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 .
Resources
31+ minBasicDOEMastering JMPVideo
