595599_en-US
"JMP®: Classic Design of Experiments" Course Homepage
Summary: This course teaches you how to design and analyze experiments in JMP to find the vital few factors or optimize the process response. The course emphasizes the principles of experimental design while demonstrating classic approaches to screening designs and response surface designs.
Duration: 7 hours of content.
Modalities:
- on-demand -- This course is available as on-demand e-learning. Right now, this can be accessed for a small fee through the JMP Learning Subscription. Soon (later in 2023), this on-demand course will become available for free in the JMP User Community and with some local-language translation for subtitles.
- live online with instructor -- This course is NOT available in our public course schedule. (The Custom Design of Experiments class is a better class for most people. This Classic DOE course is generally only of interest to academics and to researchers who need only these Classic methods, not the more flexible Custom methods.) If you want to see this course added to the public schedule, let us know.
Prerequisites: Before attending this course, it is recommended that you complete the JMP® Software: A Case Study Approach to Data Exploration and JMP®: Statistical Decisions Using ANOVA and Regression courses or have equivalent experience.
Learning Objectives:
- Explain the fundamental principles of designed experiments
- Generate and analyze full and fractional factorial designs
- Generate and analyze classic response surface designs
- Generate and analyze blocked and split-plot designs
- Generate custom designs and augment existing designs.
Course Outline:
Introduction to Design of Experiments
- Review of basic statistical concepts (optional)
- Introduction to experimental design
- Completely randomized designs
Multiple Factor Designs
- Full factorial designs
- The 2k factorial design
- Fractional factorial designs
Response Surface Methodology
- Concepts and terms
- Classic response surface designs
Blocked and Split-Plot Designs
- Randomized complete block designs
- Randomized incomplete block designs
- Split-plot designs
Introduction to Custom Design
- Custom design generation
- Optimal augmentation
Design of Experiments
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