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Introduction to Design of Experiments

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.


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

Dr. Jim Rutledge

Dr. Jim Rutledge is currently the President of Data Vision, a company that performs statistical consulting and training. Dr. Rutledge has over fifteen years of teaching and consulting experience. He specializes in teaching powerful statistical tools to non-statisticians; he has instructed over 1000 scientists, engineers, managers, and college students. Previously, he served as an Assistant Professor at the United States Air Force Academy and has extensive research and consulting experience in healthcare issues. Dr. Rutledge was recently invited by the National Academy of Sciences to give a presentation on Design of Experiments to Biomedical Engineering Materials and Applications members. Dr. Rutledge is an ASQ Certified Quality Engineer and served as President of the Colorado-Wyoming Chapter of the American Statistical Association.

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


02/09/2024 to 03/08/2024
Instructors: Dr. Jim Rutledge
08/16/2024 to 09/13/2024
Instructors: Dr. Jim Rutledge


02/07/2025 to 03/07/2025
Instructors: Dr. Jim Rutledge
08/15/2025 to 09/12/2025
Instructors: Dr. Jim Rutledge


Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

Frequently Asked Questions

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Introduction to Design of Experiments

Additional Information

Time Requirements



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.


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  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

At, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:







  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

Register For This Course

Introduction to Design of Experiments