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Date:         Thu, 17 Feb 2005 20:05:37 +0000
Reply-To:     Rani Vohra <>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         Rani Vohra <>
Subject:      Goodness of Fit Test Apply? Two-fold question:
Content-Type: text/plain; format=flowed

Hi Everyone,

I am trying to determine which tests apply to my data, and I am a bit concerned. I have collected data (1000's) about how well an industry test accurately passes a production item.

The data looks something like: After testing an item once - an item has a 40% chance of passing the test After testing an item twice (with the same test!) - an item has a 60% chance of passing After testing a item three times - at item has a 70% chance of passing

And so forth, until about 5 re-tests result in about 95% rate of passing.

Obviously, there is something wrong with the process and I will be attempting to narrow down the causes.

I have noticed that the standard deviations also decrease with each re-test. Does the F-test accurately apply here to determine if they are statistcally significant? And if so what, what does that suggest? Human or equipment error?

My second question is once I have implemented some change and have collected new data for the same categories, does it make sense to use the Chi-Squared Goodness of Fit test to determine if there is a statiscally significant change, or the Wilcoxon Matched-Pair Signed Test of Differences... I think (though I'm not sure) that a non-parametric solution is best as the data starts with it's highest peak, and then tapers off.

I would really appreciate any suggestions. THANKS!


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