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gail_massari
Community Manager Community Manager

Using Blocking When Designing Experiments

 

 

See how to:

  • Understand the restrictions associated with randomization, the difference between blocking and creating plots, and how to incorporate covariates into the design (1:26)
    • Blocks are about variance and let you group homogeneous factors to isolate extraneous noise associated with the factors (like time of day)
    • Plots address variability and let you organize units so you can estimate the noise/signal associated with factors
    • Incorporating covariates (uncontrolled factors related to the experiment and known before the experiment) lets you asses the impact of the related factors
  • Understand the factors related to the widget case study where manufacturers add hardening agent to slurry, stir it, control viscosity, mold it and then bake it (7:06)
  • Use Custom Designer to incorporate differences related to differences related to the day the experiment runs (8:55)
    • See how to group runs into blocks to estimate variability from day to day (Random Effect blocks)
    • See how to add Blocking Factor to calculate mean differences from day to day (Fixed Effect blocks)
    • Understand the impact of choosing Random vs. Fixed Effects on the number of runs needed for the design.
    • Understand why to choose a Split-Plot design to get estimates of signal plus noise
  • Use Custom Designer to build Split-Plot design that includes statistics related to interactions between hard-to-change and all other factors (18:28)
  • Compare incorrect and correct designs (27:00)
    • Examine Fixed Effect Test results
  • Understand and compare Split-Split-Plot and two-way Split-Plot (Strip Plot) Designs (30:16)
  • Add information about a related factor (covariate) to the model (38:43)
    • Handle mis-match between number of covariate observations and number of runs needed

Note: Includes Q& A at times 36:30, 43:53. 44:49 and 45:16.

 

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Use Videos and Resources to Practice JMP, JMP Pro, JMP Clinical and JMP Genomics.

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