Outline

Overview

Linear models are core to a wide range of real-world data analyses, for example in agriculture, health, sport and business. This unit provides an in-depth exploration of various linear models outlining when they can be applied, and how to assess if they are appropriate. The unit will introduce the fundamental concepts of analysis of data from both observational studies and experimental designs using classical linear methods, together with concepts of collection of data and design of experiments. You will consider linear models and robust regression methods with diagnostics for checking appropriateness of models and strategies for performing feature selection. You will learn to design and analyse experiments considering notions of replication, randomisation and ideas of factorial designs. You will apply, construct and interpret multi-way ANOVA models and make inferences, including post-hoc tests and making corrections for multiple comparisons. Throughout the unit you will use the R statistical package to perform analyses and generate statistical graphics. By completing this unit you will learn how to generate, interpret, visualise and critique linear models.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	STAT2X11 and (DATA2X02 or STAT2X12)
Corequisites ?	None
Prohibitions ?	STAT3912 or STAT3012 or STAT3922 or STAT4022
Assumed knowledge ?	Introductory knowledge to linear algebra (MATH1X02 or equivalent)
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Linh Nghiem, linh.nghiem@sydney.edu.au
Lecturer(s)	Linh Nghiem, linh.nghiem@sydney.edu.au

Assessment

The census date for this unit availability is 2 April 2024

Details
Criteria

Type	Description	Weight	Due	Length
Supervised exam ?	Final examination Final examination	50%	Formal exam period	2 hours
Outcomes assessed:
Tutorial quiz	Tutorial quiz 1 Tutorial quiz during Week 5	10%	Week 05 Due date: 22 Mar 2024 at 18:00	50 minutes
Outcomes assessed:
Assignment	Assignment 1 Answers with calculations. Submitted through Turnitin	10%	Week 07 Due date: 13 Apr 2024 at 23:59	Variable
Outcomes assessed:
Tutorial quiz	Tutorial quiz 2 Tutorial quiz during week 11	15%	Week 11 Due date: 10 May 2024 at 18:00	50 minutes
Outcomes assessed:
Assignment	Assignment 2 Answers with calculations. Submitted through Turnitin	15%	Week 13 Due date: 25 May 2024 at 23:59	Variable
Outcomes assessed:

Assessment summary

Detailed information for each assessment can be found on Canvas.

Assignments: There are two assignments, which must be submitted electronically, as PDF files only, in Turnitin, via Canvas by the deadline. Note that your assignment will not be marked if it is illegible or if it is submitted sideways or upside down. It is your responsibility to check that your assignment has been submitted correctly (check that you can view each page). Late submissions will receive a penalty. A mark of zero will be awarded for all submissions more than 10 days past the original due date. Further extensions past this time will not be permitted.
Final exam: If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator.

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	Representing complete or close to complete mastery of the material.
Distinction	75 - 84	Representing excellence, but substantially less than complete mastery.
Credit	65 - 74	Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.
Pass	50 - 64	Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.
Fail	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.

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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction, simple linear regression	Lecture (3 hr)
Week 02	Simple linear regression, review of linear algebra	Lecture and tutorial (4 hr)
Week 03	Multiple linear regression models: formulation, model fitting, and inference	Lecture and tutorial (4 hr)
Week 04	Multiple linear regression models: F-test, model diagnostics, leverage, outliers and influential analysis	Lecture and tutorial (4 hr)
Week 05	Polynomial regression, additive and interaction models, models with qualitative variables	Lecture and tutorial (4 hr)
Week 06	Model evaluation, variable selection, automated search procedure	Lecture and tutorial (4 hr)
Week 07	ANOVA models	Lecture and tutorial (4 hr)
Week 08	ANOVA models, multiple comparison	Lecture and tutorial (4 hr)
Week 09	Experimental designs: completely randomized designs, block designs	Lecture and tutorial (4 hr)
Week 10	Incomplete block designs, nested designs	Lecture and tutorial (4 hr)
Week 11	Fixed and random effects, variance component estimation	Lecture and tutorial (4 hr)
Week 12	Mixed effect models	Lecture and tutorial (4 hr)
Week 13	Mixed effect models, revision	Lecture and tutorial (4 hr)
Weekly	Computer lab on weekly topic	Computer laboratory (1 hr)

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.

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. formulate and compare linear models, including evaluation of model diagnostics and outlier detection in various settings, including regression, experiments designs, and longitudinal (panel) data
LO2. apply, construct and interpret multi-way ANOVA models and make inference on all parameters
LO3. conduct and recognize different methods of multiple comparisons
LO4. calculate and interpret confidence intervals for all parameters in linear models and distinguish the difference between confidence intervals and prediction intervals
LO5. formulate mixed models, including recognizing the difference between fixed and random effects, and implementing likelihood-based model fitting methods in R

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.

Replacing the two online quizzes by two tutorial quizzes

Additional information

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.

General Laboratory Safety Rules

No eating or drinking is allowed in any laboratory under any circumstances
A laboratory coat and closed-toe shoes are mandatory
Follow safety instructions in your manual and posted in laboratories
In case of fire, follow instructions posted outside the laboratory door
First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory
As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service: unihealth.usyd.edu.au/

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

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

STAT3022: Applied Linear Models

Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney

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

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect