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Unit of study_

QBUS5002: Quantitative Methods for Accounting

Semester 1, 2020 [Normal day] - Camperdown/Darlington, Sydney

Accountants in the workplace need to be familiar with effective techniques for dealing with business data. This unit uses spreadsheet tools and accounting examples to introduce the most important data analysis methods. The unit helps students understand variability and detect when a variation is random and when something significant is going on. This unit also enables students to uncover the relationships between variables that can be hidden in business data. Students learn how to look at accounting data and use it to forecast business performance. Students are also given examples of the misuse of statistics in an accounting context. The unit is taught through data-driven examples, exercises and business case studies.

Unit details and rules

Unit code QBUS5002
Academic unit Business Analytics
Credit points 6
ECMT5001 or QBUS5001
Assumed knowledge

Students should be capable of reading data in tabulated form, working with Microsoft EXCEL, and doing High School level of mathematics

Available to study abroad and exchange students


Teaching staff

Coordinator Jessica Leung,
Type Description Weight Due Length
Assignment Assignment
Written report
20% - 20 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Final exam hurdle task Final exam
Written exam with practical elements
50% Formal exam period 2.5 hours
Outcomes assessed: LO1 LO5 LO4 LO3 LO2
Small continuous assessment Weekly homework
Practical assessment
10% Multiple weeks n/a
Outcomes assessed: LO1 LO5 LO4 LO3 LO2
In-semester test Mid-semester exam
Written exam with practical elements
20% Week 06 1.5 hours
Outcomes assessed: LO1 LO5 LO4 LO3 LO2
hurdle task = hurdle task ?

Assessment summary

  • Weekly homework: There are up to 12 small weekly homework exercises in this assessment task. 
  • Assignment: This assessment consists of two parts. Parts 1 (due week 5) and part 2 (due week 12).
  • Mid-semester exam: This exam will require students to be able to apply theory to business situations, exercise critical judgement, use a problem-solving approach, and demonstrate a thorough understanding of the concepts. The exam involves written-answer questions requiring data analysis, both calculation and interpretation. Students will need to use a computer for performing data analysis. All material covered in weeks 1 to 5 will be examinable. 
  • Final exam: Students will be required to use a computer in the exam. A formulae sheet will be supplied with the exam. The final exam is listed as a HURDLE TASK which means you must complete the assessment in order to pass the unit. Students who fail to complete this assessment, even when their aggregate mark for the entire unit of study is above 50%, will be given a Fail grade for this unit. As a result, a student's academic transcript will show a Fail grade and the actual mark achieved if the final mark of the unit is between 0-49 and a Fail grade and a capped moderated mark of 49 for all other final marks.

Replacement exams: The Business School has determined that second replacement exams (both mid-semester and final) will not be offered for this unit of study should a student submit two special considerations for the same exam as this unit forms part of the professional accounting accreditation pathway. Should a student be granted a replacement exam via special consideration, and is then unable to attend or does not complete the replacement exam, any subsequent special consideration will result in a DC grade (Discontinue Without Fail).

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range


High distinction

85 - 100

Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school. 


75 - 84

Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.


65 - 74

Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.


50 - 64

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school. 


0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

The late penalty is 5% of the assignment mark per day, or part of a day, after the due date.

Academic integrity

The Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Introduction to statistics Lecture (3 hr)  
Week 02 Introduction to statistics Tutorial (2 hr)  
Probability and random variables Lecture (3 hr)  
Week 03 Probability and random variables Tutorial (2 hr)  
Discrete and continuous probability distributions Lecture (3 hr)  
Week 04 Discrete and continuous probability distributions Tutorial (2 hr)  
Statistical inference 1 Lecture (3 hr)  
Week 05 Statistical inference 1 Tutorial (2 hr)  
Statistical inference 2: estimation; Statistical inference 3 - hypothesis testing 1 Lecture (3 hr)  
Week 07 Statistical inference 2: estimation; Statistical inference 3 - hypothesis testing 1 Tutorial (2 hr)  
Statistical inference 4 - hypothesis testing 2; Lecture (3 hr)  
Week 08 Statistical inference 4 - hypothesis testing 2 Tutorial (2 hr)  
1. Statistical inference 4 - hypothesis testing 2; 2. Analysis of variance (ANOVA) Lecture (3 hr)  
Week 09 1. Statistical inference 4 - hypothesis testing 2; 2. Analysis of variance (ANOVA) Tutorial (2 hr)  
Linear regression analysis 1 Lecture (3 hr)  
Week 10 Linear regression analysis 1 Tutorial (2 hr)  
Linear regression analysis 2 Lecture (3 hr)  
Week 11 Linear regression analysis 2 Tutorial (2 hr)  
Linear regression analysis 3 Lecture (3 hr)  
Week 12 Linear regression analysis 3 Tutorial (2 hr)  
Time series analysis and forecasting Lecture (3 hr)  
Week 13 Time series analysis and forecasting Tutorial (2 hr)  
Revision for final exam Lecture (3 hr)  

Attendance and class requirements

Lecture recordings: All lectures are recorded and will be available on Canvas for student use. Please note the Business School does not own the system and cannot guarantee that the system will operate or that every class will be recorded. Students should ensure they attend and participate in all classes.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

  • Selvanathan, E. A., Selvanathan, S. and Keller, G. (2017) Business Statistics (Australia and New Zealand), 7th Edition.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. demonstrate an integrated understanding of key concepts and techniques in applying statistical analysis in real business and accounting applications
  • LO2. question, assess and respond independently and creatively to assumptions, propositions and debates related to the application of statistical analysis
  • LO3. analyse quantitative business and accounting data and solve related problems, and use evidence and findings to formulate strategically appropriate solutions
  • LO4. use a range of communications strategies individually and to reach an agreement with others about appropriate responses to complex problems within the field of quantitative analysis for accounting
  • LO5. work collaboratively within the context of the group project to address complex problems within the field of quantitative analysis for accounting.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered.
  • Required software: MS Excel with Data Analysis Toolpak and Data Analysis Plus add-in.
  • Aplia/MindTap - The key for using Apila/MindTap will be announced before the end of week 1.

More information can be found on Canvas.


The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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