Introduction to Design of Experiments
This course will teach you the application of DOE rather than statistical theory, and teaches full and fractional factorial designs, Plackett-Burman, Box-Behnken, Box-Wilson and Taguchi designs.
Overview
This course will teach you how to use experiments to gain maximum knowledge at minimum cost. For processes of any kind that have measurable inputs and outputs, Design of Experiments (DOE) methods guide you in the optimum selection of inputs for experiments, and in the analysis of results. Full factorial as well as fractional factorial designs are covered.
- Introductory, Intermediate
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
At the conclusion of this course you will be familiar with the foundations of experimental design. You will learn about interactions, coding and randomization, how to choose appropriate designs, and how to conduct experiments and analyze your results.
- Explain the key concepts of DOE, and why it is used
- Calculate treatment effects
- Produce plots from the results of experiments
- Specify fractional and full factorial designs
- Specify specialized designs, e.g. Taguchi, Box-Wilson, others
- Use Excel-based software to design experiments and analyze data
Who Should Take This Course
All six-sigma practitioners, scientists, engineers, and technicians who are interested in performing experiments that maximize process knowledge with a minimum amount of resources. Managers who are responsible for delivering products “on time” and “on budget” will also benefit from this course by learning what their employees should be doing. This course will stress the application of DOE rather than statistical theory. While design of experiments has been very successfully applied in research and development, that is not the only application. The techniques presented also apply to manufacturing, quality control, and even marketing.
Our Instructors
Dr. Jim Rutledge
Course Syllabus
Week 1
Foundations of DOE
- What is experimental design
- Why use DOE
- Measure of quality (Cp Cpk, dpm)
- DOE key concepts
- Interactions
- Coding
- Confounding/aliasing
- Robustness
- Randomization
Week 2
Simple Designs and Their Analysis
- DOE 12-step checklist example
- Calculating effects
- Interaction plots
- Marginal means plot of effects
- Pareto chart of effects
- Prediction equations
- Using Excel based DOE KISS software
Week 3
Design Types
- Full factorial designs
- Fractional factorial designs
- Design resolution
- Aliasing pattern
- Fold-over
- Plackett-Burman designs
- Box-Behnken designs
- Box-Wilson (central composite) designs
- Taguchi designs
Week 4
Practice Conducting and Analyzing Experimental Data
- Multiple regression
- Normal probability plot
- Importance of analyzing interactions
- Taguchi’s signal to noise ratios
- Variance reduction analysis
- Practice planning, executing, and analyzing an experiment
Class Dates
2025
Instructors: Dr. Jim Rutledge
Instructors: Dr. Jim Rutledge
Prerequisites
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Additional Information
Time Requirements
15
Homework
Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, and guided data modeling problems using software.
In addition to assigned readings, this course also has discussion tasks, and an end of course data modeling project.
Course Text
Understanding Industrial Designed Experiments by Schmidt et al is available as an e-book, or hard cover from Amazon.
Software
The course makes use of Quantum XL, an add-in to Microsoft Excel. A 30-day trial version of the add-in can be downloaded from www.sigmazone.com. The add-in should function with Excel 2002 and above, note however, the course notes are written with examples from Excel 2010.
Note: Do not start your trial prematurely – you’ll need it throughout the 4-week course.
Supplemental Information
Literacy, Accessibility, and Dyslexia
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