Note: All the capabilities shown are in JMP 17, although JMP Pro 17 is used for the demo.  The video includes Q&A and comments from the presenter Caleb King @calking about how MSA works plus plans for possible enhacements. 

See how to:

• Understand basics of MSA Design

  • MSA primary focus is on decomposing variation in product measurements into distinct sources, with the goal to reduce or elimnate one or more sources

  • The metric of interest is not an effect size (i.e. difference in mean behavior), but rather the variance contribution from a particular source

  • Underlying models are primarily random effects models or even mixed effects models if biasing terms are included

  • Nested factors are also common in MSA, but not exclusively 

• See improvements made in JMP 17

  • Better diagnostics that more closely align with MSA analytics platforms

  • Greater user interaction that allows exploration of what-if scenarios

  • Fexible Design structure interface so  users can specify nesting/crossing relationships in the design

• Understand that diagnostics are based on simulation of variance estimates, based on the standard ANOVA approach

  • Example is a 3- factor fully crossed MSA where mean squares have known Chi-square distributions that are easily be simulated and then used to compute confidence intervals, variance proportions, etc.

• Include nested and crossed structures
• Make design and evaluate diagnostics for two examples

MSA Design Factor ScreenMSA Design Factor Screen

The video includes Questions ansered at the webinar by Caleb @calking, Don McCormack @DonMcCormack and Jerry Fish @JerryFish.

One person asked online if nesting would occur when testing same methods on different equipment.  They responded: Probably not, but it would ultimately depend on how the experiment was run what assumptions were made about the methods and equipment.  If you considered the equipment/method to be a random observation from a much larger population, then they may be considered nested (in some way). If you considered them from a fixed set of values (e.g., types of equipment), then they would more likely be treated as crossed.

Resources

• Caleb's blog and interview
• More about Measurement Systems Analysis in JMP 
• More about using EMP in JMP