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Quantitative Methods in Commerce G (11405.2)

Level: Graduate Level
Credit Points: 3
HECS Bands: 2, 4
Faculty: Faculty of Business, Government & Law
Discipline: Canberra Business School


Possible changes to your unit's learning activities and assessment items

For the remainder of 2020, resulting from Australian Government's directives requiring physical distancing and restrictions on movement because of the COVID-19 pandemic, any exams that are required for assessment in a unit will be online exams. Online exams may also use online proctoring to help assure the academic integrity of those exams. Please contact your unit convener with any questions.

While the University has made efforts to ensure that Unit Outlines reflect a unit’s learning activities and assessment items, any changes to Australian Government directives because of the COVID-19 pandemic may require changes to these during the semester to ensure the safety and well being of students and staff. These changes will not be updated in the published unit outline, but will be communicated to you via your unit’s UCLearn(Canvas) teaching site. Any changes made will continue to meet the unit’s learning outcomes, as described in the Unit Outline.

Unit Outlines

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  • Semester 2, 2020, ON-CAMPUS, BRUCE (197905) - View
  • Semester 1, 2020, ON-CAMPUS, BRUCE (197904) - View
  • Semester 2, 2019, ON-CAMPUS, BRUCE (190652) - View
  • Semester 1, 2019, ON-CAMPUS, BRUCE (190336) - View

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This unit highlights the importance of mathematical and statistical methods and tools for today's managers and analysts, and demonstrates how to apply these methods to business problems using real-world data. The quantitative skills that students learn in this unit are useful in all areas of business. Students learn how to model and analyse relationships within business data; how to identify appropriate statistical techniques; how to compute statistics by calculator and Microsoft Excel; how to interpret results in the context of the business problem; and how to forecast using business data. The unit is taught through data-driven examples, exercises and business case studies. The unit content includes working with equations and graphs of straight lines, linear programming, quantitative research principles in collecting, summarising and displaying business data, index numbers, relations in categorical data, measures of association, fitting straight lines, elementary probability concepts, the normal distribution and its business applications.

Learning Outcomes

Upon successful completion of this unit, students will be able to:

1. Demonstrate an ability to solve a range of business problems by firstly synthesising, organising data, then analysing plus interpreting data and information in relation to specific topics, such as discounted cash flows and linear programming problems with at least 2 dimensions;

2. Clearly communicate to stakeholders the implications of the results of various techniques applied, as well as being able to identify and articulate the potential impacts of assumptions made and limitations of the techniques;

3. Explain how data is sampled, collected and presented using a range of summary measures;

4. Identify problems within real-world constraints and collect data for business decision making;

5. Create statistical models for studying relationship among business variables;

6. Demonstrate an ability to select appropriate techniques when dealing with unfamiliar problems in business, finance and economics, as well as structure a given problem scenario in a way that allows solution via appropriate techniques; and

7. Demonstrate the application of forecasting method. Students will also be able to articulate the impacts of the assumptions behind, and limitations of, these models.

Assessment Items

Contact Hours

Two 2 hour lectures and one 2 hour tutorial on campus per week.





Assumed Knowledge

Basic mathematics approximately to Year 10 standard.

Incompatible Units

11165 Quantitative Methods in Commerce, 5123 Business Statistics

Equivalent Units

6275 Statistical Analysis & Decision Making G

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