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Feb 9, 2017 7:25 AM
(1606 views)

Hi, I have a process that is log-normally distributed and has an upper spec limit, and I'd like to calculate a Cpk. I tried two approaches that gave me different answers:

- Save spec limit as column property for process. Plot distribution of process, then Continuous Fit --> LogNormal to get a Cpk (checking goodness-of-fit of course)
- Add new column. Set column formula as log(process), save column upper spec limit as log(upper spec limit). Plot distribution, get Cpk from capability analysis. Validate via Continuous Fit --> Normal and checking goodness-of-fit.

Which of these approaches would you recommend? Or could you suggest a better approach?

Many thanks,

Alex

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Feb 9, 2017 9:51 AM
(1959 views)

I consider both of the techniques you specified as good vialble processes. However, the process that I would actually recommend is a very nice builtin capability of the Distribution Platform. If you run the Distribution Platform, you can then go to

Continuous Fit==>All

It will then evaluate your data to find which distribution(s) it fits. Once done, and if a Log Normal distribution is found to be the fit, you can go under "GLot" distribution and

Save Transform

It creates a new column using the determined Generalized Log algorithm

Jim

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Feb 10, 2017 5:07 AM
(1943 views)

I understand your frustration. The question on why log normal does not have a Save Transform would have to be answered by the JMP developer.

Concerning the difference between the Cp/Cpk for the Distribution lognormal and your own transformed to log(x) and then calculate.......it appears that there are differences in the calculation of the PDF. I am taking that from the description within

Help==>Books==>Basic Analysis

But a more complete answer should be available from JMP Support

Jim

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Feb 9, 2017 9:51 AM
(1960 views)

I consider both of the techniques you specified as good vialble processes. However, the process that I would actually recommend is a very nice builtin capability of the Distribution Platform. If you run the Distribution Platform, you can then go to

Continuous Fit==>All

It will then evaluate your data to find which distribution(s) it fits. Once done, and if a Log Normal distribution is found to be the fit, you can go under "GLot" distribution and

Save Transform

It creates a new column using the determined Generalized Log algorithm

Jim

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Feb 10, 2017 3:45 AM
(1583 views)

Thanks Jim. It's a bit frustrating that the LogNormal transform can't be saved in the same way - the GLog transform adds additional complexity, which is hard to justify and would be (even) harder to explain to my colleagues.

Any idea why I got different answers between my initial two approaches?

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Feb 10, 2017 5:07 AM
(1944 views)

I understand your frustration. The question on why log normal does not have a Save Transform would have to be answered by the JMP developer.

Concerning the difference between the Cp/Cpk for the Distribution lognormal and your own transformed to log(x) and then calculate.......it appears that there are differences in the calculation of the PDF. I am taking that from the description within

Help==>Books==>Basic Analysis

But a more complete answer should be available from JMP Support

Jim

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Jul 18, 2018 7:52 PM
(446 views)

I have also been trying to figure out why the Cpk result from using the built-in Capability Analysis and the manual method of calculating the Cpk via a log transform of the data is giving such different results. Any insight into the formula that JMP is using for the log Normal Cpk calculation would be greatly appreciated.