The final exam is on Friday, May 9, 4:00pm - 7:00pm, in the regular classroom, SEC 217.
A review session for the final exam will be held on May 7, 3:00pm - 4:30pm, in ARC 205.
The final exam is cumulative, so make sure you review earlier sections. The following is a chapter by chapter guide intended to help you organize the material we have covered in class after the second midterm as you study for your exam. It is only intended to serve as a guideline, and may not explicitly mention everything that you need to study. It is also important you are comfortable with material from previous pre-requisite classes.
Please review all homework and quiz problems for the chapters given below, as well as examples worked out in class and in the textbook. This includes material covered in Exam 1 and Exam 2. You are also encouraged to work out the "self-test problems and exercises" at the end of each section for more practice. In addition, Prof. Eugene Speer has an excellent collection of review problems on his website. (You can ignore problems 10 and 16 in this set of Prof. Speer's review problems.)
6.2: You should know what it means for two random variables to be independent, and how to determine whether they are or not.
6.3: You should be able to find the distribution for a sum of independent variables. Pay careful attention to the particular distributions discussed in subsections 6.3.1-6.3.4.
6.4 & 6.5: Know how to get the conditional probability mass or density functions of X given Y=y and how to use them to obtain conditional probabilities.
7.2 & 4.9: Know how to find the expectation of sums of random variables in both the discrete and continuous cases. You should also know what the sample mean is. You do not need to know the material covered in subsections 7.2.1 and 7.2.2.
7.3: You should be able to find the expectation and variance of the number of events that occur.
7.4: You should know how to compute covariance, the variance of a sum of random variables and the sample variance. You should also know the properties of covariance discussed in this chapter. You will not be tested on correlation.
7.5: You should know how to find conditional expectation values, and how to use them to get other expectation values (and hence probabilities).
8.2: You should know the precise statement of Markov's and Chebyshev's inequalities and how to determine where it is appropriate to use them. You must also know the weak law of large numbers.
8.3: You should know the central limit theorem, as given in Theorem 3.1. You do not need to know the proof of this theorem, but you do need to know how to use this result to approximate other distributions using a normal distribution.
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