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Unit of study_

QBUS5002: Quantitative Methods for Accounting

Semester 2, 2022 [Normal day] - Remote

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
Prohibitions
? 
ECMT5001 or QBUS5001
Prerequisites
? 
None
Corequisites
? 
None
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

Yes

Teaching staff

Coordinator Tonghui Wang, tonghui.wang@sydney.edu.au
Tutor(s) Yuning Zhang, yuning.zhang@sydney.edu.au
Yves Tam, yves.tam@sydney.edu.au
Type Description Weight Due Length
Final exam (Record+) Type B final exam hurdle task Final exam
Written exam with practical elements
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4
Small continuous assessment Bi-weekly quizzes
See Canvas announcements.
30% Ongoing 5x50 minutes
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
In-semester test (Record+) Type B in-semester exam Mid-semester exam
Written exam with practical elements
20% Week 07
Due date: 15 Sep 2022 at 14:00
80 minutes
Outcomes assessed: LO1 LO2 LO3 LO4
hurdle task = hurdle task ?
Type B final exam = Type B final exam ?
Type B in-semester exam = Type B in-semester exam ?

Assessment summary

  • There are 5 quizzes in this assessment task. Each quiz is worth 6% of the final mark and must be completed within 50 minutes on Canvas. These quizzes will be held from 12:00pm to 12:50pm (Sydney time) on Saturday in Weeks 3, 5, 10, 12 and 13. Changes, if any, will be announced on Canvas. An Excel data file will be provided.     

  • The Mid-semester Exam is worth 20% of the final mark and must be completed within 80 minutes, including 5 minutes reading time. This exam will be held in Week 7 during the lecture time. Formulae sheets and an Excel data file will be provided. 

  • The Final Exam is worth 50% of the final mark and must be completed within 130 minutes, including 10 minutes reading time. This exam will be held during the final exam period. Formulae sheets and an Excel data file will be provided. The Final Exam is listed as Hurdle Task which means that you must undertake the assessment and achieve a mark above a minimum standard. Students who fail to achieve this minimum standard in this assessment, even when their aggregate mark for the entire unit is above 50%, will be given a Fail grade for the unit. As a result, the student's academic transcript will show a Fail grade and the actual mark achieved if the final mark is between 0-49 and a Fail grade and a capped moderated mark of 49 for all other marks. The hurdle mark for this assessment is 40. 

  • Students will need to use Microsoft Excel for data analysis in quizzes, mid-semester exam and final exam. 
  • Past exam papers and detailed information for each assessment task can be found on Canvas.
  • All quizzes and exams are individual assessments. But students are strongly advised to learn together in small study groups in order to enhance learning outcomes. 
  • Students who are unable to sit a quiz, the mid-semester exam or the final exam may apply for special consideration. If approved, mark adjustment will be applied to quizzes and replacement exam will be offered for the mid-semester/final exam. 

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

Description

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. 

Distinction

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.

Credit

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.

Pass

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. 

Fail

0 - 49

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

For more information see sydney.edu.au/students/guide-to-grades.

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:

Quizzes: Students must complete the quiz before the due date. Answers submitted after 12:50pm will not be marked. Students who encounter technical issues during a quiz may apply for special consideration.

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) LO1 LO2 LO3 LO4
Week 02 Introduction to Statistics Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Probability and Random Variables Lecture (3 hr) LO1 LO2 LO3 LO4
Week 03 Probability and Random Variables Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Probability Distributions Lecture (3 hr) LO1 LO2 LO3 LO4
Week 04 Probability Distributions Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Sampling Distributions Lecture (3 hr) LO1 LO2 LO3 LO4
Week 05 Sampling Distributions Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Statistical Inference I Lecture (3 hr) LO1 LO2 LO3 LO4
Week 06 Statistical Inference I Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Revision and Q&A for Mid-semester Exam Lecture (3 hr) LO1 LO2 LO3 LO4 LO5
Week 07 Past Exam Papers Practice Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 08 Statistical Inference II Lecture (3 hr) LO1 LO2 LO3 LO4
Week 09 Statistical Inference II Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Statistical Inference III Lecture (3 hr) LO1 LO2 LO3 LO4
Week 10 Statistical Inference III Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Statistical Inference IV + Linear Regression Analysis I (Correlation Analysis) Lecture (3 hr) LO1 LO2 LO3 LO4
Week 11 Statistical Inference IV + Linear Regression Analysis I (Correlation Analysis) Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Linear Regression Analysis I Lecture (3 hr) LO1 LO2 LO3 LO4
Week 12 Linear Regression Analysis I Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Linear Regression Analysis II Lecture (3 hr) LO1 LO2 LO3 LO4
Week 13 Linear Regression Analysis II Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Linear Regression Analysis III Lecture (3 hr) LO1 LO2 LO3 LO4
Week 14 (STUVAC) Linear Regression Analysis III (Make-up Tutorial Class) + Final Exam Revision Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

Date changes for lectures, tutorial classes and assessment tasks.
  • Required software: MS Excel with Data Analysis ToolPak and QBUS5002 Analysis ToolPak.

More information can be found on Canvas.

Disclaimer

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

To help you understand common terms that we use at the University, we offer an online glossary.