Developer Tutorial: Using JMP Pro Generalized Regression to Better Understand Observational Data

Observational data lends itself to analysis useful for large data sets, data that probably do not exhibit orthogonality and situations where we are interested in prediction rather than interpretation. The Generalized Regression platform is the place to build regression models for both designed experiments and observational data. Some things to keep in mind:

1) Penalized regression shows great promise for observational data.

2) Effect Heredity probably isn’t necessary.
3) Use a holdout set if you have enough data, otherwise the AICc.

See how to:

• Understand background principles related to estimation and the needs to consider:
• How far from the truth the estimates tend to be (Bias)
• How variable the estimates are (Variance)
• How bias and variance principles combined can be measured by Mean Square Error of an estimator
• Understand Penalized Regression techniques (Ridge, Lasso, Elastic Net, Adaptive Lasso, Double Lasso, Dantzig Selector)
• See how Effect Heredity is handled in JMP Pro
• Choose Grid Control options
• Use Forced Terms, Early Stopping and Informative Missing

Resources

• Ridge Regression: An Essential Concept in Data Science, Trevor Hastie, 30 May 2020, Technometrics
• LASSO (Least Absolute Shrinkage and Selection Operator) documentation
• JMP Pro Generalized Regression example
• Analyzing Spectral Data: Preprocessing and Beyond, Bill Worley, Discover Summit 2021