Destructive Gage RR - Using Crossed - want your thoughts

S

speedworks

#11
Miner, I understand that setup, but not sure it makes sense for my design and process. If I am going to make the assumption that 2 parts are the same (consecutive cavity shots), then I should assume all 6 are the same (2 per operator). I will run it with this, just to look at it though, and see how much different it is vs the crossed. Either way, I have a poor gage.
 
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S

speedworks

#12
So, I went back for one of the attributes and renumbers to analyze as a Nested study, and here is what I got vs Crossed:

P/T = 30.03 vs 32.85 (crossed)
Study V = 83.31 vs 90.26 (crossed)
Contr = 69.4 vs 81.47 (crossed)
Categ = 1 both

Not really different either way
 

Bev D

Heretical Statistician
Staff member
Super Moderator
#15
Select Reply to this post
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This opens a window that allows to browse to your PC , select a document - such as a spreadsheet containing your data - it will attach the document t to your reply. We can then open the do IMF t from your post
 
L

longcr

#17
The earlier comment about a DGR&R being a nested study is generally the case. Most approaches to GR&R and DGR&R are based on a Random Effects model.

There may be a slightly different way to approach your own situation. Usually 'Part', along with 'Operator' and 'Trial' (which is a unit that is tested for a DGR&R study) are treated as Random Effects. Here, you use Cavity as Part. Cavity probably isn't a valid Random Effect, so I worked up a model that treats it as a Fixed Effect.

The key to understanding these models and making sure the stats are correct is the EMS table. The EMS table tells you how to structure and compare the variance components, and it can be used as a check against the output of your statistical software to be sure the model was treated correctly.

I am attaching an example (attached pdf file). (This example assumes that I read and understood your earlier notes correctly.) My example is based on materials from Montgomery, Perez-Wilson, AIAG, and (for the deeper stats behind the Variance Components model) Searle.

Here is a link to other examples and more information on GR&R, DGR&R, and the underlying Fixed, Random, and Mixed Effects models.
goo.gl/3C2Ov3
(also see the pdf files with specific examples at the bottom of this web page)

Other links that might be of interest:
goo.gl/J7vhsn
goo.gl/TfqEnF

For links, I had to remove the h-t-t-p portion.

Getting the model correct, including the EMS, is critical. If you aren't familiar with these things, you may want to find a local statistician.

Best regards,
Cliff
 

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S

speedworks

#18
Hi Longcr, thanks for the input. I looked at your attachment and the assumptions are correct, although I am not sure I understand how to use the EMS table or what your recommendation is for my analysis - can you try to explain it again?
 
L

longcr

#19
The EMS table can be confusing, but it is the key to getting the model ... and analysis ... right. It is also the key to adapting the standard GR&R model to various other situations.

If you need a little coaching on how to use and interpret it, I might be willing to spend a few minutes. If you had data, this might make the discussion more productive.

The books that I mentioned also address the topic (EMS tables for random, fixed, and mixed effects models) and provide examples.

One other excellent book that takes the mixed effects model approach to GR&R, although a bit advanced, is Montgomery's "Design and Analysis of Gauge R&R Studies: Making Decisions with Confidence Intervals in Random and Mixed Anova Models"
 
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