Students

STAT3175 – Linear Models

2026 – Session 1, In person-scheduled-weekday, North Ryde

General Information

Download as PDF
Unit convenor and teaching staff Unit convenor and teaching staff Unit Convenor
Dr Tania Prvan
tania.prvan@mq.edu.au
Contact via tania.prvan@mq.edu.au
12 Wally's Walk Level 6 Room 6.29
TBA
Credit points Credit points
10
Prerequisites Prerequisites
20cp at 2000 level including (STAT2170 or STAT2371 or BIOL2610(P) or PSYU2248(P)) and (10cp from FOSE1005 or MATH1000 or MATH1010-MATH1025)
Corequisites Corequisites
Co-badged status Co-badged status
STAT6175
Unit description Unit description

This unit discusses statistical modelling in general and in particular demonstrates the wide applicability of linear and generalized linear models. Topics include multiple linear regression, logistic regression and Poisson regression. The emphasis is on practical issues in data analysis with some reference to the theoretical background. Statistical packages are used for both model fitting and diagnostic testing.

Learning in this unit enhances student understanding of global challenges identified by the United Nations Sustainable Development Goals (UNSDGs) Good Health and Well Being; Quality Education; Industry, Innovation and Infrastructure

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Define relevant terminology and describe the main concepts of linear models and simple generalized linear models.
  • ULO2: Formulate and solve theoretical problems in linear modelling (using matrix notation when necessary).
  • ULO3: Fit a linear model to obtain estimates together with their standard errors in applied problems.
  • ULO4: Analyse the adequacy of a linear model and suggest appropriate modifications when needed.
  • ULO5: Formulate and solve applied problems using linear modelling.
  • ULO6: Use standard statistics packages to carry out these analyses
  • ULO7: Communicate clearly your knowledge of the subject matter of linear models and their solutions to problems involving linear modelling.

General Assessment Information

Requirements to Pass this Unit

To pass this unit you need to:

  • Achieve a total mark equal to or greater than 50% across all assessments

We strongly encourage all students to actively participate in all learning activities. Regular engagement is crucial for your success in this unit, as these activities provide opportunities to deepen your understanding of the material, collaborate with peers, and receive valuable feedback from instructors, to assist in completing the unit assessments. Your active participation not only enhances your own learning experience but also contributes to a vibrant and dynamic learning environment for everyone.

Assignment Submission

Assignment submission will be online through the iLearn page. Your name and Student ID should appear on the first page.

Submit assignments online via the appropriate assignment link on the iLearn page. A personalised cover sheet is not required with online submissions. Read the submission statement carefully before accepting it as there are substantial penalties for making a false declaration.

  • Assignment submission is via iLearn.
  • Please make sure that your assignment is word processed.
  • You should upload one single PDF file.
  • Please note the quick guide on how to upload your assignments provided on the iLearn page. 
  • If there are technical obstructions to your submission online, please email us to let us know.

You may submit as often as required prior to the due date/time. Please note that each submission will completely replace any previous submissions. It is in your best interest to make frequent submissions of your partially completed work as insurance against technical or other problems near the submission deadline.

Late Assessment Submission Penalty 

 Late Submission Policy

  • 5% penalty per day: If you submit your assessment late, 5% of the total possible marks will be deducted for each day (including weekends), up to 7 days.

    • Example 1 (out of 100): If you score 85/100 but submit 20 hours late, you will lose 5 marks and receive 80/100.

    • Example 2 (out of 30): If you score 27/30 but submit 1 day late, you will lose 1.5 marks and receive 25.5/30.

  • After 7 days: Submissions more than 7 days late will receive a mark of 0.

  • Extensions:

    • Automatic short extension: Some assessments are eligible for automatic short extension. You can only apply for an automatic short extension before the due date.

    • Special Consideration: If you need more time due to serious issues and for any assessments that are not eligible for Short Extension, you must apply for Special Consideration.

Assessments where Late Submissions will be accepted 

In this unit, late submissions will accepted as follows: 

  • Assignment 1 – YES, Standard Late Penalty applies 
  • Assignment 2 – YES, Standard Late Penalty applies 
  • Final Examination - NO, unless Special Consideration is Granted

Special Consideration

The Special Consideration Policy aims to support students who have been impacted by short-term circumstances or events that are serious, unavoidable and significantly disruptive, and which may affect their performance in assessment. If you experience circumstances or events that affect your ability to complete the assessments in this unit on time, please inform the convenor and submit a Special Consideration request through ask.mq.edu.au.

If granted a successful second special consideration for Assignment 1 or Assignment 2 you will be given a new assessment with 2 weeks to complete it in. The Unit Convenor will email this assessment along with dataset/s to your student email address. 

 

FINAL EXAM POLICY 

It is Macquarie University policy not to set early examinations for individuals or groups of students. All students are expected to ensure that they are available until the end of the teaching semester, that is, the final day of the official examination period. The only excuse for not sitting an examination at the designated time is because of documented illness or unavoidable disruption. In these special circumstances, you may apply for special consideration via ask.mq.edu.au.

If you receive special consideration for the final exam, a supplementary exam will be scheduled in the interval between the regular exam period and the start of the next session. By making a special consideration application for the final exam you are declaring yourself available for a resit during this supplementary examination period and will not be eligible for a second special consideration approval based on pre-existing commitments. Please ensure you are familiar with the policy prior to submitting an application.

You can check the supplementary exam information page on FSE101 in iLearn (bit.ly/FSESupp) for dates, and approved applicants will receive an individual notification one week prior to the exam with the exact date and time of their supplementary examination.

If granted a second special consideration for Assignment 1 or Assignment 2 you will be given a new assessment with 2 weeks to complete it in.

Assessment Tasks

Name Weighting Hurdle Due Groupwork/Individual Short Extension AI Approach
Assignment 1 25% No 06/04/2026 Individual Yes Open
Assignment 2 25% No 24/05/2026 Individual Yes Open
Final examination 50% No Formal Examination Period Individual No Observed

Assignment 1

Assessment Type 1: Problem-based task
Indicative Time on Task 2: 20 hours
Due: 06/04/2026
Weighting: 25%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open

This assessment reflects professional practice in statistical data analysis, where applying theoretical concepts and practical skills is essential for interpreting real-world data. You will analyse assigned datasets using techniques covered in lectures and SGTA, producing clear and structured results. Through this task, you will develop your ability to apply statistical methods, interpret findings, and communicate results effectively.
On successful completion you will be able to:
  • Define relevant terminology and describe the main concepts of linear models and simple generalized linear models.
  • Formulate and solve theoretical problems in linear modelling (using matrix notation when necessary).
  • Fit a linear model to obtain estimates together with their standard errors in applied problems.
  • Analyse the adequacy of a linear model and suggest appropriate modifications when needed.
  • Formulate and solve applied problems using linear modelling.
  • Use standard statistics packages to carry out these analyses
  • Communicate clearly your knowledge of the subject matter of linear models and their solutions to problems involving linear modelling.

Assignment 2

Assessment Type 1: Problem-based task
Indicative Time on Task 2: 20 hours
Due: 24/05/2026
Weighting: 25%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open

This assessment reflects professional practice in statistical data analysis, where applying theoretical concepts and practical skills is essential for interpreting real-world data. You will analyse assigned datasets using techniques covered in lectures and SGTA, producing clear and structured results. Through this task, you will develop your ability to apply statistical methods, interpret findings, and communicate results effectively.
On successful completion you will be able to:
  • Define relevant terminology and describe the main concepts of linear models and simple generalized linear models.
  • Formulate and solve theoretical problems in linear modelling (using matrix notation when necessary).
  • Fit a linear model to obtain estimates together with their standard errors in applied problems.
  • Analyse the adequacy of a linear model and suggest appropriate modifications when needed.
  • Formulate and solve applied problems using linear modelling.
  • Use standard statistics packages to carry out these analyses
  • Communicate clearly your knowledge of the subject matter of linear models and their solutions to problems involving linear modelling.

Final examination

Assessment Type 1: Examination
Indicative Time on Task 2: 20 hours
Due: Formal Examination Period
Weighting: 50%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Observed

The purpose of the Final Exam is for you to formally demonstrate the expertise you have gained in this unit. The exam may include any topic covered in the unit. It will be held during the University Final Examination period.
On successful completion you will be able to:
  • Define relevant terminology and describe the main concepts of linear models and simple generalized linear models.
  • Formulate and solve theoretical problems in linear modelling (using matrix notation when necessary).
  • Fit a linear model to obtain estimates together with their standard errors in applied problems.
  • Analyse the adequacy of a linear model and suggest appropriate modifications when needed.
  • Formulate and solve applied problems using linear modelling.
  • Use standard statistics packages to carry out these analyses

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • Academic Success for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation.

3 An automatic short extension is available for some assessments. Apply through the Service Connect Portal.

Delivery and Resources

There is one two hour lecture and one two hour SGTA each week. Lectures begin in Week 1 and SGTAs in Week 2. Please consult the timetable for the scheduling of these activities. The timetable for classes can be found on the University website at: https://publish.mq.edu.au/. Enrolment can be managed using eStudent at: https://students.mq.edu.au/support/technology/systems/estudent.

Technologies used and required

Lecture material will be placed on iLearn. The statistical package R will be used.

SGTA

SGTAs are held on campus. You practice the techniques learnt in lectures. You will complete worksheets as part of the learning process.

Text book

The recommended text is: Chatterjee. S. & Hadi, A. S. (2012). Regression Analysis By Example, 5th Revised edition, Wiley. This is available online through the Macquarie University Library website.

Methods of Communication

We will communicate with you via your university email or through announcements on iLearn. Queries to the convenor can be sent to tania.prvan@mq.edu.au from your university email address. 

Unit Schedule

Week 1 Topic/s
1 Simple linear regression. Multiple linear regression.
2 The model in matrix form. Diagnostics.
3 Diagnostics. Transformations.
4 Transformations. Collinearity.
5 Polynomial regression. Categorical covariates.
6 Analysis of change. Analysis of covariance (ANCOVA).
  TWO WEEK BREAK
7 Confounding. Interaction.
8 Variable selection. Model building.
9 Introduction to generalized linear models. Logistic regression.
10 Logistic regression. Poisson regression.
11 Poisson regression. Negative binomial regression.
12 Negative binomial regression. Gamma regression.
13 Revision

 

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct

Results

Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit connect.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Academic Integrity

At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Academic Success

Academic Success provides resources to develop your English language proficiency, academic writing, and communication skills.

The Library provides online and face to face support to help you find and use relevant information resources. 

Student Services and Support

Macquarie University offers a range of Student Support Services including:

Student Enquiries

Got a question? Ask us via the Service Connect Portal, or contact Service Connect.

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Changes from Previous Offering

There are now only 3 assessments in this unit: 2 assignments instead of 3 and a final examination.

Changes since First Published

Date Description
23/03/2026 'Short Extension' date updated

Unit information based on version 2026.03 of the Handbook