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link between confidence interval Ppk

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  • #50738

    Sorour
    Participant

    Hi
    Is it possible to calculate the Ppk for a corresponding confidence interval and sample size?
    For example for a confidence interval of 95/99.9 the Ppk for 30samples is 1.34 (using aql)
     
    Are there tables or a calculation I can use to develop Ppk for 95/99.9 for different sample sizes and different confidence intervals? E.g. 9 17 21 etc.
     
    Thank you
    Paul

     

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    #174832

    Ron
    Member

    Quick answer is no.
    Ppk is a long term process capability typically with 100 samples taken or more.
    Confidence Intervals are the probability that a specifc value will fall within this range.
    Ppk is very specfic  Confidence intervals are general.
     

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    #174833

    The Kid
    Member

    Hi Paul,
      I am not 100% sure about this, but I think some time ago I did read some articles describing this. I think the outcome of the study was a look up table with sample size and confidence interval for CPK values.
    So I would suggest you do some googling on the same. Would be great if forum experts can provide some insight on this.
    Cheers,
    The kid

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    #174843

    Sorour
    Participant

    Hi Guys.
    I was talking to a work colleague. He said you can determine the Ppk by using your confidence intervals and k value. Simply by using the confidence formula 95/999 = mean –k X sd and LSL- mean divided by 3 x sd = Ppk you can work out that the Ppk is 1/3 the k value.
    Don’t know if anybody heard this before or if there some data behind it. I would be interested to hear your comments.
     
    Secondly, typically what would be the smallest sample size you would use to work out Cpk/Ppk? I am recommending a minimum of 15 but ideally 30.
    Thanks again
    Very much appreciated
    Paul

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    #174912

    Mikel
    Member

    Pp can be related to the tolerance interval which is a containment interval of individual values at a given level of confidence. E.g., a 95/95 tolerance interval contains 95% of the expected values at a 95% confidence level. Tolerance Intervals are sensitive to departures from normality so transformation is required of most data.

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