STAT 346 Statistical Methods in Operations Research (3)
Linear programming, simplex method, project management with PERT/CPM,
deterministic dynamic programming, probabilistic dynamic programming, integer
programming, queueing theory, introduction to SAS®. (Offered fall semester, even years.)
STAT 291 Statistics for Mathematical Sciences I
- Familiarity with matrix operations including inversion.
- Ability to perform Gauss-Jordan elimination.
- Familiarity with the beta, normal, exponential, and Poisson distributions,
the Central Limit Theorem.
- Ability to determine the mean and variance of a distribution and a linear
combination of independent random variables.
- Ability to use the computer for problem solving.
| Required Course Materials:
F. Hillier and G. Lieberman, Introduction to Operations Research, 9th
edition, McGraw-Hill, 2010 (ISBN 0-07-337629-9)
Prerequisite course material: J. Devore, Probability and Statistics for Engineering and the Sciences, 7th edition, Brooks/Cole, 2008 (ISBN 0-495-38217-5)
L. Marlin Eby, Ph.D., Professor of Mathematics and Statistics
- Students majoring in mathematics or computer science or minoring in
- Students wanting a more rigorous decision science course.
- This course can be used to meet the elective requirement for both
mathematics majors and computer science majors.
- To intuitively understand each concept.
- To understand, when possible and appropriate, the rigor of a mathematical
- To integrate topics by identifying commonalties.
- To understand the limitations of each analysis through consideration of
- To express general concepts in terms of the application.
- To communicate results, clearly and completely, in a manner appropriate to
- To use the computer for analysis only after understanding how to perform
the analysis manually.
- Introduction to SAS®: overview.
- Introduction to Linear Programming: model, assumptions, geometric and
- Simplex Method: standard and nonstandard problems, post-optimality
- Project Management with PERT/CPM: overview, networks, project scheduling,
and uncertain activity durations.
- Dynamic Programming: deterministic and probabilistic, tabular and
- Integer Programming: exact solution and approximation methods.
- Queueing Theory: single and multiple server, single and multiple customer
types, interarrival, waiting, and service time distributions.
Revised: August 2010