Experiment Design Made Easy (EDME), $1295

In this 2-day hands-on, computer-intensive workshop participants learn to set up, analyze, and interpret two-level factorial, general factorial and fractional factorial designs. Learn how to identify the vital few effects and discover unknown interactions through the use of powerful design of experiment (DOE) techniques.

Price includes a $95 fee for workshop materials which is subject to state and local taxes.

A 10% Early Bird discount will be applied to registrations made 6 weeks prior to the workshop date.

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Price: $1,175.00

Experiment Design Made Easy (EDME), $1295


Experiment Design Made Easy (EDME) (2 days)


Identify the Vital Few Effects—Make Breakthrough Improvements

Find out how to make breakthrough improvements using powerful design of experiments (DOE) techniques. Start with our Experiment Design Made Easy workshop to learn about using factorial designs for finding which factors you need to focus on. Discover previously unknown interactions that often prove to be the key to success. Learn how to use powerful ANOVA analysis methods that give you confidence in your findings. 

Apply Tried and True Techniques

Experiment Design Made Easy covers the practical aspects of DOE. (Students may purchase the optional "DOE Simplified" book for reference.) You learn all about simple but powerful two-level factorial designs. During this introductory DOE workshop, you will discover how to effectively:

  • Understand the motivation for factorial designs
  • Implement the DOE planning process
  • Interpret analysis of variance (ANOVA)
  • Discover hidden interactions
  • Capitalize on efficient fractional designs for screening or characterization
  • Use power to properly size designs
  • Determine when to use transformations
  • Explore multilevel categoric factors
  • Set up split-plot designs
  • Follow the strategy of experimentation from screening to response surface methods

Choose the Best Strategy

The "Experiment Design Made Easy" workshop helps you plan your DOE by selecting the appropriate designs. It guides you through your experiment and strategic analysis.

"Practical. Good mix of theory and application." 
—Chris Easter, Metallurgist

Simulations Provide Practice

Use Design-Expert® software to practice designing and analyzing experiments throughout the workshop. The software provides easy-to-use graphical tools to find key variables and view results.  You will be given a path to all simulation and data files used in class, which are posted to a special Internet site where you can also link to a free fully-functional, but time-limited, copy of Design-Expert software for use after class.
"Gives you the 'hands-on' that puts it all together."
—Matt Hanken, Senior Manufacturing Engineer

Course Outline

Day 1 Section 1—Introduction to Factorial Design
  • Background and motivation for factorial designs
  • Factorial design planning process
  • Basics of factorial design: Case study
    • Selecting effects—Half-normal plot and Pareto chart
    • ANOVA and residual diagnostics
    • Main effects, interaction, contour and 3D surface plots
    • Introduction to multiple-response optimization
Lunch Section 2—Enhancements for Design and Analysis of Factorials
  • Replicated 23full factorial: Case study
    • Explanation of power
  • 24 full factorial: Exercise
  • Transformations case study
    • Dangers of deleting outliers
    • Details of using transformations
    Section 3—Blocking and Fractionating Factorials
  • How to set up optimal blocking: Case study
    • Factors interacting vs three-factor interactions (3FIs)
  • How to set up fractional factorials
  • Understanding aliases
  • 25-1 fractional factorial: Exercise
Day 2 Section 4—Small Factorial Designs
  • Minimum-run characterization (MR5) design: Exercise
    • Dealing with a low-power response
  • Minimum-run screening (MR4) design
  • Definitive screening (DSD) design
  • Guide to using small-run designs
  Section 5—Multilevel Categoric Design (General Factorial)
  • Multilevel categoric design with replication: Case study
  • Fractionating via optimal (custom) design: Case study
    • Introduction to optimal (custom) design
    • Model graphs for multilevel categoric designs
Lunch Section 6—Split-Plot Designs
  • Restriction randomization
  • Split-plot design: Case study
  Section 7Factorial with Center Points and RSM Introduction
  • Factorial with center points: Case study
  • Introducing response surface methods (RSM)
  • Augmentating to central composite design (CCD)
    • Case study part 2
  Section 8—Overview and Homework
  • Recommendations for choosing a factorial design
  • Mixture design overview and case study
  • Homework review (breakout session, if time allows)
  • Conclusion and resources


Math skills, knowledge of basic statistics, and exposure to simple comparative experiments (e.g. two-sample t-test) are recommended. If you aren't ready for the Experiment Design Made Easy workshop, take the online PreDOE course first (a $95 value you get for free! It takes 2-3 hours to complete. You can work at your own pace). Access the PreDOE here.

Before attending class, please download a trial of Design-Expert software, v10 (if you do not already have access to it) and work through the General Multilevel-Categoric One-Factor Tutorial.

Additional Information

PDHs 16 (equals 1.6 CEUs)
Additional Information

Workshop location details will be provided with your confirmation letter, which you will receive once the minimum enrollment requirements are met.

Here are addresses we have used in the past:

Minneapolis, MN (Stat-Ease headquarters): 2021 East Hennepin Ave, Suite 480, Minneapolis, MN 55413

Edison, NJ: Raritan Plaza III, 105 Fieldcrest Avenue, Suite 201, Edison, New Jersey 08837, Website

San Diego, CA: 350 10th Avenue, Suite 950, San Diego, California 92101, Website 

San Jose, CA: 2025 Gateway Place, Suite 390, San Jose, California 95110, Website 

Recommended Texts and Software

Recommended Texts and Software

Purchase the recommended text or software at the time of registration to receive a 20% discount.