Outline

Overview

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	Boris Choy, boris.choy@sydney.edu.au
Tutor(s)	Yves Tam, yves.tam@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final exam Written exam with practical elements	50%	Formal exam period	2 hours
Outcomes assessed:
Small continuous assessment	Bi-weekly quizzes See Canvas announcements.	30%	Ongoing	5x50 minutes
Outcomes assessed:
In-semester test (Record+)	Mid-semester exam n/a	20%	Week 07 Due date: 07 Apr 2022 at 14:00	80 minutes
Outcomes assessed:
= hurdle task ? = Type B final 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, 9, 11 and 13. Students will need to use Microsoft Excel for data analysis.
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. A formulae sheet and an Excel data file will be provided with the exam. Students will need to use Microsoft Excel for data analysis.
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. A formulae sheet and an Excel data file will be provided with the exam. Students will need to use Microsoft Excel for data analysis. 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.
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.

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:

The late penalty is 5% of the assessment mark per day, or part of a day, after the due date. The closing date is the last day on which the assessment will be accepted for marking.

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.

Learning support

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to Statistics	Lecture (3 hr)
Week 02	Introduction to Statistics	Tutorial (2 hr)
Week 02	Probability	Lecture (3 hr)
Week 03	Probability	Tutorial (2 hr)
Week 03	Random Variables	Lecture (3 hr)
Week 04	Random Variables	Tutorial (2 hr)
Week 04	Probability Distributions	Lecture (3 hr)
Week 05	Probability Distributions	Tutorial (2 hr)
Week 05	Sampling Distributions	Lecture (3 hr)
Week 06	Sampling Distributions	Tutorial (2 hr)
Week 06	Statistical Inference I	Lecture (3 hr)
Week 07	Statistical Inference I and Mid-semester Exam Revision	Tutorial (2 hr)
Week 08	Statistical Inference II	Lecture (3 hr)
Week 09	Statistical Inference II	Tutorial (2 hr)
Week 09	Statistical Inference III	Lecture (3 hr)
Week 10	Statistical Inference III	Tutorial (2 hr)
Week 10	Statistical Inference IV + Linear Regression Analysis I (Correlation Analysis)	Lecture (3 hr)
Week 11	Statistical Inference IV + Linear Regression Analysis I (Correlation Analysis)	Tutorial (2 hr)
Week 11	Linear Regression Analysis I	Lecture (3 hr)
Week 12	Linear Regression Analysis I	Tutorial (2 hr)
Week 12	Linear Regression Analysis II	Lecture (3 hr)
Week 13	Linear Regression Analysis II	Tutorial (2 hr)
Week 13	Linear Regression Analysis III	Lecture (3 hr)
Week 14 (STUVAC)	Linear Regression Analysis III (Make-up Tutorial Class) + Final Exam Revision	Tutorial (2 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

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.

Outcomes
Graduate qualities

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
Learning outcomes

Responding to student feedback

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

Date changes for lectures, tutorial classes and assessment tasks.

Additional information

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

QBUS5002: Quantitative Methods for Accounting

Semester 1, 2022 [Normal day] - Remote

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect