Course is closed
On Campus
Course Length
5 Days
Course Fee
3.0 CEUs
Sign-up for Course Updates
Course is closed
Sign-up for Course Updates

Explore innovative strategies for constructing and executing experiments—including factorial and fractional factorial designs—that can be applied across the physical, chemical, biological, medical, social, psychological, economic, engineering, and industrial sciences. Over the course of five days, you’ll enhance your ability to conduct cost-effective, efficient experiments, and analyze the data that they yield in order to derive maximal value for your organization.

Course Overview


This program is planned for those interested in the design, conduct, and analysis of experiments in the physical, chemical, biological, medical, social, psychological, economic, engineering, or industrial sciences. The course will examine how to design experiments, carry them out, and analyze the data they yield. Various designs are discussed and their respective differences, advantages, and disadvantages are noted. In particular, factorial and fractional factorial designs are discussed in greater detail. These are designs in which two or more factors are varied simultaneously; the experimenter wishes to study not only the effect of each factor, but also how the effect of one factor changes as the levels of other factors change. The latter is generally referred to as an interaction effect among factors.

The fractional factorial design has been chosen for extra-detailed study in view of its considerable record of success over the last 30 years. It has been found to allow cost reduction, increase efficiency of experimentation, and often reveal the essential nature of a process. In addition, it is readily understood by those who are conducting the experiments, as well as those to whom the results are reported.

The program will be elementary in terms of mathematics. The course includes a review of the modest probability and statistics background necessary for conducting and analyzing scientific experimentation. With this background, we first discuss the logic of hypothesis testing and, in particular, the statistical techniques generally referred to as Analysis of Variance. A variety of software packages are illustrated, including Excel, SPSS, JMP, and other more specialized packages.

Throughout the program we emphasize applications, using real examples from the areas mentioned above, including such relatively new areas as experimentation in the social and economic sciences.

We discuss Taguchi methods and compare and contrast them with more traditional techniques. These methods, originating in Japan, have engendered significant interest in the United States.

All participants receive a copy of the text, Experimental Design: with applications in management, engineering and the sciences, Duxbury Press, 2002, co-authored by Paul D. Berger and Robert E. Maurer, in addition to extensive PowerPoint notes.

Participant Takeaways

  • Describe how to design experiments, carry them out, and analyze the data they yield.
  • Understand the process of designing an experiment including factorial and fractional factorial designs.
  • Examine how a factorial design allows cost reduction, increases efficiency of experimentation, and reveals the essential nature of a process; and discuss its advantages to those who conduct the experiments as well as those to whom the results are reported.
  • Investigate the logic of hypothesis testing, including analysis of variance and the detailed analysis of experimental data.
  • Formulate understanding of the subject using real examples, including experimentation in the social and economic sciences.
  • Introduce Taguchi methods, and compare and contrast them with more traditional techniques.
  • Learn the technique of regression analysis, and how it compares and contrasts with other techniques studied in the course.
  • Understand the role of response surface methodology and its basic underpinnings.
  • Gain an understanding of how the analysis of experimental design data is carried out using the most common software packages.
  • Be able to apply what you have learned immediately upon return to your company.

Who Should Attend

This course is appropriate for anyone interested in designing, conducting, and analyzing experiments in the biological, chemical, economic, engineering, industrial, medical, physical, psychological, or social sciences. Applicants need only have interest in experimentation. No previous training in probability and statistics is required, but any experience in these areas will be useful.

Program Outline

Class runs 9:00 am - 5:00 pm every day.

Day One

  • Session 1 - 9:00 - 10:00am
    • Introduction to Experimental Design
  • Session 2 - 10:30 - 12:00 noon
    • Hypothesis Testing
  • Session 3 - 1:00 - 3:00pm
    • ANOVA I, Assumptions, Software
  • Session 4 - 3:30 - 5:00pm
    • Multiple Comparison Testing

Day Two

  • Session 5 - 9:00 - 10:00am
    • ANOVA II, Interaction Effects
  • Session 6 - 10:30 - 12:00 noon
    • Latin Squares and Graeco-Latin Squares
  • Session 7 - 1:00 - 3:00pm
    • 2K Designs
  • Session 8 - 3:30 - 5:00pm
    • 2K Designs (continued)

Day Three

  • Session 9 - 9:00 - 10:00am
    • Confounding/Blocking Designs
  • Session 10 - 10:30 - 12:00 noon
    • Confounding/Blocking Designs (continued)
  • Session 11 - 1:00 - 3:00pm
    • 2k-p Fractional-Factorial Designs
  • Session 12 - 3:30 - 5:00pm
    • 2k-p Fractional-Factorial Designs (continued)

Day Four

  • Session 13 - 9:00 - 10:00am
    • Taguchi Designs
  • Session 14 - 10:30 - 12:00 noon
    • Taguchi Designs (continued)
  • Session 15 - 1:00 - 3:00pm
    • Orthogonality and Orthogonal contrasts
  • Session 16 - 3:30 - 5:00pm
    • 3K Factorial Designs

Day Five

  • Session 17 - 9:00 - 10:00am
    • Regression Analysis I
  • Session 18 - 10:30 - 12:00 noon
    • Regression Analysis II
  • Session 19 - 1:00 - 3:00pm
    • Regression Analysis III & Introduction to Response Surface Modeling
  • Session 20 - 3:30 - 5:00pm
    • Response Surface Modeling (continued), Literature Review, Course Summary


  • The logic of complete two-level factorial designs
  • Detailed discussion of interaction among studied factors
  • Large versus small experiments
  • Simultaneous study of several factors versus study of one factor at a time
  • Fractional experimental designs; construction and examples
  • The application of hypothesis testing to analyzing experiments
  • The important role of orthogonality in modern experimental design
  • Single degree-of-freedom analysis; pinpointing sources of variability
  • The trade-off between interaction and replication
  • Response surface experimentation
  • Yates' forward algorithm
  • The reliability of estimates in factorial designs
  • The usage of software in design and analysis of experiments
  • Latin and Graeco-Latin squares as fractional designs; examples
  • Designs with all studied factors at three levels
  • The role of fractional designs in response surface experimentation
  • Taguchi designs
  • Incomplete study of many factors versus intensive study of a few factors
  • Multivariate linear regression models
  • The book and journal literature on experimental design


“Very knowledgeable professor who in almost every instance provided real-world examples to illustrate lessons. I enjoyed the opportunity to be in a classroom setting and found the material germane to my job and learned new methods that I will incorporate into our technical approach.”
“Overall, this course was excellent. The knowledge I gained from the course I don't think I could get from anywhere [else].”
“Professor Berger was very engaging and he clearly has a lot of relevant knowledge regarding the complications and pitfalls of DOE application to real-world problems. The material he covered was material that I can instantly apply to my job function. He did an excellent job of covering just enough mathematical/statistical principles to maintain the rigor of his statements without bogging the class down in theoretical discussions. He really focused well on practical applications of the techniques covered in the class.”
“Definitely exceeded my high expectations and I look forward to communicating what I learned with my company internally on Monday!”
“Professor Berger was able to explain the material in a way that I could understand, not only using practical examples, but also answering questions/problems I brought from work. The material is condensed into five days only, but by being dynamic, fun, and using interesting examples, I was able to continuously pay attention and understand the topic. Every single topic covered was explained with examples.”

The type of content you will learn in this course, whether it's a foundational understanding of the subject, the hottest trends and developments in the field, or suggested practical applications for industry.

Fundamentals: Core concepts, understandings, and tools - 60%|Latest Developments: Recent advances and future trends - 15%|Industry Applications: Linking theory and real-world - 25%
Delivery Methods

How the course is taught, from traditional classroom lectures and riveting discussions to group projects to engaging and interactive simulations and exercises with your peers.

Lecture: Delivery of material in a lecture format - 90%|Discussion generated from questions from the students - 10%

What level of expertise and familiarity the material in this course assumes you have. The greater the amount of introductory material taught in the course, the less you will need to be familiar with when you attend.

Introductory: Appropriate for a general audience - 50%|Specialized: Assumes experience in practice area or field - 50%