Paper Authors

Abstract

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

• Citation

• Format
  • APA
  • APA - LaTeX bibitem
  • MLA
  • MLA - LaTeX bibitem
  • Bibtex
  • EndNote - RIS

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