Developer Tutorial: Mixed Models Part 2 - Handling Repeated Measures in Time and Space
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 second session, you will understand the rationale and techniques underpinning JMP’s mixed model repeated measures capabilities; and learn when and how to use repeated measures to account for correlation between observations and accurately assess treatment effects.
This webcast covers: Building models incorporating repeated measures where the model reflects changes in a response over time or space while allowing errors to be correlated; analyzing repeated measures designs; and simulating mixed models.