Statistical Design of Experiments
Now YOU and your colleagues can take this great course – at your workplace and on your own schedule!
COURSE DESCRIPTION :
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 on-line 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.
WHO SHOULD ATTEND
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 ON-LINE 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 on-line 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 on-line course in order to get the best experience! (Full participation, including course assignments and on-line discussion, is estimated to be equivalent to a 21-hour face-to-face course.)
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
- Basic Concepts of Data Analysis
- Review of Statistical Inference
PART II: What & How?
A. Fundamental Concepts and Applications
- 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 on-line: 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.
Registration and General Information
(3 or more from the same company)
|not available at this time||$895/person||not available at this time||$795/person|
|On-line registrations will be accepted on a rolling basis, when there are at least two people registering from the same company.|
**Program Fee for on-line participants provides access to the course website, including PDFs of class notes, plus MiniTab files for hands-on exercises, plus on-line discussion/chat room to interact with colleagues and the professor. Instructions for accessing the course web portal will be sent (via e-mail) to all registrants ???? allowing time to become familiar with the course set-up and materials..
CANCELLATIONS and SUBSTITUTIONS: Refunds granted only if the request is received in writing prior to access having been granted to the on-line course.
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 on-line 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.
On-line Course Assistance will be provided by Dr. Michael J. Piovoso (who is the creator of our on-line course Control Charting for Statistical Process Control.)