Design of Experiments (DOE) I: Introduction to DOE

  • Overview
  • Course Content
  • Requirements & Materials

Design of Experiments (DOE) I: Introduction to DOE

Course Description

Design of experiments (DOE) is a very efficient method to derive a mathematical representation of a complex system and is used in both industry and government to develop, manufacture, and acquire the best products and systems. DOE solutions provide equations that characterize the relationships between the inputs and the outputs and statistical measures that describe the strengths of the derived characterizations. You will learn the fundamental steps for planning and completing studies in which the inputs to a system/process can be varied and the outputs observed. In addition you’ll explore efficient planning and analysis methods for determining which inputs have statistically significant effects on outputs. This course will provide hands-on experience through Statapults, simulations, and case studies. You’ll observe or practice DOE experiences using various software packages such as DOE Pro, SAS JMP, and Design Expert. All DOE solutions will be calculated by hand and via computer programs in class.

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

  • 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 engineers, technicians, and managers who want to learn the techniques for designing experiments.

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What You Will Learn

  • The advantages of using DOE
  • DOE process setup and solution steps including randomization
  • Derivation of transfer functions to quantify contributions of inputs (factors) to outputs (responses)
  • Full factorial and fractional factorial experiments, screening designs, and optimal designs
  • Solution of DOE problems using examples from Statapults, computer games, and simulations
  • DOE solutions using various software packages
  • Various design and randomization techniques
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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    Taught by Experts in the Field
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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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