Developer Tutorial: Selecting the Appropriate JMP Pro Generalized Regression Distribution for Your Response
Statistics, Predictive Modeling and Data Mining
This session is for JMP users who understand basic predictive modeling principles and have used JMP for predictive modeling.
Often observational data is gathered without involving the subject of the research or the data analyst. Such data can present analysis problems such as missing key factors, selection bias, multicollinearity, and outliers.
Analyzing and building predictive models for correlated and high-dimensional data requires using variable selection techniques to select a subset of variables (predictors) to use in modeling a response variable. Shrinkage techniques like Lasso and Elastic Net are especially promising for avoiding overfitting observational data.
JMP Pro Generalized Regression is useful for many modeling situations that include and go beyond variable selection, or when you suspect collinearity in your predictors. It also lets you specify a variety of distributions for continuous, binomial, count, or zero-inflated responses and when you want to fit models that you compare to models obtained using other techniques.
In this session, you will gain a better understanding of Generalized Linear Models, how to determine if your data are normally distributed and how to identify if a skewed distribution would be appropriate. You will learn strategies for choosing a distribution for your response so that you can make sound decisions about your data.
This JMP Developer Tutorial covers: Generalized Linear Models: Logistic and Poisson regression; Information criteria; Time to Event data.