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Statistical Design of Experiments



We are extremely pleased that Dr. Babatunde Ogunnaike recorded this very popular, practical and much-needed course, so that we can make it available in a convenient self-paced format. Dr. Michael J. Piovoso, a colleague of Dr. Ogunnaike’s – both at DuPont and now here at the University of Delaware, will be available to answer your questions and engage you in online discussion as you and your colleagues work through the lectures and exercises in this course.

  • Are you an engineer or scientist who is well-trained in the “art” of problem formulation and problem-solving – but only when all entities involved are deterministic in nature?
  • Is your background rusty (at best) when it comes to applying the fundamentals of probability and statistics – especially when it comes to handling randomly varying phenomena?
  • Are you concerned that dealing with randomly varying phenomena must involve calculus? (Guess what – algebra is sufficient!)
  • Are you able to generate lots of data – but then you’re uncertain how to interpret that data and design the next experiment?

Earn 2 CEUs (equivalent to 20 professional development hours) for full participation in this 2.5-day course.


This intensive and extremely practical course – now expanded based on feedback from previous participants – provides fundamental knowledge for using the tools of probability and statistics to design experiments, efficiently interpret large amounts of experimental data, cope with random variability and uncertainty, and use the results to improve quality and reliability.

Our CONVENIENT ONLINE FORMAT – a format that fits the scheduling needs of busy professionals who can log onto the course web site at any time of the day or night – when registrants will be given access to view the course presentations and demonstrations on the use of Minitab software (starting and stopping throughout each, as needed). Then comes the hands-on portion: test your skills using the MiniTab data files provided, plus design and run your own experiment! Best yet, through the course’s online discussion/chat room, you can pose questions for the instructors and interact with other course participants, sharing challenges and solutions, doing everything from your home, your office, or the nearest internet connection. Note that there should be at least two people taking this online course in order to get the best experience! (Full participation, including course assignments and online discussion, is estimated to  be equivalent to a 21-hour face-to-face course.)

Course Topics:

PART I: Why?

A. Motivation and Introduction

  • Objectives and Nature of Experimental Research
  • Random Variability and the Role of Probability and Statistics Statistical Inference and the Role of Carefully-designed experiments Phases of Efficient Experimental Studies
  • The Role of Computer Software in DoE

B. Background

  • Basic Concepts of Data Analysis
  • Review of Statistical Inference

PART II: What & How?

A. Fundamental Concepts and Applications

  • Terminology
  • Single-factor Experiments:
  • One-way Classification
    • Completely randomized Designs
    • Fixed Effects and Random Effects
    • Latin Square and Related Design
  • Single-factor Experiments: Two-way Classification
    • Randomized Complete Block Designs
    • Latin Square and Related Designs
  • Multi-factor Experiments
    • Two-factor Experiments
    • General Multi-factor Experiments
  • 2k Factorial Designs

B. More Advanced Concepts

  • Introduction (Going on from 2k Factorials)
  • Screening Designs
    • Fractional Factorial Designs Plackett-Burman Designs
    • Plackett-Burman Designs
  • Response Surface Methodology
  • Intro. to Taguchi Robust Parameter Designs

C. Summary, Conclusions and Going on from Here


This course enables you to:

  • learn from an instructor whose unparalleled background includes industrial applications of probability and statistics to industrial processes and experimental design;
  • apply statistical inference and understand the role of carefully designed experiments;
  • analyze large quantities of data and then design new experiments using that new knowledge;
  • optimally select experimental inputs, resulting in maximum knowledge with minimal cost;
  • bring theory to practical application;
  • use computer software to solve numerous real-life examples;
  • interact and share experiences with peers from other companies;
  • understand methods that can be put to use immediately.

There will be ample time for hands-on computer exercises – whether you are taking the course on-campus or in distance format with the use of MiniTab software (a free download)!

NOTE to those taking the course online: Minitab software is required for full participation in this course. If your computer is not already loaded with the Minitab software, a free 30-day trial download of Minitab 17 is available at http://it.minitab.com/en-us/products/minitab/free-trial.aspx.

Program Fees:

Individual Registrations
On-Campus: Not available at this time
Online**: $895/person

Group Registrations (3 or more)
On-Campus: Not available at this time
Online**: $795/person

Online registrations will be accepted on a rolling basis, when there are at least two people registering from the same company.

**Program Fee for online participants provides access to the course website, including PDFs of class notes, plus MiniTab files for hands-on exercises, plus online discussion/chat room to interact with colleagues and the professor.

CANCELLATIONS and SUBSTITUTIONS: Refunds granted only if the request is received in writing prior to access having been granted to the online course.

The Presenter – Babatunde A. Ogunnaike joined the University of Delaware ‘s full-time faculty in 2002 and is now Dean for the College of Engineering and is the William L. Friend Professor of Chemical & Biomolecular Engineering. Since 1989, Tunde had served as part-time faculty, teaching Process Control & Dynamics, as well as Random Phenomena-Applied Probability and Statistics for Engineering Problem- solving,  and participating in various research programs. In 1989, he had joined the DuPont Company’s Advanced Control and Modeling group, where he was a research fellow. His work at DuPont involved online dynamic modeling for various processes, identification and control of nonlinear systems, applied statistics, and reverse engineering biological control systems for process applications. Tunde holds an MS in statistics and PhD in chemical engineering, specializing in process  control, both completed in 1981 at the University of Wisconsin-Madison . From 1981-82, he was a research engineer in systems development at the Shell Development Corporation, where he designed and implemented advanced control schemes at two refineries and developed statistics-based  modeling techniques for “dynamic matrix control”. He then spent six years as a professor of chemical engineering and statistics at his undergraduate alma mater, the University of Lagos, Nigeria. He is author or co-author for numerous texts and book chapters, has published extensively in technical journals, and is a frequent seminar speaker at universities around the world. Among his many honors was his induction into the National Academy of Engineering as well as the Nigerian Academy of Engieering in 2014; he was named a fellow in the National Academy of Inventors ins 2014, and a fellow in the American Association for the Advancement of Science (AAAS) in 2015.

Online Course Assistance will be provided by  Dr. Michael J. Piovoso (who is the creator of our online course Control Charting for Statistical Process Control.)