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Sep 22, 2014 7:50 AM
(1829 views)

Is there a way to determine the appropriate sample size for a future tolerance interval, given some historical data?

In essence, I will gather data and construct a tolerance interval (one- or two-sided) from this new data and compare the tolerance bound(s) to a specification limit. I need the tolerance bounds to be within the spec limit. I would like to be able to use the information from a prior sample to inform the minimum sample size.

I've written a simulation that I believe gives me the appropriate power that a given sample size will have a tolerance interval within my limits, but is there a formal way to approach this problem? Is there a formula somewhere that uses prior estimates/errors of the mean and standard deviation to inform a future tolerance interval sample size? Equivalently, are there formulas for power sample size calculations for percentiles?

Thanks!

Charles

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I recently co-authored a paper in Quality Engineering titled "Sample size determination strategies for normal tolerance intervals using historical data" that provides a solution to this problem by calculating values to be used in the Faulkenberry-Weeks approach. It does not guarantee certain coverage levels but it did perform very well in numerical studies. The norm.ss formula is available in the R package 'tolerance'.

http://www.tandfonline.com/doi/full/10.1080/08982112.2015.1124279

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Sep 22, 2014 3:28 PM
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7.2.6.4. Tolerance intervals based on the largest and smallest observations

The NIST/SEMATECH e-Handbook of stats methods is a great (free) on-line resource for starting to answer such questions (NIST/SEMATECH e-Handbook of Statistical Methods). I have provided the link the to one of the sections on tolerance intervals. On that page is a link to the Hahn and Meeker book on statistical intervals in which you will find details on many types of statistical intervals, parametric and non-parametric solutions, including sample size calculations.

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Sep 25, 2014 6:44 AM
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I recently co-authored a paper in Quality Engineering titled "Sample size determination strategies for normal tolerance intervals using historical data" that provides a solution to this problem by calculating values to be used in the Faulkenberry-Weeks approach. It does not guarantee certain coverage levels but it did perform very well in numerical studies. The norm.ss formula is available in the R package 'tolerance'.

http://www.tandfonline.com/doi/full/10.1080/08982112.2015.1124279