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• Query regarding design using categorical factors

  JMP student edition 18
  I was trying a custom design with 2 categorical factors and 3 levels. All effects -main and interaction show non significant p value despite using the recommended 9 run by the software.

  Manisha

  Discussions |

  Aug 6, 2025 5:59 AM

  319 views | 1 replies

• Control Chart with subgroups entering the project at different times

  We are created a process across 9 different divisions. All the Divisions did not start at the same time. As other Divisions learned of the initial cohort success, they joined the project. In aggregate, by December 2023 we were consistently achieving 93% success. We created a control chart using the Numerators and denominators of all the Divisions participating in each month. Therein lies the ...

  RatioMegalodon8

  Discussions |

  Aug 4, 2025 11:45 AM

  701 views | 4 replies

• If you fit a block as a fixed effect in a saturated model, and its effect is not significant after you reduce it via stepwise regression, do you need to add it back to the model?

  This was posted to the JMP blog yesterday where Phil Kay describes reducing a saturated model with a block effect. He says the random block should be added as a fixed effect 1st in this specific case, then after you reduce the model via stepwise regression, you should re-fit the reduced model & add the block as a random effect. In his example the block was significant after he reduced the model. I...

  mjz5448

  Discussions |

  Jul 30, 2025 12:26 PM

  1504 views | 7 replies

• Encoding of categorical variables

  Hi everyone, I'm interested in an *easy* recoding of categorical variables in JMP. Depending on the variables I want to do this to be able to specify which levels should be compared to each other. If I include all levels in the model I want to get p-values for exactly the differences I want to see or I want to do a model selection and just include certain differences of levels. In these cases the ...

  Anja_W

  Discussions |

  Jul 25, 2025 3:32 PM

  1991 views | 4 replies

• Prior parameter variance

  Hi,   I am trying to understand the Prior Parameter Variance option for the Bayesian D-optimal design. JMP help says: "The value that you enter is the square root of the reciprocal of the prior variance. A larger value represents a smaller variance and therefore more prior information that the effect is not active." I am a bit confused, since I thought that if I enter larger value, that would info...

  aatw

  Discussions |

  Jul 23, 2025 10:51 PM

  482 views | 2 replies