Developer Tutorial: Mixed Models Part 1 - A Critical Tool When You Have More Than One Source of Variation 

Application Area:
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.

Mixed models are one of the most powerful ways to handle correlated observations in designed experiments. They often go by other names, including blocking models, variance component models, nested and split-plot designs, hierarchical linear models, multilevel models, empirical Bayes, repeated measures, covariance structure models and random coefficient models.

Reaching well beyond standard linear models, mixed models enable you to make accurate and precise inferences about your data and to gain deeper understanding of sources of signal and noise in the system under study. Well-formed fixed and random effects generalize well and help you make the best data-driven decisions.

In this session you will learn the rationale and techniques behind JMP’s mixed model capabilities, see when and how to use mixed models, learn how to recognize, set up, and interpret fixed and random effects and see how to extend Analysis of Variance (ANOVA) and linear regression to numerous mixed model designs.

This webcast covers: Understanding and using degrees of freedom; analyzing randomized block, split-plot designs and random coefficient models; addressing modern dilemmas around Bayesian methods and p-values.