APPMOD1000E Applied Decision Modelling with Excel
- Course codeAPPMOD1000E
- Number of credits5
- Teaching semester2022 Autumn
- Language of instructionEnglish
- CampusRena
- Required prerequisite knowledge
Some knowledge of basic corporate finance, calculus /mathematics, and statistics is an advantage
This course introduces quantitative models that can be used to solve decision problems within various corporations. Spreadsheets (Microsoft Excel) will be actively used both in teaching and in mandatory assignments. The course is divided into four main parts. These are:
- Basic models of revenue, costs, and profit
- Linear- and Non-linear Programming Models
- Simulation Models
- Forecasting Models
The workload is approximately the same for all four main parts.
Learning Outcome
Upon passing the course, the student shall have achieved the following learning outcomes:
The student
- can explain the formulation of basic revenue-, costs- and profit functions
- can explain the concept of break-even analysis and describe how changes in price, variable- and fixed costs will affect the solution.
- can translate practical problems into quantitative models that are solvable with spreadsheets
- can discuss important research results within specific areas covered in the course and relate them to some of the analysis done.
- can explain and justify the choice of various models based on specific criteria
- can describe strengths and weaknesses of using quantitative models for decision support in practice
The student
- is able to implement and solve basic revenue-, costs-, profit-, and break-even models using Excel
- can derive optimal solutions for linear programming models by graphical solutions, calculations, and by using Excel
- can interpret the results and important concepts from solution reports from linear programming models
- can calculate optimal solution for simple non-linear programming models
- can replicate real business situations using Monte Carlo simulation
- can analyze uncertainty in various projects based on the results from Monte Carlo simulations
- can choose appropriate forecasting models for time-series data with various properties
- can make forecasts for time series variables in Excel using appropriate modelling techniques
- can calculate the accuracy of forecasts using several accuracy measures
- can evaluate the performance of various forecasting techniques based on the accuracy measures
The student
- can assess the needs various businesses might have for business modelling and forecasting techniques
- can assist in the development and use of appropriate models to improve the general performance of various businesses
Online teaching material in the form of text, pictures/illustrations, videos, problems with solutions, mandatory quizzes and assignments.
Four mandatory assignments that must be passed to be allowed to take the exam (one mandatory assignment per module. The mandatory assignment will consist of multiple choice questions directly related to the content of each module.
| Form of assessment | Grading scale | Grouping | Duration of assessment | Support materials | Proportion | Comment |
|---|---|---|---|---|---|---|
Home exam | Passed - not passed | 24 Hour(s) |
| 100% | All available resources, but exam should only be completed by the candidate (that is, no help from other people is allowed). |