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New DOE Features in JMP 14

New DOE Features in JMP 14. Bradley Jones Principal Research Fellow JMP Division. New Features List. Balanced Incomplete Block Designs (BIBD) A-optimal designs Weighted A-optimal designs Compare Designs – compare more designs DSD enhancements. BIBD. Kudos to developer Joseph Morgan.

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New DOE Features in JMP 14

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  1. New DOE Features in JMP 14 Bradley Jones Principal Research Fellow JMP Division

  2. New Features List Balanced Incomplete Block Designs (BIBD) A-optimal designs Weighted A-optimal designs Compare Designs – compare more designs DSD enhancements

  3. BIBD Kudos to developer Joseph Morgan.

  4. BIBD Ryan Lekivetz gave a talk on these this morning. I will give a one slide Executive Summary

  5. BIBD – Balanced Incomplete Block Designs A BIBD is an experiment with one categorical factor and one blocking factor. Blocks are incomplete because there are fewer runs in a block than the number of levels of the categorical factor. Balance in BIBDs Every level of the categorical factor appears the same number of times. Every level of the categorical factor appears with every other level the same number of times.

  6. New Features List Balanced Incomplete Block Designs (BIBD) A-optimal designs Weighted A-optimal designs Compare Designs – compare more designs DSD enhancements

  7. What is A-optimality? An A-optimal design minimizes the average variance of the parameter estimates for the model. The way to remember is that “A” stands for Average

  8. Why add A-optimal designs? The A-optimality criterion is easy to understand. The A-optimal criterion (unlike other criteria) allow for putting different emphasis on groups of parameters through weighting giving us Weighted A-optimal designs.

  9. Demonstration Examples Four factor, 12 runs – D-optimal vs. A-optimal all 2FIs Four factor RSM – 25 run weighted A-optimal vs. Box-Behnken Five factor, 20 runs all 2FIs – D-optimal vs. Weighted A – weighted design puts emphasis on Main Effects Three factor, 16 runs RSM – I-optimal vs Weighted A – weighted design puts emphasis on quadratics (i.e. xi2)

  10. JMP Demo

  11. New Features List Balanced Incomplete Block Designs (BIBD) A-optimal designs Weighted A-optimal designs Compare Designs – compare more designs DSD enhancements

  12. Compare Designs Now allows up to 5 designs through the UI Allows up to 10 designs through scripting Example: Four factor RSM – Weighted A-optimal vs. D-optimal vs. A-optimal vs. I-optimal The weighted design puts emphasis on quadratics (i.e. xi2)

  13. JMP Demo

  14. New Features List Balanced Incomplete Block Designs (BIBD) A-optimal designs Weighted A-optimal designs Compare Designs – compare more designs DSD enhancements

  15. DSD Enhancements Relax heredity assumption for quadratic effects Relax heredity assumption for interaction effects More meaningful marginal effect plots

  16. JMP Demo

  17. Finally, … The Accelerated Life Test (ALT) designer has a much improved interface.

  18. And one last thing … If you have JMP Pro, the Covering Array designer represents the state of the are for both design and analysis of experiments for testing software and other complex systems with no random components. Each development cycle this designer gets faster and improves its ability to generate covering arrays with fewer runs.

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