data are better than no data, but that’s about the best you can say of them

William A. Levinson
Tue, 04/05/2016 - 00:00
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Experiments that might require a handful of real-number measurements (variables data) could need hundreds or more attribute data for comparable power, i.e., the ability to determine whether an experiment improves performance over that of a control. Sample sizes needed for ANSI/ASQ Z1.4 (for inspection by attributes) are similarly much larger than sample sizes for ANSI/ASQ Z1.9 (for inspection by variables).

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One application of attribute data is the estimation of the nonconforming fraction (p) from a process. The binomial distribution is the standard model in which p is the probability that each of n items will or will not have a certain attribute (such as meeting or not meeting specifications). The probability p is assumed to be identical for every item in the population; that is, every item has the same chance of being nonconforming. In addition, the sample n is assumed to come from an infinite population. That is, removal and inspection of an item does not change the probability that the next one will have the attribute in question.

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Comments

Submitted by Dr Burns on Tue, 04/05/2016 - 14:51

Assumption not met

You state "If the latter assumption is not met...".  I'm looking forward to Dr Wheeler's next article on how much data it takes to ascertain this and before we collect such data, the process and hence its distribution will have changed.  As Shewhart pointed out, we can never know the distribution of data for a process.

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