Introduction to Quantitative Decision Making

The course will provide an introduction to modeling, analyzing and solving business decision problems under certainty and uncertainty. By developing good modeling skills, students will be able to solve and develop managerial insight, in a variety of common and not so common problems in today’s business environment. The course also develops concepts of uncertainty, probability and simulation which are the foundation of many business problems. Microsoft Excel will be used to model and solve many of these problems.

Course Content

1. Introduction to modelling - steps of modelling process
2. Introduction to decision making under certainty
3. Optimization, sensitivity analysis
4. Introduction to linear models and linear programming, product mix model, advertising model, production process model, work schedule model
5. Multi-period production model, transportation model, assignment model
6. Introduction to probability, Bayes’ Theorem
7. Probability distribution and decision analysis, payoff table and decision tree
8. Planning a decision strategy.

Methods of Instruction

Lectures and seminars.

Means of Assessment

To pass the course it is necessary to achieve at least 50% on the final exam and achieve at least 50% on the combined average of all tests and examinations.

Learning Outcomes

At the end of the course, the successful student should be able to:

1. Understand solving problems and decision making using quantitative analysis.
2. Understand the process of modelling (Cost, Revenue and Profit Models).
3. Understand decision making under certainty (Optimization, Sensitivity Analysis).
4. Understand computer-based systems to help with quantitative analysis and design models for business use.
5. Understand developing Excel-based models for problem solving.
6. Understand linear models and linear programming (product mix model, advertising model, production process model, work schedule model) using optimization functions such as Excel Solver, LINDO Systems.
7. Understand multi-period production model, transportation model, assignment model.
8. Understand probability, and Bayes’ Theorem.
9. Understand probability distribution and decision analysis.
10. Understand structuring the decision problem with payoff table and decision tree.
11. Understand making decisions under certainty and uncertainty.
12. Understand decision strategy.
13. Understand carrying out sensitivity analysis.

MATH 1120 or MATH 1125

Below shows how this course and its credits transfer within the BC transfer system. 

A course is considered university-transferable (UT) if it transfers to at least one of the five research universities in British Columbia: University of British Columbia; University of British Columbia-Okanagan; Simon Fraser University; University of Victoria; and the University of Northern British Columbia.

For more information on transfer visit the BC Transfer Guide and BCCAT websites.

If your course prerequisites indicate that you need an assessment, please see our Assessment page for more information.