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JMP® Education

Design of Experiments and Quality Improvement

JMP®: Design and Analysis of Mixture Experiments

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.

Duration: 4 half-day sessions

Registration Fee: $400 US

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.

Before attending this advanced course, you should complete the JMP®: Classic Design of Experiments or JMP®: Custom Design of Experiments course or have equivalent experience.

This course is not an introduction to design of experiments.

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.
Course Dates

This course is not available at this time. Please check back at a later date.

Don’t see a course you want listed on the dates that you need? Contact us at training@jmp.com.