ELEC 471: ABET Course Objectives and Outcomes


ABET program outcomes that we must meet


Course objectives:

Students finishing this course will understand the basic concepts and tools of probability theory, and they will appreciate the relevance and usefulness of probability and statistics in practical engineering problems. Students will be able to apply probability to problems of statistical inference that are drawn from the field of electrical engineering, particularly detection and estimation in digital communication systems. Hands-on experience with the course material is provided through demonstrations and projects using MATLAB.


Course outcomes:

At the conclusion of the course, students will be able to

  1. explain the meaning and significance of the following terms: disjoint events, independent events, conditional probability, random variables (r.v.'s), expected value, mean and variance, probability mass function (pmf), probability density function (pdf), cumulative distribution function (cdf), covariance, correlation coefficient, estimation of one r.v. by observing a related r.v. with minimum mean-squared error, the Gaussian r.v., least-squares estimation of parameters.
    (a, n)

  2. use discrete random variables (Bernoulli, uniform, binomial, geometric, Pascal, and others) to compute probabilities and average values in a variety of applications.
    (a, n)

  3. apply conditional probability analysis to develop decision rules and estimates; then, evaluate the performance of the decision rule/estimate in terms of probability of error/mean-squared error, as in the digital communication system example.
    (a, c, e, n)

  4. analyze pairs of random variables in terms of their joint probabilities, covariance, and correlation coefficient.
    (a, n)

  5. compute probabilities associated with Gaussian random variables.
    (a, n)

  6. present solutions of probability problems to classmates.
    (g, n)

  7. use Matlab to simulate the performance of systems containing randomness that are described by probabilistic models.
    (k, m, n)

  8. develop least-squares estimates for parameters that appear in linear models.
    (a, e, n)