Department of Electrical and Computer Engineering
ECE 313 Late-Breaking News
The Final Examination is scheduled for
Friday December 14, 2001 from 8:00 a.m. to 11:00 a.m.
in Room 260 Everitt Laboratory.
- You are allowed to bring TWO 8.5" by 11" sheets of notes to the exam;
both sides of the sheets can be used.
Calculators, laptop computers, Palm Pilots and the like are not permitted.
- You are expected to know what is meant by
- a Bernoulli random variable with parameter p
- a binomial random variable with parameters (n,p)
- a geometric random variable with parameter p
- a negative binomial random variable with parameters (n,p)
- a Poisson random variable with parameter (lambda)
- a random variable uniformly distributed on (a,b)
- an exponential random variable with parameter (lambda)
- a gamma random variable with parameters (t, lambda)
- a Gaussian random variable with mean (mu) and variance
- a bivariate random variable (X,Y) uniformly distributed on a
region of the plane
If you have forgotten the formulas for the pmf/pdf/CDF
or the mean and variance of these (or do not have them written down on your
sheets of notes,) you will not
be given these pieces of information during the exam.
- jointly Gaussian random variables with means (mu)x
and (mu)y respectively, variances (sigmax)2
and (sigmay)2 respectively, and correlation
A table of values of the unit Gaussian CDF will be supplied to you if
it is needed on the exam.
You may use the expressions for the means and variances of these random
variables in computing numerical values on the exam. For example, if
I describe a situation in which a certain measurement can be modeled
as a binomial random variable, and ask for E[X], you need not explicitly
show all the details of how you computed the sum of
I will accept an answer of the form:
will not accept the bald and unadorned statement
- Since X is a binomial random variable with
parameters (100, 0.3), E[X] = 100*0.3 = 30.
with no explanation whatsoever. In short, I am looking for a
statement that shows that you have recognized from the word description
that X is a binomial random variable, and what its parameters are.
The Final Examination is comprehensive in that much of the subject matter
covered in the entire course is
included on the exam. However, there is considerably more emphasis on
the material not covered on the two Hour Exams, and the material covered
after the Second Hour Exam. It is recommended that
you pay particular attention to topics such as
I have not yet decided whether to include multiple-choice or TRUE/FALSE
questions on the Final Exam.
- univariate CDFs, pdfs and pmfs
- computation of the probability that X lies in an interval
- mean, variance, and LOTUS
- computation of the pdf/pmf of a function of one random variable
- the Poisson random process
- conditional pmfs and pdfs
- joint pdfs and pmfs
- computation of marginal pdfs and pmfs
- computation of the probability that (X,Y) lies in a region of
- computation of the pdf/pmf of ONE function of two random variables
- covariance, correlation coefficient, variance of a sum of random
variables, covariance of aX+bY and cX+dY, etc.
- jointly Gaussian random variables
- minimum-mean-square-error and linear minimum-mean-square-error
estimation of one random variable in terms of another
As to what is not included on
the Final Exam, there will be no questions on
- Decision theory, maximum-likelihood decision rules, Bayesian decision
- Characteristic functions
- joint pdf of two or more functions of two or more random variables
except that the joint distribution
of the maximum and minimum of two variables, and of linear transformations
of jointly Gaussian random variables are part of the material on
which I might ask a question