Application Of Statistics In Engineering Technology Programs

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Wei Zhan
Rainer Fink
Alex Fang


Engineering education, error analysis, Monte Carlo methods, simulation, statistics


Statistics is a critical tool for robustness analysis, measurement system error analysis, test data analysis, probabilistic risk assessment, and many other fields in the engineering world. Traditionally, however, statistics is not extensively used in undergraduate engineering technology (ET) programs, resulting in a major disconnect from industry expectations. The research question: How to effectively integrate statistics into the curricula of ET programs, is in the foundation of this paper. Based on the best practices identified in the literature, a unique “learning-by-using” approach was deployed 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 were introduced to students in different courses. Design of experiments (DOE), regression, and the Monte Carlo method were illustrated with practical examples before the students applied the newly understood tools to specific problems faced in their engineering projects. Industry standard software was used to conduct statistical analysis on real results from lab exercises. The result from a pilot project at Texas A&M University indicates a significant increase in using statistics tools in course projects by students.  Data from student surveys in selected classes indicate that students gained more confidence in statistics.   These preliminary results show that the new approach is very effective in applying statistics to engineering technology programs.


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