Quality Engineering and Taguchi Methods: a Perspective
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IMS Presidential Address(Es) Each Year, at the End of Their Term, the IMS CONTENTS President Gives an Address at the Annual Meeting
Volume 50 • Issue 6 IMS Bulletin September 2021 IMS Presidential Address(es) Each year, at the end of their term, the IMS CONTENTS President gives an address at the Annual Meeting. 1 IMS Presidential Addresses: Because of the pandemic, the Annual Meeting, Regina Liu and Susan Murphy which would have been at the World Congress in Seoul, didn’t happen, and Susan Murphy’s 2–3 Members’ news : Bin Yu, Marloes Maathuis, Jeff Wu, Presidential Address was postponed to this year. Ivan Corwin, Regina Liu, Anru The 2020–21 President, Regina Liu, gave her Zhang, Michael I. Jordan Address by video at the virtual JSM, which incorpo- 6 Profile: Michael Klass rated Susan’s video Address. The transcript of the two addresses is below. 8 Recent papers: Annals of 2020–21 IMS President Regina Liu Probability, Annals of Applied Probability, ALEA, Probability Regina Liu: It’s a great honor to be the IMS President, and to present this IMS and Mathematical Statistics Presidential address in the 2021 JSM. Like many other things in this pandemic time, this year’s IMS Presidential Address will be also somewhat different from those in the 10 IMS Awards past. Besides being virtual, it will be also given jointly by the IMS Past President Susan 11 IMS London meeting; Olga Murphy and myself. The pandemic prevented Susan from giving her IMS Presidential Taussky-Todd Lecture; Address last year. In keeping with the IMS tradition, I invited Susan to join me today, Observational Studies and I am very pleased she accepted my invitation. Now, Susan will go first with her 12 Sound the Gong: Going 2020 IMS Presidential Address. -
STATISTICS in the DATA SCIENCE ERA: a Symposium to Celebrate 50 Years of Statistics at the University of Michigan
STATISTICS IN THE DATA SCIENCE ERA: A Symposium to Celebrate 50 Years of Statistics at the University of Michigan September 20-21, 2019 Rackham Graduate School 915 E. Washington Street Ann Arbor, MI 48109 A Symposium to Celebrate 50 Years of Statistics at the University of Michigan 1 PROGRAM Friday, September 20, 2019 Saturday, September 21, 2019 9:30-10:30 ...............Coffee and Registration 8:30-9:00 ................ Coffee 10:30-10:50 ..............Opening Remarks Invited Session 2 Chair: Snigdha Panigrahi, University of Michigan Xuming He and Dean Anne Curzan 9:00-9:30 ................ Adam Rothman, University of Minnesota Shrinking Characteristics of Precision Matrix Keynote Session 1 Chair: Ambuj Tewari, University of Michigan Estimators 10:50-11:50 ...............Susan Murphy, Harvard University Online Experimentation with Learning Algorithms 9:30-10:00 ............... Bodhisattva Sen, Columbia University in a Clinical Trial Multivariate Rank-based Distribution-free Nonparametric Testing using Measure 11:50-12:00 ...............Group Photo Transportation PROGRAM 12:00-1:30 ................Lunch and Poster Session 10:00-10:30 ............. Min Qian, Columbia University Personalized Policy Learning using Longitudinal University of Michigan Invited Session 1 Chair: Ziwei Zhu, Mobile Health Data 1:30-2:00 .................Sumanta Basu, Cornell University Large Spectral Density Matrix Estimation by 10:30-11:00 .............. Coffee Break Thresholding Invited Session 3 Chair: Kean Ming Tan, University of Michigan 2:00-2:30 .................Anindya Bhadra, Purdue University 11:00-11:30 ............... Ali Shojaie, University of Washington Horseshoe Regularization for Machine Learning in Incorporating Auxiliary Information into Learning Complex and Deep Models Directed Acyclic Graphs 2:30-3:00 .................Coffee Break 11:30-12:00 .............. -
The Completely Sufficient Statistician (Css)
Kansas State University Libraries New Prairie Press Conference on Applied Statistics in Agriculture 2002 - 14th Annual Conference Proceedings THE COMPLETELY SUFFICIENT STATISTICIAN (CSS) Ralph G. O'Brien Follow this and additional works at: https://newprairiepress.org/agstatconference Part of the Agriculture Commons, and the Applied Statistics Commons This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License. Recommended Citation O'Brien, Ralph G. (2002). "THE COMPLETELY SUFFICIENT STATISTICIAN (CSS)," Conference on Applied Statistics in Agriculture. https://doi.org/10.4148/2475-7772.1196 This is brought to you for free and open access by the Conferences at New Prairie Press. It has been accepted for inclusion in Conference on Applied Statistics in Agriculture by an authorized administrator of New Prairie Press. For more information, please contact [email protected]. Conference on Applied Statistics in Agriculture Kansas State University Applied Statistics in Agriculture 1 THE COMPLETELY SUFFICIENT STATISTICIAN (CSS) Ralph G. O'Brien Department ofBiostatistics and Epidemiology Cleveland Clinic Foundation, Cleveland, OH 44195 Today's ideal statistical scientist develops and maintains a broad range of technical skills and personal qualities in four domains: (1) numeracy, in mathematics and numerical computing; (2) articulacy and people skills; (3) literacy, in technical writing and in programm,ing; and (4) graphicacy. Yet, many of these components are given short shrift in university statistics programs. Can even the best statistician today really be "completely sufficient?" Introduction Let me start by saying that I was absolutely delighted, grateful, and humbled to be invited to give the workshop and keynote address at this meeting. -
QEM the Mission of the Quality and Engineering Management
QEM The mission of the Quality and Engineering Management (QEM) program is to provide a pathway for employees who hold degrees in technical and non-technical areas to acquire the knowledge and skills necessary to be more successful in all areas of manufacturing. The master’s program is 30 hours long and has the option of either a thesis or non-thesis track. The program is 100% online and provides a pathway for employees who hold degrees in technical and nontechnical areas to acquire the knowledge and skills necessary to be more successful in all areas of manufacturing or to improve efficiency in service sector units. The emphasis on leadership provides critical skills for advancing into manufacturing leadership positions. Specific benefits of the program are: Designed to provide individuals with baccalaureate degrees in technology or business related fields with an in-depth study of the organizational, technical, and strategic tools commonly used in manufacturing to improve productivity Targeted to working professionals and other individuals who have a background in one of these fields, but are seeking to expand their knowledge across these disciplines Emphasizes the applications of these tools to address quality, technology implementation and productivity issues in manufacturing related industries to help manufacturers standardize procedures, measure performance, improve customer satisfaction and manage resources more wisely Because the degree is 100% online and designed for working professionals, students can continue to be employed while going through the program. Working on a master’s degree is a great way to signal to an employer that you are preparing yourself to move up and take on more responsibility. -
A R Software for Wavelets and Statistics
A R Software for Wavelets and Statistics Here is a list and a brief description of some important R packages related to/that make use of wavelets. Such packages are important as they enable ideas to be reproduced, checked, and modified by the scientific community. This is probably just as important as the advertisement of the ideas through scientific papers and books. This is the ‘reproducible research’ view expressed by Buckheit and Donoho (1995). Making software packages freely available is an extremely valuable service to the community and so we choose to acclaim the author(s) of each package by displaying their names! The descriptions are extracted from each package description from CRAN. The dates refer to the latest updates on CRAN, not the original publication date. Please let me know of any omissions. adlift performs adaptive lifting (generalized wavelet transform) for irregu- larly spaced data, see Nunes et al. (2006). Written by Matt Nunes and Marina Knight, University of Bristol, UK, 2006. brainwaver computes a set of scale-dependent correlation matrices between sets of preprocessed functional magnetic resonance imaging data and then performs a ‘small-world’ analysis of these matrices, see Achard et al. (2006). Written by Sophie Achard, Brain Mapping Unit, University of Cambridge, UK, 2007. CVThresh carries out level-dependent cross-validation for threshold selection in wavelet shrinkage, see Kim and Oh (2006). Written by Donghoh Kim, Hongik University and Hee-Seok Oh, Seoul National University, Korea, 2006. DDHFm implements the data-driven Haar–Fisz transform as described in 6.5, see references therein. Written by Efthimios Motakis, Piotr Fryzlewicz and Guy Nason, University of Bristol, Bristol, UK. -
Quality Management in Japan
Section 3 Taguchi’s Methods versus Other Quality Philosophies Taguchi’s Quality Engineering Handbook. Genichi Taguchi, Subir Chowdhury and Yuin Wu Copyright © 2005 Genichi Taguchi, Subir Chowdhury, Yuin Wu. Quality Management 39 in Japan 39.1. History of Quality Management in Japan 1423 Dawn of Quality Management 1423 Period Following World War II 1424 Evolution from SQC to TQC 1426 Diffusion of Quality Management Activity 1427 Stagnation of Quality Management in the Second Half of the 1990s 1428 39.2. Quality Management Techniques and Development 1428 Seven QC Tools and the QC Story 1428 Quality Function Deployment to Incorporate the Customers’ Needs 1430 Design of Experiments 1431 Quality Engineering 1435 References 1440 39.1. History of Quality Management in Japan The idea that the quality of a product should be managed was born in Japan a Dawn of Quality long time ago. We can see some evidence that in making stone tools in the New Management Stone Age, our ancestors managed quality to obtain the same performance around 10,000 years ago. In the Jomon Period, a few centuries before Christ, earthenware were unified in terms of material and manufacturing method. In the second half of the Yayoi Period, in the second or third century, division of work (specialization) occurred in making earthenware, agricultural implements, weapons, and ritual tools. After the Tumulus Period, in the fourth or fifth century, the governing or- ganization summoned specialized engineers from the Asian continent and had them instruct Japanese engineers. For the geographical reason that Japan is a small country consisting of many archipelagoes, this method of convening leaders from technologically advanced foreign countries has been continued until the twentieth century. -
Indian Statistical Institute
CHAPTER VII INDIAN STATISTICAL INSTITUTE 7.1 The Indian Statistical Institute (ISI) came into being with the pioneering initiative and efforts of Professor P.C. Mahalanobis in early thirties (registered on 28 April, 1932). The Institute expanded its research, teaching, training and project activities and got national/international recognitions. The Institute was recognized as an “Institute of National Importance” by an Act of Parliament, known as “Indian Statistical Institute Act No.57 of 1959”. Significantly, Pandit Jawaharlal Nehru, the then Prime Minister piloted the bill in the Parliament in 1959. This Act conferred the right to hold examinations and award degrees/diplomas in Statistics. Degree courses viz. Bachelor of Statistics (B.Stat.), Master of Statistics (M.Stat.) and post graduate diplomas in SQC & OR and Computer Science were started from June 1960. The Institute was also empowered to award Ph.D./D.Sc. degree from the same year. Subsequently, Master of Technology courses in Computer Science and in Quality, Reliability & Operations Research were also started. The Institute’s scope was further enlarged to award degrees/diplomas in Statistics, Mathematics, Quantitative Economics, Computer Science and such other subjects related to Statistics by virtue of “Indian Statistical Institute (Amendment) Act, 1995. The Institute took up research activities not only in Statistics/Mathematics but also in Natural and Social Sciences, Physics and Earth Sciences, Biological Sciences, Statistical Quality Control & Operations Research and Library and Information Sciences. 7.2 Sankhya – The Indian Journal of Statistics, being published by the Institute since 1933, is considered as one of the leading Statistical Journals of the world. -
Defining the Quality of Business Processes
Defining the Quality of Business Processes Robert Heinrich1 and Barbara Paech2 Abstract: Business process models are used to gain a joint understanding of complex processes. Often they are applied in change projects where either the supporting IT or the processes themselves or both are to be improved. So it is an important question to assess the quality of the modeled business processes. However, so far there is no standard definition of the quality of a business process. Furthermore, business process models are not tuned to capture quality aspects. The goal of our work is to collect important quality characteristics and attributes of processes and to enhance business process modeling languages with means to express these attributes. This paper is a first step in this direction. We define a first set of quality characteristics, attributes and measures for these attributes in a business process. As an example we evaluate how well these measures can be expressed in a BPMN model. 1Introduction Business process models aim at providing a joint understanding of business processes. Therefore, they typically cover information about structure and behavior like description of activities or decisions within the process. But they do not aim at providing quality information. Quality information, for example, is information about the reliability or the usability of a business process. That information is of high interest for organizations because business process models are often used in change projects where either the supporting IT or the processes themselves or both are to be enhanced. Furthermore, the quality of a business process highly affects the success of an organization. -
Quality Management
DEPARTMENT OF HEALTH AND HUMAN SERVICES ENTERPRISE PERFORMANCE LIFE CYCLE FRAMEWORK <OPDIV Logo> PPPRRRAAACCCTTTIIICCCEEESSS GGGUUUIIIDDDEEE QUALITY MANAGEMENT Issue Date: <mm/dd/yyyy> Revision Date: <mm/dd/yyyy> Document Purpose This Practices Guides is a brief document that provides an overview describing the best practices, activities, attributes, and related templates, tools, information, and key terminology of industry-leading project management practices and their accompanying project management templates. This guide is focused on practices that should be applied to ensure the project will satisfy business needs, objectives, and requirements. Background The Department of Health and Human Services (HHS) Enterprise Performance Life Cycle (EPLC) is a framework to enhance Information Technology (IT) governance through rigorous application of sound investment and project management principles and industry’s best practices. The EPLC provides the context for the governance process and describes interdependencies between its project management, investment management, and capital planning components. The Quality Management Plan is an important component of a Project Management Plan deliverable. The discipline of quality management complements project management with its focus on customer satisfaction, prevention of defects over inspection, management responsibility, and continuous improvement. Practice Overview Project Managers should assume responsibility for the development of quality policies for the project and the coordination of related activities, in compliance with any organizational and/or regulatory standards. A Quality Management Plan documents this information and describes the authorities, policies, tools and techniques that are specific to ensuring project excellence, reducing cost and eliminating unnecessary corrections and/or changes. It is important to note that the concept of quality does not necessarily require perfection. -
Quality Management Tekst Do Nagrania Audio
Quality Management Tekst do nagrania audio Kurs Techniczny Angielski 2.0 QUALITY MANAGEMENT 1 Quality Management Quality management ensures that an organization, product or service is consistent. It has four main components: quality planning, quality assurance, quality control and quality improvement. Quality management is focused not only on product and service quality, but also on the means to achieve it. Quality management, therefore, uses quality assurance and control of processes as well as products to achieve more consistent quality. What a customer wants and is willing to pay for it determines quality. It is written or unwritten commitment to a known or unknown consumer in the market . Thus, quality can be defined as fitness for intended use or, in other words, how well the product performs its intended function. Evolution Walter A. Shewhart made a major step in the evolution towards quality management by creating a method for quality control for production, using statistical methods, first proposed in 1924. This became the foundation for his ongoing work on statistical quality control. W. Edwards Deming later applied statistical process control methods in the United States during World War II, thereby successfully improving quality in the manufacture of munitions and other strategically important products. Quality leadership from a national perspective has changed over the past decades. After the second world war, Japan decided to make quality improvement a national imperative as part of rebuilding their economy, and sought the help of Shewhart, Deming and Juran, amongst others. W. Edwards Deming championed Shewhart's ideas in Japan from 1950 onwards. He is probably best known for his management philosophy establishing quality, productivity, and competitive position. -
Introduction to Statistically Designed Experiments, NREL
Introduction to Statistically Designed Experiments Mike Heaney ([email protected]) ASQ Denver Section 1300 Monthly Membership Meeting September 13, 2016 Englewood, Colorado NREL/PR-5500-66898 Objectives Give some basic background information on statistically designed experiments Demonstrate the advantages of using statistically designed experiments1 1 Often referred to as design of experiments (DOE or DOX), or experimental design. 2 A Few Thoughts Many companies/organizations do experiments for Process improvement Product development Marketing strategies We need to plan our experiments as efficiently and effectively as possible Statistically designed experiments have a proven track record (Mullin 2013 in Chemical & Engineering News) Conclusions supported by statistical tests Substantial reduction in total experiments Why are we still planning experiments like it’s the 19th century? 3 Outline • Research Problems • The Linear Model • Key Types of Designs o Full Factorial o Fractional Factorial o Response Surface • Sequential Approach • Summary 4 Research Problems Research Problems Is a dependent variable (Y) affected by multiple independent variables (Xs)? Objective: Increase bearing lifetime (Hellstrand 1989) Y Bearing lifetime (hours) X1 Inner ring heat treatment Outer ring osculation (ratio Outer Ring X2 between ball diameter and outer ring raceway radius) Ball X3 Cage design Cage Inner Ring 6 Research Problems Objective: Hydrogen cyanide (HCN) removal in diesel exhaust gas (Zhao et al. 2006) Y HCN conversion X1 Propene -
Appendix A: Mathematical Formulas
Appendix A: Mathematical Formulas Logarithms Inab = \na -\-\nb', \na/b = \na— \nb; \na^=b\na Geometric Series If Irl < 1, we find that oo oo E ar^ = ; y^akr^ = r; ^=0 A:=o y^ n Z-^ 1 -r ^^ 1 -r ^=1 k=0 Limits UHospitaVs rule. Suppose that \\vOix->XQ fix) = lim;c-^A:o sM = 0* or that liirix-^xo Z^-^) = l™x^jco SM — =t^^- Then, under some conditions, we can write that y fix) ^. fix) um = hm . x-^XQ g{x) x^xo g^{x) If the functions f\x) and g\x) satisfy the same conditions as f(x) and g(x), we can repeat the process. Moreover, the constant XQ may be equal to ±oo. Derivatives Derivative of a quotient: d fix) gix)fix) - fix)g\x) = 7^ 11 gix) ^0. dx gix) g^ix) 340 Appendix A: Mathematical Formulas Remark. This formula can also be obtained by differentiating the product f(x)h(x), where/Z(A:) := l/g(x). Chain rule. If w = ^(jc), then we have: d d du , , ^fiu) = —f(u) . — = f\u)g\x) = AgixWix). dx du dx For example, if w = jc^, we calculate — exp(M^) = exp(w^)2w • 2x = 4x^ expU"*). dx Integrals Integration by parts: j udv = uv — I vdu. Integration by substitution. If the inverse function g~^ exists, then we can write that rh rd j f(x)dx = j f[g(y)]g{y)dy, where a = g(c) ^ c = g ^(a) and b = g(d) <^ d = g ^(b). For example, / e''^'dx-^=^' f 2yeydy=2yey\l-2 f e^ dy = 2e^-\-2.