Control Charts and Non-Normal Data

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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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Comments (11)

  • AnonymousJune 29, 2014 Reply

    Thank you for another great and interesting Newsletter Bill, and your SPC teaching. Looking forward to Version 5. Didrik

  • AnonymousJune 30, 2014 Reply

    now i don't have cognitive dissonance on normality in control charts :)

  • AnonymousFebruary 9, 2015 Reply

    Hi thank you for writing this article- it’s very helpful and informative. I just have a quick question- is it unusual for non-normal data to have Individuals and Moving Range graphs in control before transformation, but to have the graphs out of control after transformation? Any advice would be greatly appreciated. Kind regards

    • billFebruary 11, 2015 Reply

      I find that odd but I would have to see the data to understand what is going on.  Have you seen this?

  • Costa, RicardoDecember 30, 2016 Reply

    Does it will be more pedagogical to suggest the readers to evaluate data distribution (such as shown in Figure 1) and then choose the most appropriate chart (exponential chart for this case/data)?

    • billDecember 30, 2016 Reply

      There is nothing wrong with doing that.  Just need to be sure that there is a reason why your process would produce that type of data.  But most of the time, the individuals chart will give you pretty good results as explained above.

  • RaajJanuary 4, 2018 Reply

    Hii Bill, Thanks for the great insight into non-normal data. Can you please explain this statement " The control limits are found based on the same probability as a normal distribution.  So, the LCL and UCL are set at the 0.00135 and 0.99865 percentiles for the distribution. " in detail. I want to know how control limits will be calculated based on above mentioned percentiles.

    • billJanuary 4, 2018 Reply

      If you have a perfect normal distribution, those probabilities represent the the probability of getting a point beyond three sigma limits.  So, you simply use the functions for each different distribution to determine the values that give the same probabilities.

  • Ravi August 16, 2021 Reply

    Thanks for this insightful information. I have read that option three (transformation to normal data) is not recommended as that is manipulating the data? Would this be correct or is it advisable to transform it? Thanks

    • billAugust 16, 2021 Reply

      Transforming the data gives different values – this makes it more difficult to understand the output.  I usually don't do this, although there are some who believe it is ok to do.  If you are only interested in out of control points, i guess it would be ok.  But i would stick to the X-mR chart most likely.

      • Ravi August 17, 2021 Reply

        Thanks for the response Bill

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