The Statistics Division is looking for two speakers to represent the division at the ASQ Audit Division Conference held in Dallas, Texas on October 12-13, 2017. We are looking for presentations that provide interaction and active learning. Attendees should leave each session with tools they can use immediately and for future audit planning. The presentation will be 45 minutes in length and can focus on sampling methodology and introductory statistical applications. A complimentary conference registration will be awarded. Each session presenter will be responsible for their own hotel, transportation arrangements and expenses. If you are interested in speaking at this conference, contact Steven Schuelka at firstname.lastname@example.org to obtain the speakers form. Submissions are due May 15, 2017.
The Statistics Division of the American Society for Quality is pleased to announce the availability of $7500 scholarships to support students who are enrolled in, or are accepted into enrollment in, a master’s degree or higher program with a concentration in applied statistics and/or quality management. This includes the theory and application of statistical inference, statistical decision-making, experimental design, analysis and interpretation of data, statistical process control, quality control, quality assurance, quality improvement, quality management and related fields. The emphasis is on applications as opposed to theory. Studies must take place at North American institutions.
Year 2016-17 scholarship winners are:
Mr. Andrew Walter, University of Kansas in the M.S. category, and
Mr. Matthew Keefe, Virginia Tech., in the Ph.D. category.
During the last 19 years, scholarships totaling over $280,000 have been awarded to 52 deserving students.
Qualified applicants must have graduated in good academic standing in any field of undergraduate study. Scholarship awards are based on demonstrated ability, academic achievement, industrial and teaching experience, involvement in student or professional organizations, faculty recommendations, and career objectives.
Application instructions and forms should be downloaded from:
Forms for the 2017-18 academic year will be accepted only between January 1 and April 1, 2017.
For more information, contact:
Dr. Lynne B. Hare
55 Buckskin Path
Plymouth, MA 02360
Governing Board: Lynne Hare, J. Stuart Hunter, Tom Murphy, Dean V. Neubauer, Robert Perry, Susan Schall, Ronald Snee, J. Richard Trout and Neil Ullman.
Join the Statistics Division on Tuesday, April 25th at 12:00PM Eastern for a webinar to be given by Dan O’Leary.
Acceptance sampling is a common method to determine if the output of a process, the product, should move to the next process or ship to the customer. One common sampling technique is attribute sampling using ANSI/ASQ Z1.4:2003. This presentation explains how to use the standard to determine sampling plans (single, double, and multiple) using an incoming inspection example. The quality engineer needs to understand the binomial distribution, the basis for these sampling plans. This leads to characterizing the risk, described by the operating characteristic (OC) curve. Understanding the OC curve helps determine some distinguished points related to the acceptable and rejectable quality levels (AQL & RQL) and the associated producer’s and consumer’s risk. The standard can help screen rejected lots; the average outgoing quality (AOQ) and average outgoing quality limit (AOQL) describe the plan’s performance.
Join the Statistics Division on Wednesday, March 29rd at 2:00PM Eastern for a webinar to be given by Matthew Barsalou
This webinar will describe how to conduct an empirical root cause analysis. A brief mention of reasons for performing a root cause analysis will be given followed by typical tools. A case will be made for using empirical root cause analysis. The correct application of the scientific method will also be explained. This includes concepts such as Exploratory Data Analysis for hypothesis generation as well as the attributes of a good hypothesis. John Platt’s version of the scientific method will be explained using examples and these concepts will be combined into a Plan-Do-Check-Act process.
The concept of empirical root cause analysis will be depict in the form of a graphic which will show the various steps to combine Exploratory Data Analysis, the scientific method, and Box’s iterative inductive deductive process into a Plan-Do-Check-Act like method. Examples will be presented to illustrate these concepts. Real world examples will include the analysis of a cracked cable coating, vibration sensor failures and a bushing and bracket failure. This final section of the presentation will provide advice on how both the customer and supplier can better prepare for a complaint. The view of both customers and suppliers will be represented including information the customer must provide with a claim as well as how a supplier can better prepare for a customer issue.
The Statistics Division is seeking volunteers to present one hour webinars on any of the topics listed below as they relate to the Certified Quality Engineer (CQE) Exam. For instance, a presentation on Design and Analysis of Experiments might discuss Factorial Designs and Analysis of Variance. Please click here for more information about the CQE Exam. We plan that these webinars will become part of the Statistics Division body of knowledge to which we may refer any society member. To that end, the webinar will be recorded and afterward posted on the division Youtube channel. Anyone interested in presenting such a webinar between April and December of 2017, inclusive, should contact Adam Pintar by email at email@example.com.
- Acceptance Sampling
- Probability Distributions
- Statistical Decision-Making (e.g., hypothesis testing)
- Relationships Between Variables (e.g., linear regression)
- Statistical Process Control
- Process and Performance Capability
- Design and Analysis of Experiments
Join the Statistics and Innovation Divisions of ASQ on Thursday, February 23rd at 12:00PM-1:00PM Eastern Standard Time for a webinar to be given by Kymm Hockman, Innovation Process Champion, DuPont Electronics & Communications.
In today’s competitive environment, companies are looking to remain financially strong by increasing their profitability. Innovation leading to business growth is increasingly important. In this presentation we discuss the unique roles that statistics and statisticians can play in facilitating and leading innovation efforts. Data-based decision making, systems thinking, an independent perspective and the ability to influence others all work together to equip and position the statistician to lead growth project work to successful commercial success. Examples from real statistician-led projects will illustrate the role of statistics in making wise commercialization decisions. Finally, recommendations will be discussed on how the statistics field will need to broaden the skill base to prepare innovation leaders of the future.
Join the Statistics Division on Tuesday, December 20th at 9:00 – 10:00 AM PST for a webinar to be given by Eduardo Santiago, Technical Training Specialist at Minitab.
El análisis de capacidad de un proceso requiere de la evaluación de la variación y la comparación del desempeño del proceso con respecto a sus especificaciones. Tradicionalmente decimos que un proceso es capaz si la mayoría de las partes que produce se encuentran dentro de especificación, en otras palabras si la tasa de defectos es pequeña. Inicialmente este análisis fue popularizado por Joseph Juran en su Manual de Control de Calidad, y hoy en día la capacidad de un proceso se mide por lo regular en función de su Cpk y Ppk.
A pesar de la simplicidad en el cálculo de dichos estadísticos, la falta de entendimiento de estos métricos de capacidad pueden conducirnos a su malinterpretación o peor aún la manipulación de tales sin haber antes mejorado el proceso. Por lo tanto es importante entender los supuestos necesarios para validar e interpretar los estadísticos de capacidad. En esta presentación, discutiremos cómo validar los supuestos, pero más importante, cómo lidiar con situaciones cuando los supuestos han sido violados.
En este seminario consideraremos tres enfoques para tratar datos que no están normalmente distribuidos:
1) Hablaremos de las transformaciones de Box-Cox y Johnson
2) Discutiremos la utilización de distribuciones no-normales, y por último,
3) Utilizaremos métodos alternos para estimar con mayor exactitud y precisión los estadísticos de capacidad.
The William G. Hunter Award is presented annually to encourage the creative development and application of statistical techniques to problem-solving in the quality field. Named in honor of the Statistics Division’s founding chairman, the award recognizes an individual who demonstrates Bill Hunter’s most enduring qualities of excellence in statistics as a communicator, a consultant, an educator, an innovator, an integrator of statistics with other disciplines and an implementer who obtains meaningful results.
This year’s winner is A. Blanton Godfrey. He was nominated by Jeffrey Hooper, with testimonials from Maureen Bisognano, Robert Waller, J. Stuart Hunter, Roger Hoerl, Emily Parker, Charles Little, Ronald D. Snee, Craig Long, and Mark J. Fischer. Dr. Godfrey is a statistician and the Joseph D. Moore Distinguished University Professor in the College of Textiles at North Carolina State University. He earned a bachelor’s degree in physics from Virginia Polytechnic Institute as well as a master’s degree and PhD in statistics from Florida State University. He is the Former Head of the Quality Theory and Technology Department of AT&T Bell Laboratories and the Former Chairman and Chief Executive Officer of Juran Institute, Inc. He is a fellow of the American Society for Quality, American Statistical Association, World Academy of Productivity Sciences, and the Royal Society for the encouragement of Art, Manufacturers, and Commerce.
Blan Godfrey is well known to many in academia and industry. He has received the Edwards Award and the Distinguished Service Medal. He has given the Deming Lecture and presented the W. J. Youden Address. He is Co-author of Modern Methods for Quality Control and Improvement and Curing Health Care: New Strategies for Quality Improvement. Blan is the Founding Editor of the Six Sigma Forum, a Member of Committee for ISO 9000 and has served on numerous boards and executive committees.
Blan was unable to attend the Fall Technical Conference in Minneapolis. Bill Meeker accepted the award on Blan’s behalf.
Past recipients are available here: http://williamghunter.net/award/.
The ASQ Statistics Division is proud to announce that the Lloyd Nelson award was presented at this year’s Fall Technical Conference. The award recognizes the paper in the ASQ journal, Journal of Quality Technology (JQT), which has “the greatest immediate impact to practitioners”.
As the editor of ASQ’s Industrial Quality Control, Nelson in the 1960s proposed dividing the magazine into two publications. Eventually, he convinced the board to create the general-interest magazine Quality Progress and the more technical quarterly Journal of Quality Technology. He became the first editor of the Journal of Quality Technology. Nelson was honored in 2001 as one of the first recipients of ASQ’s Distinguished Service Medal. He was named an ASQ Fellow in 1964 and was awarded the Shewhart Medal in 1978. Nelson also served on ASQ’s Awards Board and Shewhart and Deming Medal Committees, and was a long-time member of the Journal of Quality Technology’s Editorial Review Board.
This year, the Lloyd Nelson Award was presented to Russell V. Lenth for his outstanding paper “The Case Against Normal Plots of Effects”, published in Volume 47, Issue 2. Russ attended the Fall Technical Conference in October in Minneapolis to receive the plaque.
The abstract for the award winning paper reads as follows:
When analyzing effects in an unreplicated experiment, normal or half-normal plots of the effects (also called Daniel plots) are a popular way to visualize them and to judge which are active. This article discusses the ways in which these plots can be confusing and misleading. There are other methods available that are less subjective, easier to explain, more powerful, and less likely to be misinterpreted. I recommend against using Daniel plots, even as a supplement to one of these better analyses.
This paper has been given Open Access by the publisher until October 2017 and can be found at the following link: http://rube.asq.org/quality-technology/2015/04/engineering/the-case-against-normal-plots-of-effects.pdf
The ASQ Statistics Division is proud to announce that the Søren Bisgaard award was presented at this year’s Fall Technical Conference. The award recognizes the paper in the ASQ journal, Quality Engineering (QE), with the “greatest potential for advancing the practice of quality improvement”.
Søren Bisgaard, Isenberg Professor of Management at the University of Massachusetts-Amherst, died in Boston on December 14, 2009, after a year-long struggle against mesothelioma. Bisgaard was an expert on quality management and applied statistics who had an international reputation. He was recognized for his work and granted several awards, including the Ellis R. Ott Award (1990), the Wilcoxon Prize (1998), the Shewell Award (1981 and 1987), the Brumbaugh Award (1987, 1995, and 2008), the Shewhart Medal (2002), and the George Box Award (2004). He was a Fellow of the American Statistical Association and American Society for Quality and an academician of the International Academy for Quality. Søren was a strong advocate of quality improvement and was for many years the author of the “Quality Quandaries” column in QE.
This year, the Søren Bisgaard Award was presented to Rick Picard, Michael S. Hamada, Geralyn M. Hemphill, and Robert E. Hackenberg for their outstanding paper “Accounting for Nonrandomly Sampled Data in Nonlinear Regression”, published in Volume 27, Issue 2. Rick Picard attended the Fall Technical Conference in October in Minneapolis to receive the plaque.
The abstract for the award winning paper reads as follows:
We analyze data that are “cherry picked” (i.e., nonrandomly sampled) from a population and are then used for regression modeling and prediction. Nonrandom data are encountered in numerous situations, and the application of standard statistical methods developed for random samples can easily lead to incorrect conclusions. A case study is presented to illustrate the related issues, as well as the repercussions of erroneously ignoring the nonrandom sampling.
This paper has been given Open Access by the publisher through September 2017 and can be accessed at the following link: http://www.tandfonline.com/doi/full/10.1080/08982112.2014.933979