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  • Learn how to build custom Python data connectors and further customize JMP’s Data Connector Framework with the Python Data Connector Demo, available now in the JMP Marketplace!
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JMP Blog

A blog for anyone curious about data visualization, design of experiments, statistics, predictive modeling, and more
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Summer DOE Series: Advanced Topics

JMP continues to push the boundaries of what is possible with DOE in new capabilities and platforms. This session focuses on two of those capabilities: functional DOE and self-validating ensemble models (SVEM). Functional data is two-dimensional data, such as a kinetics curve, heat evolution over time, particle size distribution, or a chemical spectrum. A functional DOE allows you to optimize that entire response curve (instead of a single parameter). Self-validating ensemble models (SVEM) allow you to run even fewer experiments in your design through an advanced modeling algorithms.

(view in My Videos)

 

Q&A / Discussion

Q: Is functional data analysis in standard JMP? 

A: No, functional data analysis is a feature only in JMP Pro.

 

Q: How do you create a target curve?
A: The target curve can be created based on prior results or a theoretical target that you manually create.

 

Q: How do you make virtual links between data tables?

A: You can make virtual joins by using Link ID and Link Reference Column Properties.

Last Modified: Sep 7, 2023 9:47 AM