FTC 2022 Short Courses

Short courses will be held on October 12, 2022. Attendees may registers for the conference and short courses together or separately. Conference attendance is not required to attend a short course.

FULL DAY COURSES

Advanced Quality Control Methods: CUSUM and EWMA Procedures

James Lucas, J. Lucas & Associates

This course will discuss CUSUM (Cumulative Summation) and EWMA (Exponentially Weighted Moving Average) Procedures for Quality Control Applications.  The presenter is a consultant and researcher who was an integral part of the largest known implementation of CUSUM procedures.  He has guided many successful implementations of these procedures and was the developer of many of the most used enhancements.  This will be a “hands on” course with discussions of real problems and workshops to practice using the techniques.  You will come from this course knowing how to use these procedures to monitor a process (and to detect problems). The course knowledge will enhance your data analysis skills.  Both variables and count data (including procedures for rare events) will be discussed.  Comparisons with classical Shewhart procedures will be given throughout the course to show where these advanced procedures have large benefits. The prerequisite for the course is a quality control course or some implementation experience.

CONTACT: JamesM.Lucas@verizon.net

JAMES LUCAS is the principal at J. M. Lucas and Associates, a consulting firm in Statistics and Quality Management.  This firm implements business systems with statistical aspects.  Before starting his own consulting group Lucas was a Senior Consultant at DuPont’s Applied Statistics Group for over twenty years.

He has been an Adjunct Professor at the University of Delaware and at Drexel University and he has directed six PhD dissertations.  He is a Fellow of the American Statistical Association (ASA) and of the American Society for Quality (ASQ), an Associate Editor of the Journal of Quality Technology and Quality Engineering, and a past Associate Editor of Chemometrics and Intelligent Laboratory Systems and of Technonetrics.

Lucas is a Past President of the Delaware Chapter of the ASA, a Past Chair of the Chemical and Process Industries Division of the ASQ.  He has been a team leader for the Delaware Quality Award and a past Chair of the Gordon Research Conference on Statists in Chemistry and Chemical Engineering. He has over 70 publications and many are cited frequently.  He authored the most cited paper in two volumes of Technometrics and in two volumes of the Journal of Quality Technology.  He has won many awards including the 2018 Hunter Award, and before that, the Shewhart Medal, the Brumbaugh Award, the H. O. Hartley Award, the Ellis R. Ott Foundation Award, the Don Owen Award, the Shewell Award, and the Youden Prize.

He has a PhD in Statistics from Texas A&M University, a MS in Statistics from Yale University, and a BS in Engineering from The Pennsylvania State University.

Methods for Designing & Analyzing Mixture Experiments

Greg Piepel, MIXSOFT

Mixture experiments involve changing the proportions of the components of a mixture that make up a product and then observing the resulting changes in the product’s characteristics. The proportions of the components in the mix cannot be varied independently (as in factorial experiments) because they must sum to 1.0 for each run in the experiment. Mixture experiments are very useful in many product development areas, including foods and drinks, plastics, alloys, ceramics and glass, gasoline blending, fertilizers, textile fibers, concrete, drugs, and many others.

The short course will provide an overview of various approaches and methods used in designing mixture experiments and analyzing the resulting data. Designs for simplex-shaped and irregular-shaped regions (the latter resulting from placing additional constraints on the component proportions) will be covered. The various types of mixture models that can be fitted to mixture data will be covered, as will graphical techniques for interpreting component effects. Methods for including process variables and/or a total amount variable in mixture experiments will be discussed. Graphical and analytic methods for developing mixtures with optimum properties will also be covered. Numerous examples will be used to illustrate the topics discussed.

The course is designed for anyone (statistician or non-statistician) wanting to know about statistical methods for designing mixture experiments and analyzing the resulting data. Prerequisites are an understanding of elementary statistics concepts and some previous exposure to experimental design and least squares regression.

CONTACT: mixsoft@aol.com

DR. GREG PIEPEL retired in September 2020 from being a Laboratory Fellow at the Pacific Northwest National Laboratory (PNNL) in Richland, Washington, where he had been employed for 42 years. His statistical specialty is the design and analysis of mixture experiments, a subject area he has worked in since 1978. Dr. Piepel is a Fellow of the American Statistical Association (ASA), a Fellow of the American Society for Quality, and an elected member of the International Statistics Institute.

HALF DAY COURSES

Functional Data Analysis and Its Applications

Pang Du, Virginia Tech

This course aims to introduce the modern field of functional data analysis to a general audience with the emphasis on how the relevant techniques can be applied to real examples. As a a generalization of the traditional data concepts from numbers and vectors of numbers to curves and surfaces, functional data has attracted a lot of attention from statisticians and found many interesting applications in a variety of fields in the past decades. The course will start with the introduction of real examples for functional data. Based on these examples, common functional data analysis techniques such as function smoothing, functional principal component analysis, and functional linear regression models will be presented. R implementation of these techniques will be introduced and demonstrated.

CONTACT: pangdu@vt.edu

DR. PANG DU graduated from Purdue University with PhD in Statistics in 2006 and has since then been a faculty member at the Department of Statistics, Virginia Tech. His research interest lies in both the development of new statistical methodology and novel applications of statistics to various studies in other disciplines. His research domain on statistical methodology includes functional data analysis, nonparametric smooth modeling, high dimensional data, failure time data analysis, change point analysis, and ROC curve methodology. His application areas include public health, biomechanical engineering, biomedical engineering, cybersecurity, environmental sciences, and biological sciences. His research has been supported by 9 research grants, including PI on 3 NSF grants, and co-I on a recent NIH R01 grant. He has over 50 publications in prestigious journals such as Annals of Statistics, Biometrika, Journal of the American Statistical Association, Annals of Applied Statistics, Biometrics, Statistica Sinica, and Statistics in Medicine.

He has conducted research on functional data analysis for more than a decade. His experience has led him to develop a new graduate student topic course, STAT 5554 Functional Data Analysis, at Virginia Tech. The course has attracted a total of over 30 graduate students, including 3 from non-statistics disciplines, since its debut a few years ago. His FTC short course will be an application-driven version with emphasis on hands-on experience with common functional data analysis techniques.

Bootstrap Methods and Permutation Tests

Tim Hesterberg, Instacart

We begin with a graphical approach to bootstrapping and permutation testing, illuminating basic statistical concepts of standard errors, confidence intervals, p-values and significance tests.

We consider a variety of statistics (mean, trimmed mean, regression, etc.), and a number of sampling situations (one-sample, two-sample, stratified, finite-population), stressing the common techniques that apply in these situations. We’ll look at applications from a variety of fields, including telecommunications, finance, and biopharma.

These methods let us do confidence intervals and hypothesis tests when formulas are not available. This lets us do better statistics, e.g., use robust methods (we can use a median or trimmed mean instead of a mean, for example). They can help clients understand statistical variability. And some of the methods are more accurate than standard methods.

CONTACT: timhesterberg@gmail.com

DR. TIM HESTERBERG is a Staff Data Scientist at Instacart.  He previously worked at Google, Insightful, Franklin & Marshall College, and Pacific Gas & Electric Co. He received his Ph.D. in Statistics from Stanford University, under Brad Efron, and is a Fellow of the American Statistical Association and AAAS.  He is author of the “Resample” package for R, Chihara and Hesterberg “Mathematical Statistics with Resampling and R” (2018, 3e in press), and “What Teachers Should Know about the Bootstrap: Resampling in the Undergraduate Statistics Curriculum”, The American Statistician 2015.