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- Nonparametric Tests: Kolmogorov Smirnov test for variance testing

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Jan 3, 2017 2:39 PM
(4410 views)

Hello:

I have multiple (large data) distribution that I have already tested the means and determined that they are the same and roughly zero. This is what I expected based on subject expertize. Two of the distribution have larger variations than my other distribution. I see this graphically and based on the summary statistics. I want to be more formal and test for differences in the variances. I was thinking of doing Kolmogorov Smimov testing; however, I could not figure out how to do this for my 12 distributions simultanously. Should I even be using the KS test for this purpose? I do not want to be tied to normal distribution testing. Suggestions are appreciated.

Rob

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Jan 3, 2017 4:56 PM
(7448 views)
| Posted in reply to message from robert_reviere 01/03/2017 05:39 PM

Generally speaking, the non-parametric tests help you decide if two or more populations are different with regard to **any** parameter. They are not specific to a **particular** parameter, as are tested with the parametric tests.

Learn it once, use it forever!

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Re: Nonparametric Tests: Kolmogorov Smirnov test for variance testing

Jan 3, 2017 3:31 PM
(4401 views)
| Posted in reply to message from robert_reviere 01/03/2017 05:39 PM

JMP provides several statistical tests for comparing the variances between multiple groups. Under Fit Y by X (oneway), you can select "Unequal Variances" from the red triangle, and JMP will provide a list of stats that will give you a better option than KS for testing the variances.

Jim

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Re: Nonparametric Tests: Kolmogorov Smirnov test for variance testing

Thanks.

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Jan 3, 2017 4:56 PM
(7449 views)
| Posted in reply to message from robert_reviere 01/03/2017 05:39 PM

Generally speaking, the non-parametric tests help you decide if two or more populations are different with regard to **any** parameter. They are not specific to a **particular** parameter, as are tested with the parametric tests.

Learn it once, use it forever!