Document Type

Conference Paper

Publication Date

6-2018

DOI

10.18260/1-2--30689

Pages

1-14 pp.

Conference Name

2018 ASEE Annual Conference & Exposition, Salt Lake City, UT, June 24-27, 2018

Abstract

Statistical methods and procedures are very important in engineering applications. In most of the engineering fields electronic devices are used as sensing and controlling components. Lack of proper calibration of these devices and of performance analysis using different statistical methods may lead to erroneous measurements and results. In medical or manufacturing areas such errors in the experimental results could be catastrophic. Applying different statistical tests and procedures enhance the quality of engineering work. Traditionally, most engineering curricula have at least one required course in applied statistics in engineering, but that is not generally the case in engineering technology programs. Most of the engineering technology BS graduates work as field engineers and collect the data from different physical processes and do data analysis to validate the systems performances. Exposure to statistical methods use and data analysis will provide technology graduates with valuable skills in the current high-tech job market. This paper focuses on how statistical analysis and methods using hand calculations and software tools can be integrated in undergraduate engineering technology courses, enhancing the hands-on approach of real engineering projects with software assisted data analysis. Learning the skills of collecting experimental data from real processes and performing statistical analysis on it is the effective approach of solving engineering problems, and it provides higher learning outputs than simulation-based approach. Specifically, integration of statistical analysis was introduced in an industrial instrumentation class, in which the lab component included the use of various sensors and other measurement instruments. By the end of the class, students demonstrated newly acquired statistical skills by performing sensor calibration and they also applied simple linear regression analysis model on the experimental data

Rights

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2018 American Society for Engineering Education.

Original Publication Citation

Chitikeshi, S., & Hildebrant, J., & Popescu, O., & Ayala, O. M., & Jovanovic, V. M. (2018, June), Integrating statistical methods in engineering technology courses [Paper presentation]. 2018 ASEE Annual Conference & Exposition, Salt Lake City, Utah. https://peer.asee.org/30689

ORCID

0000-0003-0604-8606 (Ayala), 0000-0002-8626-903X (Jovanovic)

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