Dimensional Analysis and Its Applications in Statistics

Abstract:  Dimensional Analysis (DA) is a fundamental method in the engineering and physical sciences for analytically reducing the number of experimental variables prior to the experimentation.  The principle use of dimensional analysis is to reduce from a study of the dimensions of the variables on the form of any possible relationship between those variables.  The method is of great generality.  In this talk, an overview/introduction of DA will be first given.  A basic guideline for applying DA will be proposed, using examples for illustration.  Some initial ideas on using DA for Data Analysis and Data Collection will be discussed.  Future research issues will be proposed.
 
 
Bio:  Dr. Dennis Lin is a University Distinguished Professor of Supply Chain and Statistics at Penn State University.  His research interests are quality engineering, industrial statistics, data mining and Statistical Inference.  He has published near 200 professional (SCI/SSCI) papers in a wide variety of prestigious journals (such as, Technometrics, Annals of Statistics, Biometrika, Statistica Sinica, etc).  He has served as a co-editor for ASMBI as well as an associate editor for various (about 10) top journals.  Dr. Lin is an elected fellow of ASA, IMS and ASQ, an elected member of ISI, a fellow of RSS, and a lifetime member of ICSA.  He is the recipient of the 2004 Faculty Scholar Medal Award at Penn State University.  He is also an honorary chair professor for various universities, including a Chang-Jiang Scholar at Renmin University of China.  His recent awards include Don Owen Award (ASA), Youden Address (ASQ) and Loutit Lecturer (SSC).