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Practice JMP using these webinar videos and resources. We hold live Mastering JMP Zoom webinars with Q&A most Fridays at 2 pm US Eastern Time.See the list and register. Local-language live Zoom webinars occur in the UK, Western Europe and Asia. See your country jmp.com/mastering site.

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Building Linear Mixed Models using JMP® Pro 15

Use  JMP Pro 15 to Handle Random and Fixed Effects, Repeated Measures, Random Coefficients and Variance Components

 

 

See how to:

  • Handle Random and Fixed Effects
  • Analyze a wide variety of linear mixed models easily and effectively
  • Analyze split-plot designs using an example that studies the effects of four process conditions on resistance in wafers
    • Understand how fitting Standard Least Squares can lead to incorrect inferences
    • Understand when JMP Pro 15 mixed model scripts are useful for split-plot designs
  • Analyze Repeated Measures, using an example that accounts for correlation between observations, to effectively understand treatment effects
    • Consider and compare alternative covariance structures and choose the best fit
  • Perform Individual Growth Analysis using Random Coefficients and an example that seeks to determine if any of three treatments had an effect on a growth response
  • Perform Variance Components Analysis using a measurement system study example

NOTE: Q&A is included at times 37:52 and 53:21.

 

Resources:

Comments

@jiancao I really appreciated this webinar, a couple of questions.

 

Can you give a brief explanation why the mixed model personality is better than Standard Least Squares with REML, even in instances when they produce nearly identical results.

 

You mentioned that you knew that the random effects did not account for a significant portion of the variance because the confidence intervals crossed the zero mark. I'm assuming that is saying that the variance component is close enough to zero that it is not significant. Is that a correct interpretation, and are there other metrics that communicate a similar idea.

jiancao
  1. Mixed Model provides Conditional Model Inference reports including Conditional Profiler and Conditional Residual Plot. It also allows you to add a covariance structure to account for within-subject variability (e.g., RE+AR(1)).
  2. Correct.  JMP reports Wald p-values for the random effect estimates.

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