Salt Lake City, Utah
June 23, 2018
June 23, 2018
July 27, 2018
Computing and Information Technology
For many undergraduate and graduate engineering technology students, data collection and data analysis—including methodology, statistical analysis, and data preparation—is the most daunting and frustrating aspect of working on capstone senior projects and master’s theses. This paper provides an introduction to a number of statistical considerations, specifically statistical hypotheses, statistical methods, appropriate analytic techniques, and sample size justifications. Statistical analysis of data utilizing statistical software packages, including MATLAB, SPSS, Minitab, EXCEL, and R, will be shown for scientific applications, quality assurance, corporate finance modeling and other purposes. Our goal is to prep our students so that they are adept in a variety of basic and advanced statistical methods and are able to use these tools judiciously to fully understand and interpret their analyses and results. This paper will explain how this learning and teaching module is instrumental for progressive learning of students; the paper will also demonstrate how the numerical and integral algorithms are derived and computed through leverage of EXCEL, Minitab and MATLAB data structures. As a result, there will be a discussion concerning the comparison of SPSS, EXCEL, Minitab and MATLAB programming, as well as student feedback. The result of this new approach is expected to strengthen the capacity and quality of our undergraduate and graduate degree programs, in addition to enhancing overall student learning and satisfaction.
Muqri, M. R., & Harper, N., & Muqri, H., & Wesr, B. K. (2018, June), Leveraging the power of Matlab, SPSS, EXCEL, and Minitab for Statistical Analysis and Inference Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. https://peer.asee.org/30772
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