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Unit of study_

OLET5608: Linear Modelling

Linear models form the bedrock of many real-world data analyses. They are versatile, interpretable and easily implemented. This unit provides an overview of two of the most common methods of statistical analysis of data: analysis of variance and regression. You will generate data visualisation and diagnostics plots to interpret and discover the limitations of linear models and identify when more complex approaches may be needed. You will learn to code your analyses and perform reproducible research using the open source statistical package R. A key component of this unit involves generating visualisations, estimating and selecting appropriate linear models using your data. By doing this unit you will learn how to generate, interpret, visualise, discover and critique linear models applied to your original research.


Academic unit Mathematics and Statistics Academic Operations
Unit code OLET5608
Unit name Linear Modelling
Session, year
Intensive May, 2021
Attendance mode Block mode
Location Camperdown/Darlington, Sydney
Credit points 2

Enrolment rules

DATA2002 or DATA2902 or ENVX2001
Assumed knowledge

Exploratory data analysis, sampling, simple linear regression, t-tests and confidence intervals. Ability to perform data analytics with coding, basic linear algebra. E.g. DATA1001 and OLET5606 (Data wrangling).

Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Garth Tarr,
Lecturer(s) Garth Tarr ,
Administrative staff
Type Description Weight Due Length
Tutorial quiz Quiz 1
Online quiz
15% Week 02 10 questions
Outcomes assessed: LO1 LO2 LO4
Tutorial quiz Quiz 2
Online quiz
15% Week 03 10 questions
Outcomes assessed: LO1 LO2 LO3 LO4
Presentation Presentation*
Oral presentation
30% Week 05 15 minutes
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Final report
Final report
40% Week 06 10 pages
Outcomes assessed: LO1 LO2 LO3 LO4
  • Quizzes: completed online through Canvas.
  • Presentation: delivered in class in week 5, feedback will be provided which should then be incorporated into your final report.
  • Final report: written submission due in week 6.

Detailed information for each assessment can be found on Canvas.

*Students impacted by the travel ban will present via Zoom or as a video submission. For further details see 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

Representing complete or close to complete mastery of the material.


75 - 84

Representing excellence, but substantially less than complete mastery.


65 - 74

Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.


50 - 64

Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.


0 - 49

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

For more information see

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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

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

WK Topic Learning activity Learning outcomes
- When to use a linear model Online class (1 hr) LO1
Estimating a linear model Online class (1 hr) LO1 LO2 LO4
Assumption checking and diagnostics Online class (1 hr) LO1 LO2 LO4
Categorical predictors Online class (1 hr) LO1 LO2 LO3 LO4
Interaction terms Online class (1 hr) LO1 LO2 LO3 LO4
Inference in linear models Online class (1 hr) LO1 LO2 LO3 LO4
Model selection Online class (1 hr) LO1 LO2 LO3 LO4
Transformations Online class (1 hr) LO1 LO2 LO3 LO4
Using linear models for prediction Online class (1 hr) LO1 LO2 LO3 LO4
Week 04 Drop in help lab Computer laboratory (2 hr) LO1 LO2 LO3 LO4
Week 05 Students deliver their presentations to the class Presentation (2 hr) LO1 LO2 LO3 LO4

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 2 credit point unit, this equates to roughly 40-50 hours of student effort in total.

Required readings

All readings for this unit can be accessed through the Reading List link on Canvas.

  • Faraway, Julian James. Linear Models with R . Second edition. Boca Raton: CRC Press, Taylor & Francis Group, 2015.

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. formulate domain/context specific questions and identify appropriate statistical analysis
  • LO2. generate, interpret and compare numerical and graphical summaries of different data types
  • LO3. identify, justify, implement and evaluate appropriate parametric or non-parametric statistical tests for more than two samples
  • LO4. generate, critique and interpret appropriate linear models to predict outcomes and describe relationships between multiple variables.

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 is the first time this unit has been offered.

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.


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