iSixSigma

DOE

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  • #52051

    Burichi
    Participant

    hi,anybody who could help me figure out what to do? i have ran an experiment… 2^2 factorial design… my problem is my response variable is an attribute/proportion data. is factorial design applicable for this case? coz i was thinking that factorial design is only applicable when you have variables type of response.thanks in advance!

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    #182485

    Mikel
    Member

    It’s fine, you just needs lots of data – go look at sample size for a two
    proportion test and make sure you have it covered.

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    #182492

    Ashky
    Participant

    You can run DOE for attribute data. all you have to do is collect 10 or 20 samples (for ex) for each run and check the no. of fails or pass.
    Ashky

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    #182493

    Mikel
    Member

    Your advice of 10 or 20 is dangerous. Sample size depends on the p value, how big of a difference you
    need to detect and desired confidence. You sample size is likely
    much bigger than your suggestion.

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    #182494

    MBBinWI
    Participant

    and if you end up not having good replicates, you can always fall back on Chi Sq. just using counts for each condition.

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    #182496

    GB
    Participant

    Stan,
    got your note…made contacts in/out.

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    #182498

    Mikel
    Member

    Your advice of 10 or 20 is dangerous. Sample size depends on the p value, how big of a difference you
    need to detect and desired confidence. You sample size is likely
    much bigger than your suggestion.

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    #182507

    Craig
    Participant

    Burichi,
    Why didn’t you ask this question before you ran the experiment? I have performed factorial experiments in the SMT industry where the quantity of solder balls was the response. (Count data). You should always anticipate how you will analyze your data before you collect it.
    HACL

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    #182508

    Bower Chiel
    Participant

    Hi BurichiIn the book Process Quality Control, (subtitled Troubleshooting and Intrepretation of Data) by Ellis Ott et al, the numbers tested for each combination of factor levels range in the examples he gives range from around 40 upwards. The book uses Analysis of Means (ANOM) rather than Analysis of Variance (ANOVA) but has lots of good examples in it. I have the third edition and it comes with a CD with software for ANOM.Best WishesBower Chiel

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    #182511

    Ron
    Member

    You did not share the nature of your data.. some attribute data can be stretched into continuous data if it is correctly setup.
    Did you have simply pass or fail or did you have degrees of goodness?

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    #182516

    MBBinWI
    Participant

    Oftentimes there was actual continuous data gathered, then it was evaluated and a judgement of “good” or “bad” was made.  See if there was actual continuous data to begin with.  Surprisingly, I’ve seen organizations “flush” continuous data, but retain forever pass/fail. 

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    #182630

    Burichi
    Participant

    why use two proportions to get the sample size? should i use one proportion?

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    #182631

    Burichi
    Participant

    so what did you use in your DOE? what kind of experimental design?

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    #182632

    Burichi
    Participant

    for each legs i ran a few samples… i can compute for the proportion for each leg. can i use a factorial design for this?

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