DATAWorks Speakers and Abstracts

Dr. Tom Donnelly

JMP Statistical Discovery LLC
Design and Analysis of Experiments – Next-Level Methods with Case Studies
Day 1, Room: A 9:00 AM-4:00 PM

Tom Donnelly works as a Systems Engineer for JMP Statistical Discovery supporting users of JMP software in the Defense and Aerospace sector. He has been actively using and teaching Design of Experiments (DOE) methods for the past 40 years to develop and optimize products, processes, and technologies. Donnelly joined JMP in 2008 after working as an analyst for the Modeling, Simulation & Analysis Branch of the US Army’s Edgewood Chemical Biological Center – now DEVCOM CBC. There, he used DOE to develop, test, and evaluate technologies for detection, protection, and decontamination of chemical and biological agents. Prior to working for the Army, Tom was a partner in the first DOE software company for 20 years where he taught over 300 industrial short courses to engineers and scientists. Tom received his PhD in Physics from the University of Delaware.

Abstract: Design and Analysis of Experiments – Next-Level Methods with Case Studies

Advancing Test & Evaluation of Emerging and Prevalent Technologies

This is the short course for you if you are familiar with the fundamental techniques in the science of test and want to learn useful, real-world, and advanced methods applicable in the DoD/NASA test community. The focus will be on use cases not typically covered in most short courses. JMP software will primarily be used, and datasets will be provided for you to follow along many of the hands-on demonstrations of practical case studies. Design topics will include custom design of experiments tips, choosing optimality criteria, creating designs from existing runs, augmenting adaptively in high gradient regions, creating designs with constraints, repairing broken designs, mixture design intricacies, modern screening designs, designs for computer simulation, accelerated life test, and measurement system testing. Analysis topics will include ordinary least squares, stepwise, and logistic regression, generalized regression (LASSO, ridge, elastic net), model averaging (to include Self-Validated Ensemble Models), random effects (split-plot, repeated measures), comparability/equivalence, functional data analysis (think your data is a curve), nonlinear approaches and multiple response optimization and trade-space analysis. The day will finish with an hour-long Q&A session to help solve your specific T&E challenges.