# DoE Analysis Help

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This topic contains 4 replies, has 4 voices, and was last updated by Kristen Hill 9 months, 4 weeks ago.

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- May 17, 2018 at 7:51 pm #56003

Mike ScullyParticipant@mscully26**Include @mscully26 in your post and this person will**

be notified via email.I am performing a DoE and I have responses that are dimensions with specs. How should I analyze this data? Do I just enter the data as is or as ‘in/out of spec’? It’s been a long time since I have messed with minitab so please forgive my ignorance. Thanks for any help I can get.

0May 18, 2018 at 10:04 am #202560

Robert ButlerParticipant@rbutler**Include @rbutler in your post and this person will**

be notified via email.You could analyze it both ways – it would depend on what you want to do but before you try anything there are a couple of things you might want to consider.

1. You say that your responses are dimensions which presumes you have taken multiple measures of each dimension of interest at each experimental condition. This, in turn, probably means that you built multiple items for each of the experimental conditions and took measurements off of each of the items. If this is the case then your measurements are repeated measures – not independent measures and you will have to take this into account when running your analysis.

2. If you choose to run the DOE on in/out spec responses you will have to use logistic regression and your coefficients will be odds ratios which will express the odds of getting defects for certain combinations of experimental conditions. You will need more data for a logistic regression in order to detect significant differences (as compared to running an analysis on actual measurements) and you will still have to take into account the repeated measures nature of the data.

3. Another possibility is this – it sounds like you are more concerned with in/out spec issues than mean responses. Since your post gives the impression that you ran a full factorial you might want to become familiar with the Box-Meyer’s method for data analysis. If you analyze your DOE data using this method you will be able to identify the variables that are contributing the most to your observed measurement variation and thus are most likely to contribute to the occurrence of out of spec material.

If #3 is what you are really trying to do then get a copy of Understanding Industrial Designed Experiments – Schmidt and Launsby – 4th edition through inter-library loan. Sections 5-28 to 5-31 has the details for running a Box-Meyer’s analysis.

As for the issue of repeated measures I don’t know Minitab but I seem to recall from other threads on this site that Minitab can handle repeated measures – your best bet would be to check their website and/or talk it over with their support people.

0May 21, 2018 at 7:30 am #202566

Kraig Swogger@kswog1**Include @kswog1 in your post and this person will**

be notified via email.Just to add, I assume that dimensions with specs, means actual measurements. I’d lean toward the that “continuous” data rather than the more discrete (in/out) which moves you to a logistic regression. The more continuous your data, the better the tools, the more information you get from the analysis and the easier the analysis will be.

0May 22, 2018 at 7:54 pm #202572

Chris SeiderParticipant@cseider**Include @cseider in your post and this person will**

be notified via email.One of the first things taught in DOE usage is design considerations including the Y’s. Hope the DOE wasn’t expensive to execute since you may find you need to learn more by measuring differently.

0May 29, 2018 at 7:58 am #202599

Kristen HillParticipant@misskristen**Include @misskristen in your post and this person will**

be notified via email.It generally would be a more powerful analysis with less data if you use continuous data. If there is an ideal result (e.g. x mm diameter), then you can measure how far off the ideal each piece is. That gives you far more information than Yes/No for In/Out of spec per piece. If you use In/Out of spec then you will need to get many more data points per setup to have enough data to find anything useful from your logistic regression. With continuous data measurements, you could do more different setups and run fewer pieces at that setup to get the data you need.

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