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3 Credits | 300 Level | 38 Contact hours


Probability and Stochastic Processes, by R. D. Yates and D. J. Goodman, third edition, 2014, ISBN 978-1-118- 32456-1.
Note: 1st and 2nd editions of the text cannot be used due to major changes made to this 3rd edition.


A calculus-based introduction to probability theory and its applications in engineering and applied science. Topics covered include counting techniques, conditional probability, independence, discrete and continuous random variables, expected value and variance, joint distributions, covariance, correlation, central limit theorem, and an introduction to statistical inference.


1. Experiments, Models, and Probabilities.
2. Sequential Experiments.
3. Discrete Random Variables.
4. Continuous Random Variables.
5. Multiple Random Variables.
6. Probability Models of Derived Random Variables.
7. Conditional Probability Models.
8. Random Vectors.
9. Sums of Random Variables.
10. The Sample Mean.
11. Hypothesis Testing.
12. Estimation of a Random Variable.


To provide the student with:
• A working knowledge of concepts and tools of probability theory.
• Experience with a variety of typical applications of probability.
• A foundation for further studies in probability and statistics.
• An enhancement of overall mathematical skills.


1. Attendance and in-class participation: 10%
2. Assignments: 10%
3. Assignments on lectures: 20%
4. Partial Exam: 30 %
5. Final Exam: 30 %

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