I am working on a mixture design in JMP with four continuous factors and four responses. While I have made progress in modeling and evaluating individual responses, I am finding it challenging to analyze all responses collectively. Specifically:
- The ternary plots are limited to visualizing only three mixture components at a time, which makes it difficult to understand the behavior of all four factors simultaneously.
- Some of my responses suggest conflicting trends, requiring a "middle ground" approach for optimization.
- The prediction profiler, though helpful, doesn't fully address the complexity of combining all responses for a comprehensive analysis.
- Instead of identifying a single optimal value for each factor, I aim to define smaller, practical ranges for each compound. These ranges will be used in subsequent analyses where evaluation will consider additional responses.
Here is what I have already done:
- For each response, I retained only the significant effects that reduced RMSE.
- I examined the residuals and lack of fit, finding no evident trends or issues.
I am looking for the best approach or tools within JMP to analyze this mixture design effectively, particularly in synthesizing insights across all responses and identifying meaningful ranges for each factor. If you could point me toward relevant strategies or documentation, I would greatly appreciate it.
Thank you in advance!
