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Curious Cat Library: Design of Experiments

Design of Experiments Articles, Reports, Handbooks and Guides

Applying DOE to Microwave Popcorn by Mark Anderson and Hank Anderson, Feb 1998
"Our DOE on microwave popcorn unintentionally turned out to be a destructive test. The heat and smoke generated at the upper limits of time and power degraded the chamber to a point where we decided it might be best to get a new machine."
Three Romeos and a Juliet: - Our early brush with Design of Experiments by Ravindra Khare, Jul 2002
Very easy to understand explanation of 3 factorial designed experiment. Defeinately no math or statistics knowledge needed.
Design of Experiments: An Overview and Application Example by John S. Kim and James W. Kalb, Mar 1996
"DOE techniques are not new to the health-care industry. Medical researchers have long understood the importance of carefully designed experiments. These techniques, however, have not been applied as rigorously in the product and design phases as in the clinical evaluation phase of product development."
How to Institute DOE in Your Company by Al Corwin, Sep 1999
"DOE works, but I don't need to sell that to the readers of this newsletter. But as certain as we all are, no one can deny that design of experiments faces resistance even in environments where it is a proven tool. Every research scientist or engineer who has had a major success from DOE can tell you story after story of how management still wanted problems solved one-factor-at-a-time."
DOE It Yourself by Mark J. Anderson, Nov 0002
A list of Design of Experiments excercizes that can be done in a classroom setting or as home by students with short explanations and links to documents online wtih more details.
A Personal Story of DOE by Bill Kappele, Feb 1999
"Back in the lab, I tackled ink with DOE. I was able to perform a small number of experiments and learn about interactions among the ingredients. I could see which ingredients appeared to be the most important, which ingredients interacted, and which interactions were most important. This really was a powerful technique."
The Rationale of Scientific Experimentation by John Dowd, Nov 0002
"In addition to their efficiency, factorial designs also offer the only method of detecting interactions through experimentation. Because numerous factors can be combined in the same series of experimental runs, the interactions can be detected and the nature of their effects can be evaluated when they are present."
Business Process Improvement using Cause-and-Effect Analysis and Design of Experiments by Nari Kannan, Jul 2005
"a combination of process modeling with cause-and-effect analysis combined with careful design of experiments (DOE) can help a company decide how and where they can allocate the monies they have to maximize their salutary effects on the company."
What Can You Find Out From 8 and 16 Experimental Runs? by George Box, Feb 1992
Includes two articles on options for designed experiments. Design of Experiments is a tools used heavily in Six Sigma programs.
William G. Hunter: An Innovator and Catalyst for Quality Improvement by George Box, Nov 2002
This is the text of a talk given at the Speakers' Dinner at the Sixth Annual William G. Hunter Conference on Quality in Madison, Wisconsin, on June 2, 1993. In it, George Box recalls Bill Hunter's pivotal role in the birth of the quality movement in the city of Madison. Without Hunter's catalytic contributions, Madison would not have its current leadership position in the improvement of quality in government, industry, and education.
What Can You Find Out From 12 Experimental Runs? by George Box and Soren Bisgaard, Nov 2002
Examines the possibilities of factorialy designed experiments using many variables at once.
Teaching Engineers Experimental Design With a Paper Helicopter by George Box, Dec 1991
How a helicopter (made in with a regular sheet of paper) can be used to teach principles of experimental design including - conditions for validity of experimentation, randomization, blocking, the use of factorial and fractional factorial designs, and the management of experimentation.
An Explanation and Critique of Taguchi's Contributions to Quality Engineering by George Box, Soren Bisgaard, and Conrad Fung, Mar 1988
"This paper presents an overview of Professor Genichi Taguchi's contributions and concludes that Professor Taguchi's quality engineering ideas are of great importance. However, many of the statistical design and analysis techniques he employs are often inefficient and unnecessarily complicated and should be replaced or appropriately modified."
Using Design of Experiments as a Process Road Map by Davis Balestracci, Feb 2006
"The current design of experiments (DOE) renaissance seems to favor factorial designs and/or orthogonal arrays as a panacea. In my 25 years as a statistician, my clients have always found much more value in obtaining a process "road map" by generating the inherent response surface in a situation. It's hardly an advanced technique, but it leads to much more effective optimization and process control."
Some Comments on Historical Designed Experiments by Keith M. Bower, Nov 2004
"I wanted to summarize some of the key reasons when and why such an approach would be statistically invalid, and this was the resulting paper. The concept of removing datapoints to 'get the software to work' was something I was very keen to address in the paper - i.e. it is wholly inappropriate."
Introduction to Statistical Experimental Design by Johan Trygg and Svante Wold, Aug 2002
"Statistical experimental design, a.k.a. design of experiments (DoE) is the methodology of how to conduct and plan experiments in order to extract the maximum amount of information in the fewest number of runs"
Studies in Quality Improvement: Dispersion Effects from Fractional Designs by George Box and R. Daniel Meyer, Feb 1986
"The expense of repeating measurements can sometimes be avoided by using unreplicated fractional factorials to identify factors that affect dispersion."
Design of Experiments for Six Sigma by Peter Peterka, Nov 2002
"Experimental methods are used to quantify previously undefined factors and interactions between factors. This is accomplished through crafting planned experiments where controlled changes of factors will determine which factors have the largest impact on quality characteristics."
Design Of Experiment For Software Testing by Madhav S. Phadke, Nov 2002
"Use of orthogonal array based testing has demonstrated to produce superior test plans that improve testing productivity by a factor of 2. This method is found effective in testing the incremental work done in all stages of software development. These stages include writing requirements, selecting architecture, designing the system (functional breakdown), unit testing, platform testing, integration testing, prototype testing, and system testing."
Combinatorial Software Testing by Rick Kuhn, Raghu Kacker, Yu Lei, and Justin Hunter, Aug 2009
While the most basic form of combinatorial testing, pairwise, is well established, and adoption by software testing practitioners continues to increase, industry usage of these methods remains patchy at best. However, the additional training required is well worth the effort. Teams seeking to maximize testing thoroughness given tight time or resource constraints, and which currently rely on manual test case selection methods, should consider pairwise testing. When more time is available or more thorough testing is required, t-way testing for t > 2 is better.
Software Testing by Statistical Methods by David Banks et. al., Mar 1998
"The goal of this work is to ensure software quality and to develop methods for software conformance testing based on known statistical techniques, including multivariable analysis, design of experiments, coverage designs, usage models, and optimization techniques,"
Navigating the Depths of Multivariable Testing by Gordon H. Bell and Roger Longbotham, Feb 2007
This article looks at multi-variable testing for marketing efforts. "Multivariable testing - also called scientific testing, mulvariate or matrix testing... has evolved over the last 80 years. Since the 1930's a small group of academic statisticians has developed new test designs and techniques focused on efficient ways to test more variables more quickly."
Design of Experiments: An Overview and Application Example by John S. Kim and James W. Kalb, Mar 1996
"Carefully planned, statistically designed experiments offer clear advantages over traditional one-factor-at-a-time alternatives. These techniques are particularly useful tools for process validation, where the effects of various factors on the process must be determined. Not only is the DOE concept easily understood, the factorial experiment designs are easy to construct, efficient, and capable of determining interaction effects. Results are easy to interpret and lead to statistically justified conclusions."
101 Ways to Design an Experiment, or Some Ideas About Teaching Design of Experiments by William G. Hunter, Jun 1975
A great article sharing ideas on teaching and learning about Design of Experiments (DoE), a powerful and greatly underutilized tool. 6 simga efforts have greatly increased the use of DoE.
Managing Our Way to Economic Success: Two Untapped Resources by William G. Hunter, Feb 1996
"American organizations could compete much better at home and abroad if they would learn to tap the potential information inherent in all processes and the creativity inherent in all employees."

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