Summary: 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.
Duration: 14 hours of content.
Modalities:
- on-demand -- This course is available as free on-demand e-learning.The course is currently only available in English, but local-language translation for subtitles is in planning. Enroll Now!
- live online with instructor -- This course is also available periodically in our public course schedule. The public courses are an opportunity to learn this content with a live instructor, but they are currently only offered in English and at times most convenient to a US audience (because most of our instructors are in US time zones). Don't see what you are looking for? Let us know.
Prerequisites: Before attending this course, it is recommended that you complete the Getting Started with JMP: On Demand and JMP®: Statistical Decisions Using ANOVA and Regression courses or have equivalent experience.
Learning Objectives:
- Use custom design for any experiment.
- Choose appropriate criteria for optimal design.
- Effectively and efficiently test factor effects or predict responses.
- Augment existing experiments to address new questions.
- Design and analyze experiments with hard-to-change factors.
- Find the best factor settings to achieve desired response levels.
Course Outline:
Introduction
- Introduction to design of experiments.
- Introduction to custom design.
- About responses.
Experiments with One Factor
- Testing a categorical factor.
- Testing a continuous factor.
- Validating the model.
- Testing a quadratic effect.
Experiments with Two Factors
- Testing two categorical factors.
- Testing one continuous factor and one categorical factor.
- Testing with one blocking factor.
- Testing two continuous factors.
Experiments with Multiple Factors
- Two responses to five factors.
- Three responses to seven factors.
