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:

- Model building: objective functions and constraints on decision variables,
- Feasible regions,
- Optimal solutions, and
- Sensitivity analysis: reduced cost, shadow prices, and allowable changes.

__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:

- Probability and expectation,
- Expected value of perfect information, and
- Decision trees for multistage decisions.

__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:

- Descriptive statistics and graphics,
- Binomial and normal distributions,
- Sampling distributions and the central limit theorem,
- Inference about a population mean and population proportion,
- One sample hypotheses tests for population means and proportions,
- Simple and multiple regression analysis,
- Model building with regression, and
- Forecasting with regression.

The statistical component of the course is heavily weighted towards regression analysis and its applications.

__Resources__

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__

- The only method to waive the course is through the placement exam.
- It is offered once a year at the end of August or beginning of September.
- The exam if multiple choice,
- The exam emphasizes concepts,
- The exam uses output from EXCEL’s Data Analysis and Solver,
- Students may use calculators, although “number crunching” is minimal,
- Laptops, PDA, cell phones, and internet enabled devices are not allowed in the exam,
- Students are not allowed to bring books or notes to the exam,
- Formulae sheets and statistical tables will be provided at the exam, and
- The exam is for placement purposes only. In the past, a passing grade was around 65% to 70%.

__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.