Design of Experiments (DOE) II: Advanced Topics to Make You an Expert Experimenter

  • Overview
  • Course Content
  • Requirements & Materials
Overview

Design of Experiments (DOE) II: Advanced Topics to Make You an Expert Experimenter

Course Description

Building on the foundations of factorial experimental design from DOE I, this
course will provide techniques and practical advice for dealing with the reality of
complex experiments. Through a process of discovery and critical thinking,
students will uncover reliable tools for recovering from lost data, identifying
outliers, using random factors, interpreting sophisticated statistical plots, using
binary responses, evaluating experimental designs holistically, and much, much
more!


The aim of this course is to turn a technical professional who knows how to create
relatively basic experimental designs into a true DOE expert and practitioner who
has the newfound confidence to tackle any manner of complex systems. Using a
Socratic teaching method alongside class examples, case studies, manipulatives,
and software practice, the goal is to excite technical professionals about
experimental design in such a way that they finish the course saying, “let’s characterize
everything!”

Course Content

Overview of DOE

  • Basic DOE history
  • Benefits of using DOE
  • Types of DOE designs
  • Incrementally increasing the design complexity
  • Test design principles

Review of Statistics

  • Descriptive statistics
  • Confidence intervals
  • Statistical control
  • Statistical power
  • Sample sizing tradeoffs

Types of DOE Designs

  • Comparison of ANOVA to DOE
  • Designs that accommodate hard-to-change factors
  • Split plot and nested designs
  • Blocked designs

Application of DOE

  • Case studies in applied DOE
  • Munitions against moving targets
  • Radar detection and tracking

Handling Data Issues

  • Handling missing data
  • Examples of missing replications
  • Examples of missing runs

Course Summary

  • Review of topics and questions
  • Discussion of types of DOE issues encountered by students
Requirements & Materials

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 class is best suited to technical professionals (scientists, chemists, engineers, physicists, statisticians etc) including their technical managers who are ready to take their factorial experimental designs to the next level of sophistication. The reality is, performing experiments is a complex mix of analytical knowledge, informed judgement, and a lesson in adapting to an everchanging environment. This class is for students who want to build confidence in all three of those categories in order to characterize their complex systems with more success, more often, and with more rigor.


If a technical professional has ever performed an experiment following the techniques in DOE I and later asked, “how do I know if I have an outlier?” or “what if I have missing data?” or “what if my response is binary?” or “was this design the best choice?” or “the non-linear model still doesn’t confirm!”, then this class is for them.

Adult professional attending defense tech course

What You Will Learn

Using a Socratic teaching style alongside class examples, case studies, manipulatives, and software practice, students who take this class will learn how to:

  • Size experiments with binary responses
  • Identify outliers quickly and learn techniques to recover from them
  • Design an experiment that cannot be fully randomized (split-plot designs)
  • Find the best possible solution for multiple responses with competing interests (desirability)
  • Analyze an experiment with missing data
  • Nest designs to control against multiple nuisance factors
  • Learn about other very popular fractional designs to further increase resource efficiency (think 11 factors in 12 runs!)
  • Holistically evaluate multiple possible experimental designs
  • Use random and non-random factors together
  • Properly review all the numbers in an ANOVA table (not just p-values!)
  • Use computer generated factorial designs to optimize the size of an experiment
  • Capitalize on the intersection between model and simulation (M&S) with factorial experimentation (DOE)

And much, much more!

Adult learners participating in classroom discussion

How You Will Benefit

Technical professionals who take DOE II will deepen their understanding of factorial experimentation and learn practical techniques for adapting to the messy reality of experimentation. The truth is, performing experiments is a complex mix of analytical knowledge, informed judgement, and a lesson in adapting to an everchanging environment. This class is for students who want to build confidence in all three of those categories in order to characterize their complex systems with more success, more often, and with more rigor.

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    Taught by Experts in the Field

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
President

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