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Developer Tutorial: Mixed Models Part 2: Handling Repeated Measures in Time and Space

Published on ‎11-07-2024 03:30 PM by Staff | Updated on ‎11-07-2024 05:40 PM

This technical presentation includes examples presented in JMP for Mixed Models. Data from the book used in the presentation is included. The video is over 90 minutes long with questions interspersed throughout. This tutorial and examples of regression, random coefficient and multi-level models all require JMP Pro. Part 3 will be scheduled for early 2022 and will cover assessing power and precision for mixed models.

 

Mixed Models Part 2.mp4
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      Mixed Models are useful when you have multiple predictors, for example a factorial treatment with or without interactions, when there is curvature in the response over the predictor, and where multiple subjects are measured. 

       

      See how to:

      • Analyze repeated measures and longitudinal data in a pharmaceutical stability trial example
        • Address lack of independence of observations made on the same subject over time

        • Assess covariance and correlation between time points to select candidate structure

        • Compare Source and Degrees of Freedom for Experiment Design, Treatment design and Skeleton ANOVA
        • Understand the stats (model) behind the Skeleton ANOVA
        • Build the model and include interactions
        • Understand and evaluate Compound Symmetry, Auto-Regressive Correlation Matrix, Toeplitz and Antedependent Correlation Matrices
      •  Model correlations in space (Spatial Models)
        • Understand spatial model terminology in a hazardous waste example
          • Variogram – graphical display of the semivariance as distance increases

          • Nugget – “jump” in semivariance at small distances

          • Sill – plateau of the semivariance

          • Range – distance to the Sill

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