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gail_massari
Community Manager Community Manager

Designing Mixture Experiments - Part 2

 

 

See how to:

  • Choose model types for your mixture experiment 
    • Understand the difference between, and impact of choosing, linear and cubic models
    • Understand why defining more interactions increases the number of required runs
  • Understand the importance of JMP automatic randomization
  • Understand the impact of choosing minimum runs vs. default number of runs
  • Run and compare Linear and Scheffe Cubic models
  • Preview design, choose options (simulate responses, include run order) and then run design
    • Use Prediction and Mixture Profilers to find optimal settings for responses relating to your goals, and then interpret and compare Mixture Profiler results and use Prediction Profiler
  • Optimize using Prediction Profiler
    • See how to optimize for two responses
  • See how to design an mixture experiment that includes process variables
    • See how to lock requirements and maximize desirability for locked requirements
    • Understand and apply inear constraints or the disallow combinations

Note: Q&A is included at times 19:32, 19:45, 20:42, 37:54 and 39:07 .

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