OMS 501 Placement Examination
OMS 501 is a 3 credit hour (14 week) course on business decision making. It has three units: Constrained Optimization, Decision Making under Uncertainty, and Statistical Analysis. The only method to waive the course is the placement exam, which is offered once a year at the end of August or beginning of September.
Constrained Optimization (2 weeks)
Deterministic decision making relies on constrained optimization of an objective function. The canonical business example is maximizing profits given resources constraints. OMS 501 introduces the basic concepts of constrained optimization and uses EXCEL’s Solver to obtain optimal solutions. The course does not discuss optimization procedures. The topics for constrained optimization are:
Decision Making Under Uncertainty (2 weeks)
Next, the course introduces uncertainty into the decision problem. Returning to our canonical example, future profits depend on future demand, and demand is uncertain. The topics are for decision making under uncertainty are:
Statistical Analysis (10 weeks)
Finally, OMS 501 uses data and statistics to reduce the uncertainty in business decision making. In the example, one could use past demand or marketing research data to reduce uncertainty about future demand. The topics from statistics are:
The statistical component of the course is heavily weighted towards regression analysis and its applications.
A student who has a solid background in calculus, statistics and probability should be able to pass the exam. A good text on basic statistics is Statistics for Business and Economics, by James McClave, George Benson, and Terry Sincich, though other introductory statistics texts would be reasonable substitutes. Optimization Modeling with Spreadsheets by Kenneth Baker is probably one of the best references for constrained optimization. Wikipedia has good articles on optimization and decision theory. See http://en.wikipedia.org/wiki/Linear_program and http://en.wikipedia.org/wiki/Decision_theory as a starting point. Texts on optimization give more detail on algorithms and procedures than required for the waive exam.
Placement Examination Format
Who Should Take the Exam?
Students who successfully waive the course usually have had an introductory course in statistics along with at least one intermediate course. (It is assumed that all students have had calculus since it is a prerequisite for the program.) These students most often have had significant work experience using statistics. Typically, students who have had an introductory statistics course several years ago and nothing since then perform marginally on the waiver exam. On very rare occasions, students with limited or no statistical experience pass the exam, but these, few students spend several weeks of intensive study before the exam. It is possible, but requires superlative dedication and indomitable spirit.