Design of Experiments (DOE) I: Introduction to DOE

  • Overview
  • Course Content
  • Requirements & Materials

Design of Experiments (DOE) I: Introduction to DOE

Course Description

A properly designed experiment should be efficient, informative, and directional. Sadly, technical professionals are almost never taught the rigorous techniques of experimentation that allow them to make informed, statistically meaningful decisions. This course introduces students to the long-lost technique of factorial experimentation where, upon course completion, a student will be almost certain to exclaim, “why was I never taught this in school?!”

In this course, you will learn how to efficiently derive a mathematical representation of a complex system that can be used to inform you about system behavior, predict outcomes with statistical confidence, and direct innovation in a meaningful way.   

In addition this course will provide hands-on experience through Statapults, simulations, and case studies. A brief introduction into specialized DOE software will also be used as a means to gain experience into the tools DOE practitioners use to build and analyze their experiments.

Due to minimum enrollment requirements to hold a course, we ask that you register as early as possible. View our terms and conditions for further information. If you have specific questions or requirements, please contact the course administrator. 

Course Content


  • Introduction to DOE process and setup
  • Learn how to understand the statistical output metrics
  • Review of statistics, confidence, and statistical power
  • Understand sample sizing to test enough for the problem at hand


  • Linear full factorial designs
  • Quadratic full factorial designs
  • Mixed level DOE designs
  • Recovering from a missed quadratic effect


  • The importance of interaction effects
  • Why fractional factorial designs are used
  • How to determine the aliasing patterns and what they mean
  • Detecting possible problems with confounding
  • Recovering from a missed interaction effect


  • Screening designs
  • Optimal designs
  • Incrementally increasing the complexity of designs


  • Using simple Microsoft Excel add-in software for statistical tests and DOE solutions
  • Using DOE-specific software for statistical power calculations and trade-offs
  • Modeling distances shot from Statapults and deriving linear full factorial, augmented quadratic , and full factorial quadratic models from the shot data and using DOE software
Requirements & Materials



  • Working knowledge of probability and statistics



  • Textbook and course notes
  • Single-output Design of Experiments software package


  • Laptop computer

Session Details

  • Special Discounts: Georgia Tech Research Institute (GTRI) employees are eligible to receive a discount.  If you are a GTRI employee, please go to the Organizational Development website and look for the coupon code under GT Professional Development. Review coupon instructions for more information.

Who Should Attend

This course is designed for any technical professional who is frustrated by the length and monotonous nature of experimental testing.  This includes, but is certainly not limited to, engineers who want to shorten their testing duration of a complex system, technicians who want to optimize machine efficiency, data analysts who want to understand complex relationships driving KPIs, industrial designers interested in reducing assembly time, or a lean practitioners looking to reduce system variance.  Any technical professional interested in characterizing complex systems can benefit from this course.

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What You Will Learn

  • How factorial experimentation is superior to other common, more rudimentary methods of experimentation
  • How to answer the question:  “How much testing is enough?”
  • How to answer the objection:  “This is a lot of testing.  How can you justify the cost?”
  • The method of sequential experimentation and how it is critical to being efficient with resources
  • How to brainstorm with teams in a way that answers the question, “what should we test and why?”
  • How to use system characterization questions to predict behavior
  • Semi-advanced techniques including  fractional factorial experiments, screening designs, and optimal designs, and sequential experimentation
  • Solution of DOE problems using examples from Statapults, computer games, and simulations
  • How to design an experiment properly with  modern software tools
Adult learners participating in classroom discussion

How You Will Benefit

  • Recognize how to format the problem or evaluation to take advantage of the DOE process and solution.
  • Characterize the system under test or the system to be analyzed.
  • Derive equations that explain the behavior of the response based on the factors and the behavior of the variability of the response based on the factors.
  • Examine cause and effect, and verify controllable inputs and accurate, repeatable measurement systems.
  • Understand how to generate orthogonal designs and their benefits.
  • Efficiently design and conduct experimental studies for comparative evaluation, input-output characterization, output variance control, input sensitivity, and process control/optimization.
  • Validate transfer function results or recover from lack of confirmation.
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The course schedule was well-structured with a mix of lectures, class discussions, and hands-on exercises led by knowledgeable and engaging instructors.

- Abe Kani


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