If you are going to the Joint Statistical Meetings this year, mark your calendar for the one-day short course, “Generalized Additive Models and Their Extensions: The Penalized Regression Spline,” sponsored by SPES and taught by Simon N. Wood of the University of Bath, UK. It will take place July 31 from 8:30 a.m. until 5:00 p.m.
The course is designed for statisticians and experimenters who need methods for analyzing generalized additive models, as a tool to analyze a variety of complex models, including models that fit under the framework of quadratically penalized GLMs. The practical use of these methods is illustrated with a variety of examples using the mgcv package in R. Participants should bring a laptop with the latest version of R installed. The course material will be based on Wood’s book Generalized Additive Models: An Introduction.
Wood is the author of mgcv and has published a number of papers on the development of methods for generalized additive modeling.
Fall Technical Conference
The 56th Annual Fall Technical Conference will be held October 4–5 at the Millennium Hotel in downtown St. Louis, Missouri. The conference has long been a forum for both statistics and quality and is co-sponsored by the American Society for Quality (Chemical and Process Industries Division and Statistics Division) and the American Statistical Association (Section on Physical and Engineering Sciences and Section on Quality and Productivity). The goal of the conference is to engage researchers and practitioners in a dialogue that leads to more effective use of statistics to improve quality. Program, short course, registration, and accommodation details can be found at the conference website.
Short courses to be offered include “Optimal Design of Experiments,” by Peter Goos and Bradley Jones; “Methods and Applications of Generalized Linear Models,” by Doug Montgomery; “SPC for Autocorrelated Processes,” by Doug Timmer; and DOE Tools to Combine Mixture and Process Variables,” by Pat Whitcomb.
SPES is sponsoring “Optimal Design of Experiments: A Case-Study Approach,” which will take place on October 3 and cost $300. The course covers the standard and routine use of a fully flexible approach to design of experiments, named optimal design of experiments, by showing its industrial application in 10 case studies covering a wide range of practical situations. The course will provide answers to questions such as the following:
- How can I do screening inexpensively if I have many factors to investigate?
- What can I do if I have day-to-day variability and I can only perform three runs a day?
- How can I do RSM cost effectively if I have categorical factors?
- How can I design and analyze experiments when there is a factor that can only be changed a few times over the study?
- How can I include both ingredients in a mixture and processing factors in the same study?
- How can I design an experiment if there are many factor combinations that are impossible to run?
- How can I make sure a time trend due to warming up of equipment does not affect the conclusions from a study?
- How can I take into account batch information when designing experiments involving multiple batches?
- How can I add runs to an experiment to resolve ambiguities?
The material is based on the book Optimal Design of Experiments: A Case Study Approach by Goos and Jones.
Goos is full professor at the Faculty of Applied Economics and StatUa Center for Statistics of the Universiteit Antwerpen and the Erasmus School of Economics of the Erasmus Universiteit Rotterdam. For his work, he has received the Shewell Award and Lloyd S. Nelson Award of the American Society for Quality and the Young Statistician Award of the European Network for Business and Industrial Statistics. He is a senior member of the American Society for Quality.
Jones is a principal research fellow at SAS Institute and a guest professor at the Faculty of Applied Economics of the Universiteit Antwerpen. He is a senior member of the American Society for Quality.
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