Hi, I have two possible test methods for measuring a response, and I want to determine which test method is the better measurement system. I've done a Gauge R&R study for each test method (using same parts and same operators, varying only the method used). The results of the two studies are quite similar, so I'd like to do a hypothesis test to understand whether the test method with the slightly lower R&R% is significantly better than the other test method. The Gauge R&R/MSA platform doesn't provide a suitable option for doing this, as far as I can tell (whether I do separate analyses or include the test method type as a binary grouping factor). The only place where I can find rigorous heterogeneity of variance tests in JMP is in the Fit Y by X platform, but that won't do for this situation because there are multiple factors to consider ( operator, part, operator*part , and test method type). So my thinking on how to tackle this is as follows: Stack my two test method responses into one column and add a categorical column specifying which test method was used Fit Model with operator, part and operator*part as the only factors (i.e. not including test method type) Save residuals from this model into a new column Using Fit Y by X, fit the residuals by the test method type Conduct heterogeneity of variance tests to see whether the two test methods give significantly different levels of variation Does this sound like a valid approach? Does anyone have any other suggestions for how I could approach this? Many thanks, Alex
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