**ELEC 471: ABET Course Objectives and Outcomes **

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

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

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

(a, n)
- present solutions of probability problems to classmates.

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

(k, m, n)
- develop least-squares estimates for parameters that appear in
linear models.

(a, e, n)