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Third European DOE User Meeting
Lucerne, Switzerland
May 31st - June 3rd, 2010

 

For final programme with links to presentations, click here.

Keynote Speakers
This page contains a list of keynote speakers. Additional speakers, as well as titles of all presentations will be added to the program.

Peter Goos is Full Professor at the Faculty of Applied Economic Sciences of the University of Antwerp and at the Erasmus School of Economics of the Erasmus University of Rotterdam. His major fields of expertise are optimal design of experiments, stated preference studies and general linear models.

Pat Whitcomb is the founding principal and president of Stat-Ease, Inc. Before starting his own business, he worked as a chemical engineer, quality assurance manager, and plant manager. Pat co-authored Design-Ease® software, an easy-to-use program for design of two-level and general factorial experiments and Design-Expert® software, an advanced user's program for response surface, mixture, and combined designs. He's provided consulting on the application of design of experiments (DOE) and other statistical methods for several decades. In addition, Pat is co-author of the books, "DOE Simplified: Practical Tools for Effective Experimentation" and "RSM Simplified: Optimizing Processes Using Response Surface Methods for Design of Experiments," and has published many articles on design of experiments (DOE).

 

Talks

Peter Goos — Industrial Strip-Plot Designs: Design and Analysis

The cost of experimentation can often be reduced by forgoing complete randomization. A well-known design with restricted randomization is a split-plot design, which is commonly used in industry when some experimental factors are harder to change than others or when a two-stage production process is studied. Split-plot designs are also often used in robust product design to develop products that are insensitive to environmental or noise factors. Another, lesser known, type of experimental design plan that can be used in such situations is the strip-plot experimental design. Strip-plot designs are economically attractive in situations where the factors are hard to change and the process under investigation consists of two distinct stages, and where it is possible to apply the second stage to groups of semi-finished products from the first stage. They have a correlation structure similar to row-column designs and can be seen as special cases of split-lot designs. In this talk, I show how optimal design of experiments allows for the creation of a broad range of strip-plot designs.

Pat Whitcomb — Size Your Design for Success

Prior to performing any experiment all efforts should be made to ensure that the size of the design suffices for detecting the signal of interest.  For factorial screening the detection of effects is the driving force for sizing.  However, when the goal is optimization via response surface methods (RSM) the experimenter becomes more interested in precision of the model prediction.  A discussion and pertinent examples will show attendees how to size either type of design—factorial or RSM.  Attendees will take away a strategy for determining if a particular design has power or precision appropriate for their modeling needs.

Ivan Langhans — Correcting for Multiple Testing: Old Tricks and State-of-the-Art Solutions

Anyone who frequently analyzes larger experimental designs (or other datasets) has run into situations where a model is just too big to be true, i.e. there are too many terms statistically significant when testing each term at the 5% significance level.

This talk will present some methods that provide an alternative for old tricks like Bonferroni corrections. Some will come from a class of methods related to controlling the number of false discoveries (methods that have become the standard in the “-omics” world), another approach is the slightly more elaborate but insightful method of generalized degrees of freedom (GDF).

Mark Anderson — What's new in Design-Expert V8

This talk will introduce an array of statistical methods for design of experiments (DOE) made easy and more powerful via version 8 of Design-Expert software. It highlights key features from simple to sublime, ranging from factorials through response surface methods (RSM) to mixture design.

 

 

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