CEC Faculty Articles

Title

Making Probabilistic Methods Real, Relevant, and Interesting Using MATLAB

Event Date/Location

San Juan, PR

Document Type

Article

Date

11-1999

Publication Title

29th ASEE/IEEE Frontiers in Education Conference

ISSN or ISBN

0190-5848

First Page

13b4-2

Last Page

13b4-7

Description

Teaching probabilistic methods (PM) to undergraduate electrical engineering students is often difficult because of the increased conceptual level of the material compared to their previous learning experiences. However, with the help of software tools like MATLAB, an instructor can unravel the mysteries of probability theory and its marvelous array of applications. A typical PM course normally covers the following topics: set theory and Venn diagrams, probability axioms and probability distributions, basic statistics, hypothesis testing, regression analysis, random processes, correlation analysis, power spectrum, and optimum linear systems. If we were to teach these concepts in this particular order with traditional pencil and paper learning, we could end up with students who have merely learned to put a puzzle together by numbers instead from the actual picture. In this paper we will describe how MATLAB exercises and projects can be used to make a course in probability interesting, real, and relevant to the electrical engineering student. We will describe a teaching strategy that calls for students to apply the above mentioned concepts to a nonstationary data set as a means to motivate them, to promote active learning and to help them integrate the concepts. Finally, we will also describe a sample term project in which students are given a nonstationary data set that represents the output of some unknown system. Using this data set, students are asked to convert the data set to white noise through rhe application of a sequence of transformations using concepts from regression analysis, Fourier and speciral analysis, correlation analysis, and linear filtering, using MATLAB. They are asked to validate the model, which requires hypothesis testing, confidence intervals, cumulative periodogram testing, probability plots, etc. Assessment of student satisfaction has shown that this strategy of using MATLAB helped students grasp the concepts and learned to integrate them.

DOI

10.1109/FIE.1999.840378

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