# BUSN 6251: Decision Analysis and Modelling

Students learn to integrate personal judgment and intuition in realistic business situations with the most widely applicable methodologies of decision and risk analysis, probability and statistics, competitive analysis, and management science. Topics include an introduction to decision analysis and modelling; spreadsheet engineering and error reduction; framing decision analysis problems; framework for analyzing risk; data analysis; resource allocation with optimization models; multi-period deterministic models; multi-factor deterministic models; regression modelling; strategic interactive decisions; and interpreting models, data, and decisions.

## Learning outcomes

• Describe the range of cognitive, psychological, and social pitfalls which decision makers should avoid.
• Critically evaluate decisions of others and develop ways they could have improved their decision making.
• Demonstrate translating descriptions of decision problems into formal models, and investigate those models in an organized and systematic fashion.
• Illustrate best practice modelling techniques such as the FAST modelling standard, strategies for reducing errors, and other methods to ensure consistent and easy to understand models.
• Demonstrate how analytical techniques and statistical models can help enhance decision making by converting data to information and insights for decision making.
• Categorize and construct multistage decision analysis problems using decision trees.
• Categorize and construct multifactor problems with multiple objectives and uncertainty.
• Demonstrate linear, non-linear, and goal programming models for resource allocation and optimization.
• Critically evaluate various short-term forecasting and regression models and identify their appropriate use and limitations.
• Effectively use online data sources and Internet resources to access necessary information for model development.
• Interpret model results in the context of the business situation and address the inherent assumptions through sensitivity and scenario analysis.
• Effectively communicate the analysis, outcomes, assumptions, and implications through spreadsheet models and presentations.

## Course topics

• Module 1: Introduction to Decision-Making
• Module 2: Spreadsheet Engineering and Error Reduction
• Module 3: Data Analysis - Descriptive Statistic
• Module 4: Forecasting - Predicting the Future
• Module 5: Modelling - Framing Decision Analysis Problems
• Module 6: Simulation - A Framework for Analyzing Risk
• Module 7: Resource Allocation - Optimization Models
• Module 8: Decision Trees - Multi-Period Decisions
• Module 9: AHP and DEA - Group Decision-Making Tools
• Module 10: Data ELT
• Module 11: Dashboards and Visualization
• Module 12: Data and Decisions – A Management Consultant Perspective
• Module 13: Practice Exam

## Required text and materials

Online students are responsible for sourcing and ordering their own textbooks. Please see the list of required textbooks here: https://www.tru.ca/distance/courses/MBA_Textbook_List.pdf.

Please note that publishers may offer several package options that include additional resource material not required in your course. You may purchase a package of your choice as long as it includes the correct author, title and edition listed for your course.

• A computer with Internet access
• Excel 2019 (If you have a Windows- or Mac-based machine, then Excel 2016 is fine.)
Note: If you have a Mac you MUST have Excel 2016 or later and be prepared to do a lot of research into Apple’s version of functions and short-cut keys. We will be using a series of free Excel add-ins in this course (Decision Trees, Tornado Charts, etc.), and be aware that some of these add-ins may not work on a Mac.