ECE 313 Late-Breaking News

- You are allowed to bring ONE 8.5" by 11" sheet 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 Pascal or negative binomial random variable with parameters (r,p)

- a Poisson random variable with parameter (lambda)

Material covered on the examination is all that was covered in class through Monday February 24 and part of the lecture of February 26 (completion of the Geiger counter example). In terms of the text, we have covered all of Chapters 1 and 2 (except Section 2.4), and Section 3.6 on conditional pmfs, means, and variances, though you might have noticed that there is more stuff in the lectures than in the textbook. If you are following along in the Powerpoint slides on the class web page, we have covered Lectures 00-08 and 11-14 thus far. Lectures 9-10 and 15-17 will be covered later and are not included on the First Hour Examination (except for the mean and variance calculation for a binomial random variable that you will find at the very beginning of Lecture 9).

### Final Examination: The Final Exam will be scheduled by the Campus as a Combined-Section Final. Do NOT use the time shown in the Spring 2003 Timetable for classes meeting on 10 MWF or 11 MWF.

- 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 Pascal or negative binomial random variable with parameters (r,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
(sigma)
^{2} - a bivariate random variable (X,Y) uniformly distributed on a region of the plane

- jointly Gaussian random variables with means (mu)
_{x}and (mu)_{y}respectively, variances (sigma_{x})^{2}and (sigma_{y})^{2}respectively, and correlation coefficient (rho)

A table of values of the unit Gaussian CDF will be supplied to you if it is needed on the exam.

- a Bernoulli random variable with parameter p

- a Bernoulli random variable with parameter p