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Teaching Statistics To Engineering Technology Students

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2009 Annual Conference & Exposition


Austin, Texas

Publication Date

June 14, 2009

Start Date

June 14, 2009

End Date

June 17, 2009



Conference Session

Innovative Curriculum and Practices in Engineering Technology

Tagged Division

Engineering Technology

Page Count


Page Numbers

14.1153.1 - 14.1153.11



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Paper Authors


Wei Zhan Texas A&M University Orcid 16x16

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Dr. Wei Zhan is an Assistant Professor of Electronics Engineering Technology at Texas A&M University. Dr. Zhan earned his D.Sc. in Systems Science from Washington University in 1991. From 1991 to 1995 he worked at University of California, San Diego and Wayne State University. From 1995 to 2006, he worked in the automotive industry as a system engineer. In 2006 he joined the Electronics Engineering Technology faculty at Texas A&M. His research activities include control system theory and applications to industry, system engineering, robust design, modeling, simulation, quality control, and optimization.

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Rainer Fink Texas A&M University

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Rainer Fink was born in Speyer, West Germany in 1966. He received the BS degree in biomedical engineering (1988), the MS degree in biomedical engineering (1992), and the Ph.D. in biomedical engineering (1995) from Texas A&M University. After finishing his Ph.D., he was a lecturer in the Bioengineering Program and the Department of Engineering Technology at Texas A&M University. In August 1996, he joined the Electronics Engineering Technology faculty at Texas A&M University. His research activities include mixed-signal testing, analog circuit design and biomedical electronics.

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Alex Fang Texas A&M University

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Dr. Alex Fang is an Assistant Professor in the Department of Engineering Technology and Industrial Distribution at Texas A&M University. He received the BS degree in aerospace engineering (1976) from Tamkang University in Taiwan, the MS degree in aerospace engineering (1987) and the Ph.D. degree in mechanical engineering (1996) from Texas A&M University. He joined the Manufacturing and Mechanical Engineering Technology faculty at Texas A&M in 2007. He teaches courses in the area of nondestructive testing (NDT), nonmetallic materials, and strength of materials. Dr Fang’s research interests are in the areas of ceramic grinding, lapping, and polishing, NDT, acoustics, genetic algorithm, and multi-objective optimization.

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NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Teaching Statistics to Electronics Engineering Technology Students

Abstract Statistics is an important tool for robustness analysis, measurement system error analysis, test data analysis, probabilistic risk assessing, and many other fields in the engineering world. However, traditionally statistics is not covered extensively in undergraduate engineering technology programs. Usually the students take a statistics course from the Statistics Department as a prerequisite for other engineering courses and seldom use the knowledge they learned in the course again, until they graduate from school and are faced with real-world statistics based engineering tasks. By then they have forgotten most of what they learned in the statistics course, or it was not relevant to the engineering applications encountered in the real-world.

Based on the results from existing literatures in the area of statistics education, a unique learning-by-using approach is proposed for the Electronics Engineering Technology program at Texas A&M University. Simple statistical concepts such as standard deviation of measurements, signal to noise ratio, and Six Sigma are introduced to students in different courses. Design of experiments (DOE), regression, and Monte Carlo method are illustrated with practical examples before the application of these tools to specific problems the students face in their engineering projects. Software is used to conduct statistical analysis.

Introduction During the past two decades there has been a trend in industries to use management philosophies with emphasis in the systematic use of statistical methods. The Japanese manufacturing industry has made a tremendous improvement in quality because of the wide use of statistical methods such as Total Quality Management. Other statistical tools such as Statistical Process Control (SPC) 38 and Six Sigma11,33 have also been proven effective in improving processes, product quality, and the corporate bottom lines. For example, Motorola credited the Six Sigma initiative for saving $940 million over three years and AlliedSignal reported a $1.5 billion savings in 199738. Other companies responded to the quality competition by adopting these statistical methods. For industries such as pharmaceutical and manufacturing industries, tools such as Six Sigma have become required knowledge for a successful engineer. While there is no doubt that today’s industry needs engineers with experience and knowledge of statistics3, most engineering students think that probability and statistics courses are useless, boring, and difficult. These course are too theoretical and appear to be unrelated to the engineering subject they study. As pointed out by Godfrey10: “We too often teach what appears to the students a collection of unrelated methods illustrated by examples taken from coin-tossing, card-playing and dice-rolling. And then we expect the students to be able to translate this wide

Zhan, W., & Fink, R., & Fang, A. (2009, June), Teaching Statistics To Engineering Technology Students Paper presented at 2009 Annual Conference & Exposition, Austin, Texas. 10.18260/1-2--4601

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