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Interaction In R%R

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  • #37272

    ROSS
    Member

    Hi:
       How should we deal it when we find part*operator is significant when we do R%R study?
      Tks!

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    #109451

    ROSS
    Member

    Hi:
       Can somebody who has experience on it help me?
       Tks!

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    #109454

    Lee
    Member

    Just so we are clear, your part to part variability is low, your person to person variability is low, but your person-part variability is high?
     
     

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    #109459

    ROSS
    Member

    Hi Rock:
       Thanks for your feedback first!
       Pls see belowing sample from Minitab help file, we can find Part  and Operator*Part is significant factor for their P-value is less than 0.05. So, my question is what should do on it?
    Two-Way ANOVA Table With Interaction
    Source         DF  SS       MS        F        P                                                            Part            9  2.05871  0.228745  39.7178  0.00000Operator        2  0.04800  0.024000   4.1672  0.03256Operator*Part  18  0.10367  0.005759   4.4588  0.00016Repeatability  30  0.03875  0.001292                  Total          59  2.24912 

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    #109479

    P3
    Participant

    Since you haven’t provided any info regarding %Contribution the P-value itself may be strong but that doesn’t necessarily mean you have to react on it.
    By the Degrees of Freedom and MS-values I made a quick calculation and came up with the conclusion that the %Part was approx 0,903 meaning your measurement equipment (including interaction) is less than 10%. This is just about accepted unless you have seen some trouble with it.
    To deal with interaction it all comes down to why part(s) is behaving differently when measured with a specific instrument (device). You have to find the part, and the device, that contributed to the high interaction value. Then examine all factors that distinguish the part/device from the others, and hopefully you find the answers to the odd behaviour.
    What part and device that you’re looking for could be seen in MINITAB’s GageR&R interaction graph (bottom, right hand graph; in ver 14).
    “To solely react on P-values is like putting up the umbrella when a rain drop falls on your head, but the sky’s almost blue.”
    The drop is real, true (that’s what the P-value says).But there’s no rain as far as the eye can see; that’s what the %Contribution says.So, no umbrella (no reaction).
    I know, a lousy analogy, but I want you to look further than just a few inches, which I think you may do when looking at single parameters.
    P3
     

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