This course is for JMP users who deal with mixture or formulation experiments. The course demonstrates how to use various approaches to create an appropriate experimental design for commonly encountered mixture situations. The analysis of mixture experiments is also covered, including finding the optimum formulation.

Learn how to

• Use factorial-type designs in mixture scenarios.
• Design mixture experiments using traditional and advanced design strategies.
• Properly analyze and interpret formulation experiments.
• Characterize response behavior over the formulation space with a model.
• Find optimal formulations.

Who should attend

Individuals who work with formulations and need to plan their experiments and analyze the results

Formats available	Standard Duration
Classroom:	2 days
Live Web Classroom:	4 half-day session(s)	System Requirements

Before attending this course, you should complete either the JMP® Software: Classic Design of Experiments course or the JMP® Software: Custom Design of Experiments course.

This course addresses JMP software.

Introduction to Mixture Designs

• Review of experimental design principles.
• Using factorial designs for mixture scenarios.

Mixture Scenarios without Constraints for Linear Responses

• Define the Scheffé model for the linear response.
• Use simplex-lattice designs and simplex-centroid designs for mixtures with linear response.
• Use custom designs for mixtures with linear response.
• Analyzing mixture experiments.

Mixture Scenarios with Constraints for Linear Responses

• Accommodate constraints within and among mixture components.
• Use pseudo-component coding to reduce imposed collinearity.
• Use custom design for mixtures with constraints.

Mixture Designs for Nonlinear Response

• Define the Scheffé model for the nonlinear response, including cubic models.
• Augment initial design for mixtures with nonlinear response.
• Use simplex-lattice design, simplex-centroid design, and ABCD design.
• Use custom design.

Advanced Mixture Scenarios

• Define and interpret the Cox model.
• Combine process factors with mixture components in the same design.
• Design experiments for "mixtures within mixtures."
• Use and interpret Snee screening simplex design.
• Study space-filling designs for mixture experiments.