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Unit outline_

BUSS7902: Quantitative Business Research Methods

Semester 1, 2022 [Normal day] - Camperdown/Darlington, Sydney

This unit introduces Business School HDR students to quantitative techniques for research. It provides students with a review or introduction to the types of quantitative analyses that they may be required to know, discuss or conduct, both during their PhD and in their future working lives. It aims to provide a basic training with a focus on statistical and business analysis methods.

Unit details and rules

Academic unit Business School
Credit points 6
Assumed knowledge

Basic knowledge of statistical concepts

Available to study abroad and exchange students


Teaching staff

Coordinator Boris Choy,
Lecturer(s) Boris Choy,
Type Description Weight Due Length
Assignment Assignment 1
Data Skills Data analysis and problem solving.
Due date: 23 Apr 2022 at 17:00

Closing date: 23 Apr 2022
10 pages
Outcomes assessed: LO1 LO2 LO3
Assignment Assignment 2
Data analysis and problem solving
Due date: 04 Jun 2022 at 17:00

Closing date: 04 Jun 2022
15 pages
Outcomes assessed: LO1 LO2 LO3
Small test Test
Computer-based exam
10% Week 07
Due date: 04 Apr 2022 at 14:00

Closing date: 04 Apr 2022
1.5 hours
Outcomes assessed: LO1 LO3 LO2

Assessment summary

  • Test: This assessment task will test your understanding of the basic statistical concepts that are assumed knowledge for this course and left for self-study.
  • Assignment 1: This is a practical assignment that requires sufficient mastery of the statistical software to apply the concepts covered in class to a provided dataset. The individual assignment has to be submitted electronically in PDF format via Turnitin.
  • Assignment 2: This final assessment requires students to apply the linear regression theory to familiar situations, exercise critical judgment, use a problem-solving approach, and demonstrate their understanding of the concepts. The individual assignment has to be submitted electronically in PDF format via Turnitin.

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 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 Assessment Procedures 2011 provide that any written work submitted after the due date will be penalised by 5% of the maximum awardable mark for each calendar day after the due date. If the assessment is submitted more than ten calendar days late, a mark of zero will be awarded.

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 and probability Lecture (3 hr)  
Week 02 Statistics, data collection, sampling, probability and random variable Individual study (3 hr)  
Week 03 Discrete probability distributions with applications Individual study (3 hr)  
Week 04 Continuous probability distributions with applications Individual study (3 hr)  
Week 05 Statistical inference 1: Introduction Individual study (3 hr)  
Week 06 Statistical inference 2: parameter estimation Individual study (3 hr)  
Week 07 Statistical inference 3: hypothesis testing Individual study (3 hr)  
Week 08 Statistical inference 4: one-way analysis of variance (ANOVA) Lecture and tutorial (3 hr)  
Week 09 Introduction to Statistical Learning. Relationships between variables. Correlation. Lecture and tutorial (3 hr)  
Week 10 Simple linear regression analysis: Estimation and Prediction Lecture and tutorial (3 hr)  
Week 11 Multiple linear regression analysis: Diagnostics, Tests and Confidence Intervals Lecture and tutorial (3 hr)  
Week 12 Further topics and issues in multiple linear regression analysis: Nonlinear Regression Functions, Synergy, Dummy variables, Heteroskedasticity, Multicolinearity. Lecture and tutorial (3 hr)  
Week 13 Introduction to Classification: Logistic regression Lecture and tutorial (3 hr)  

Attendance and class requirements

Lecture recordings: All lectures and seminars 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

Useful references:

  • Selvanathan E.A., Selvanathan, S and Keller, G. (2017) Business Statistics, Australia and New Zealand 7th Edition. Cengage Learning, Australia. ISBN: 978-017 0369466


  • James Stock and Mark Watson (2019), Introduction to Econometrics, 4th Edition, Pearson.


  • Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. An Introduction to Statistical Learning : with Applications in R. New York :Springer, 2013.

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. apply research principles and methods for gathering and analysing data/information relevant to your major field of study
  • LO2. use the descriptive statistics tools to create and analyse results using your data for the research project, and apply the learnt statistical concepts to the research project
  • LO3. evaluate and interpret the results of using quantitative methods in your project, and communicate statistical knowledge and outcomes relevant to the research project.

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


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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