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#### drew88

Hello,

New to the forum. I tried reading up on related topics but I can't seem to wrap my head around this. Hope someone can help me! Sorry if I have butchered the world of statistics because I am trying to learn as I go and it's just too confusing...

I have 6 sets of pull force data of plastic heat staked posts (destructive tensile testing). Understanding that I will have a lot of difficulty justifying that the measurement system is reliable, we needed to go ahead and test this in some way to establish some level of process/product compliance to specification. Each set of data represents a specific location on the same processed part (6 posts per part).

Started by running basic capability/histograms and found 3 & 5 are not normal. Seems that the best fit (non-transformation) is 3 parameter weibull. Since all 6 posts should be the same, I thought I would perform the non-normal analysis uni formally.

This generally makes all the Ppk values go up. In 3 cases, the p-value goes up considerably which means it is a better fit? In 3 other cases, normal distribution has a better p-value. Does this mean that Weibull can not be used or that it also fits with a lower confidence?

As far as interpretation of what these capability indices mean, does it matter that I have a single process represented by Weibull distributions with different parameters? I feel like this defeats the whole purpose of characterizing the statistical model.

Also, for my understanding and curiosity, why would I not always characterize a model (even if it is close to normal) as Weibull if the data can be more closely characterized by the 3 parameters?

Data attached for reference. Any help would be greatly appreciated.

Thank you in advance!

Andrew

New to the forum. I tried reading up on related topics but I can't seem to wrap my head around this. Hope someone can help me! Sorry if I have butchered the world of statistics because I am trying to learn as I go and it's just too confusing...

I have 6 sets of pull force data of plastic heat staked posts (destructive tensile testing). Understanding that I will have a lot of difficulty justifying that the measurement system is reliable, we needed to go ahead and test this in some way to establish some level of process/product compliance to specification. Each set of data represents a specific location on the same processed part (6 posts per part).

Started by running basic capability/histograms and found 3 & 5 are not normal. Seems that the best fit (non-transformation) is 3 parameter weibull. Since all 6 posts should be the same, I thought I would perform the non-normal analysis uni formally.

This generally makes all the Ppk values go up. In 3 cases, the p-value goes up considerably which means it is a better fit? In 3 other cases, normal distribution has a better p-value. Does this mean that Weibull can not be used or that it also fits with a lower confidence?

As far as interpretation of what these capability indices mean, does it matter that I have a single process represented by Weibull distributions with different parameters? I feel like this defeats the whole purpose of characterizing the statistical model.

Also, for my understanding and curiosity, why would I not always characterize a model (even if it is close to normal) as Weibull if the data can be more closely characterized by the 3 parameters?

Data attached for reference. Any help would be greatly appreciated.

Thank you in advance!

Andrew