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P chart vs. IMR chart

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  • #50939

    Van Loon
    Participant

    If i use P chart to plot % defective I get a lot of points out of control.
    If i use IMR chart I mayby get one point or none.
    I have read that IMR is OK to use, but i get two very different conclusions, P – out of control, IMR – in control.
    Is P charts more sensitive ?
     
    Jan

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    #175764

    JenniferA
    Participant

    Is the data you are plotting binomial?

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    #175766

    T Niebank
    Member

    Jan,
    IMR and P charts are used for two different types of data.  If your data is Attribute data use the P chart.  If it is Continuous data use the IMR.

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    #175768

    Van Loon
    Participant

    Produced xxxx parts, yy bad parts = z% bad parts.
    So yes the data are binomial, but mayby the proces is not ;-)

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    #175769

    Van Loon
    Participant

    I get that.
    But even Wheeler in his SPC book states that it is OK to use IMR on some types of atribute data.
    He even states that in many cases binominal data dont come from binominal processes, thus IMR would be prefereable.
    Jan

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    #175773

    Darth
    Participant

    As usual, you are getting erroneous information. Yes, Wheeler says you can switch to an I/MR chart if the p>=5. If your sample size is getting too large, you start to violate some of the assumptions of the binomial and the large n in the denominator are giving you a false tightening of the control limits. If p>=5 then an assumption of continuous/normal can be made and thus the use of the I/MR.

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    #175776

    JenniferA
    Participant

    Look at page 252 of Wheeler’s Advanced Topics in SPC. 
    Usually the two charts are very similar, so your question is interesting.  I’m guessing that you have something going on in the data. Does the sample size from sample-to-sample vary considerably (more than about 25%)?  The I chart won’t take that into account, but the p-chart will.  Plot the sample size vs. %defectives to see what that looks like.   

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    #175777

    JenniferA
    Participant

    Darth,
    Do you mean np? 
    Darth stated the sample size concern better than I did, sorry about that.

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    #175780

    Darth
    Participant

    Good catch, np>5.

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    #175790

    Bower Chiel
    Participant

    Hi JanI’ve been involved in some charting of data on the process of care of stroke patients. It was a state requirement that monthly proportions of patients having a CT brain scan within 48 of admission to hospital be reported. We decided not to use p-charts for this data because the probability of having a scan within 48 hours is not constant from patient to patient since, for example, the provision of radiographers differs (sadly) between week-ends and week days. Thus we could not assume a binomial distribution which underpins the p-chart. We therefore used an Individuals (X)chart. Apparently justification for this approach may be found in the Western Electric Quality Control Handbook from 1956.You might find it of interest to read Improved Control Charts for Attributes by David Laney available in full and free at http://www.quadrantonegroup.com/pdf/laneydoc.pdf. He introduces the p’ chart and argues it does a better job than the X chart when binomial assumptions are not met. I’ve written a crude Mintab macro to produce a basic p’chart which I’ll happily pass on to you if you contact me at [email protected] WishesBower Chiel

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    #175796

    Van Loon
    Participant

    Yes sample seize varys from 1.000 to 5.000.
    Maby I get “punished” for the large sample sizes, making my CL to narrow.

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    #175799

    Ron
    Member

    Jan,
    Dr. Donald Wheeler as always is  a great mentor I highly recommend his book “Understanding Statistical Process Control”.
    P charts are not the chart of choice in most cases.
     

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