A structured overview on the use of computational simulators for teaching statistical methods

ABSTRACT

Modern technology is affecting what is taught and how statistics is taught. Computer intensive methods like bootstrapping, generalized regression, coordinate exchange algorithms and computer experiment emulators are now part of the statistical toolkit. In parallel with these new methods and tools, a similar evolution appears in statistical education. Simulators offer opportunities for hands-on experience in the classroom, bridging the gap between theory and practice. In this article, we map a wide range of simulation based tools used in teaching statistical methods, putting them in the context of a quality ladder representing various maturity levels of organizations and course level sophistication. The mapping is designed to help educators assess and integrate options related to the incorporation of simulators as teaching aids in an educational and pedagogical roadmap. The mapping is applied to specific simulation products but provides a general framework that can be expanded and used in a generic way.

KEYWORDS:

• pedagogical roadmap
• process simulators
• statistics curriculum
• statistical education
• the quality ladder

Dedication

This article is dedicated to Tony Greenfield who challenged us continuously to improve how we provide education in statistical methods and tools. The Aluminium Wheels simulator is only one example of his breakthrough contributions.

Additional information

Notes on contributors

Marco Reis

About the authors

Marco S. Reis, Ph.D., Professor in the Department of Chemical Engineering of the University of Coimbra, Portugal. He is currently President of the European Network for Business and Industrial Statistics (ENBIS) and responsible for the Process Systems Engineering (PSE) research group at the department of Chemical Engineering of the University of Coimbra. His research interests are centered on the field of process systems engineering (system identification, monitoring, and control), chemometrics, and industrial statistics. Other areas of interest include multivariate image analysis, systems biology, and process improvement through initiatives such as six-sigma and lean manufacturing. He has published ∼70 articles in international journals or series of books with peer reviewing, 4 book chapters, and 2 books, and authored or co-authored more than 100 presentations in international congresses.

Ron S. Kenett

Ron S. Kenett, Ph.D., Chairman of the KPA Group, Israel, Research Professor, University of Turin, Italy and International Professor, NYU Tandon School of Engineering, Center for Risk Engineering, NY, USA. Ron is Past President of the Israel Statistical Association (ISA) and of the European Network for Business and Industrial Statistics (ENBIS). He authored and co-authored over 200 papers and 12 books. His textbook with Shelley Zacks on Modern Industrial Statistics with applications in R, MINITAB and JMP (Wiley, 2014) has been translated into Spanish and Chinese. He has been editor in chief and member of the editorial board of several international journals and was awarded the 2013 Greenfield Medal by the Royal Statistical Society in recognition for excellence in contributions to the applications of Statistics. He is member of the National Public Advisory Council for Statistics Israel, member of the Executive Academic Council, Wingate Academic College and member of the board of several high-tech and biotech companies.