I am such a novice in JMP and statistics. I have basic questions for experts here.
The data I have is product dimension. I have ‘min’ dimension data, and ‘max’ dimension data around 30 points for each. And with these data, I need to propose to team min and max dimension specification.
In JMP11, I used Analyze>Distrubution and found most of the data are slightly skewed, and there are some with a few outlier points. I checked continuous fit – normal – fitted normal - goodness of fit: which shows Prob<W less than 0.05.
Traditionally I was told that we just take mean/average then +- 3 STDEV, which I don’t find it appropriate in this case.
If I could be pointed out proper analysis of my data, and how I should be setting specification, or JMP article I should read, I would appreciate very much.
Thank you,
Nan
Nan, an alternate approach is to use the data to create a control chart for each potential specification item. The published specification [e.g. to an internal or external customer] is then based on +/- 3 Sigma [Hence 6 Sigma quality].
Update the chart as new data becomes available and use the charts to not only set specifications but to, more significantly, control the process.
Thank you very much for your suggestion.
Nan, I like to use tolerance intervals for helping to set spec limits. The online (NIST/SEMATECH e-Handbook of Statistical Methods) engineering stats book has a section (end of chapter 7) on both tolerance intervals based on normal distributions and a non-parametric version. I would start there and then you can explore further. You are correct to note that using the mean +/- 3 standard deviations might result in spec limits that you can meet given you might be looking at skewed distributions.
Thank you very much for your suggestion Karen. And the e-Handbook looks very useful.