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Custom Design of Experiments Course

Started ‎07-05-2023 by
Modified ‎01-19-2024 by
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custom-design-of-experiments.zip

We’re excited to bring you this free e-learning course on Custom Design of Experiments. This course focuses on the core principles of designing an experiment, enabling you to understand and apply those principles to achieve an optimal design using the Custom Design platform in JMP.

 

Custom design is an approach to designing experiments that produces optimal designs for the problem you’re trying to solve, whether that’s identifying important effects, or trying to optimize one or more responses. In addition to learning about custom design in JMP, you’ll explore key design concepts including sample size and power, balance, choice of factor ranges, blocking, and design evaluation. This course is for anyone who works in discovery, research, development, and quality assurance or control.

 

The demonstrations in this course were recorded in JMP 14. If you are using JMP 15 or higher, you might notice some changes to the interface, such as additional menu items or options in dialogs. These changes do not affect the course content and should not affect your ability to follow along with the course. You can go to Help > New Features to view a PDF of the new features and enhancements.

 

Download the course file (custom-design-of-experiments.zip) at the top of the page to use in the course.

 

Please send any feedback about the course to Ruth.Hummel@jmp.com with the title “Feedback on Custom DOE course”.

 

 

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Comments
iKnowVeryLittle
‎11-05-2024 02:39 PM

Trying to understand the reasoning behind the supplier coefficient being 10.

monica_beals
2 weeks ago

Hi @iKnowVeryLittle, thanks for your question! In this example, we want to know the power of our experiment to detect a change in the breaking load from 30 lbs (the average for Acme) to 50 lbs (the minimum breaking load required). So we're looking for an effect of 20 pounds. The anticipated coefficient for the power analysis is entered as half of the expected effect size, so that's 10 here. The video "Scale Invariance" in the "Testing a Continuous Factor" section explains this in the context of continuous factors, but it applies to Supplier as well since there are only two levels.