Monitoring Destructive Test Methods

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Thanks so much for reading our publication. We hope you find it informative and useful. Happy charting and may the data always support your position.


Dr. Bill McNeese
BPI Consulting, LLC

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Measurement Systems Analysis/Gage R&R

Comments (4)

  • Stephen PuryearDecember 1, 2015 Reply

     Another really excellent article!  I look forward to reading these every month and they never fail to teach me something.  First of all, this is unusually good technical writing about a subject that can be confusing and is still being created.  Second, the approach is always very practical and generous rather than an attempt to talk over the heads of the audience.  I use the word generous because if the previous topics in this series were assembled, they could easily form the basis for a two or three year Operations Research degree program at a very expensive school.  So dive in!  I also appreciate the references to other people's work in the field such as Dr. Donald Wheeler.  Dr Wheeeler is another good source for anyone trying to get a stronger understanding of how Gage R&R works. 

  • DaleWMay 19, 2016 Reply

    Should the fact that this dataset provides no evidence of a correlation between the two supposed duplicates (r=-0.03) cause one to reconsider whether one really has succeeded in obtaining duplicates that are sufficiently the "same" to go ahead with this effort to partition variance between the process and the test method?

    • billMay 21, 2016 Reply

      Good point.  The data were not actual data but generated to demostarte the calculations and layout of this method.  But you are correct.  The duplicates are not correlated which raises questions about if they are really the "same."

    • Bryan CastlesDecember 4, 2017 Reply

      Since the data represent measurements of the same property taken on "identical" samples, there should not be any correlation of the data. If the processes, including sampling and testing, are in control,  the differences represent the random variation of the process. Correlation between the measurements could indicate a bias in the sampling and testing. Correlation between the pairs would indicate variation of the process with time, assuming the data are time ordered.

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