Students

ACST8083 – Actuarial Statistics

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

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff UC, lecturer
Pavel Shevchenko
pavel.shevchenko@mq.edu.au
UC, lecturer
Ken Siu
ken.siu@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
STAT8310
Corequisites Corequisites
Co-badged status Co-badged status
This unit is co-badged ACST3061
Unit description Unit description

This unit examines the use of statistical models in the general insurance context. Applications will include linear models and generalised linear models and Bayesian statistics including Credibility Theory. Students gaining a credit average across both ACST8083 and STAT8310 (minimum mark of 60 on both units) will satisfy the requirements for exemption from the professional subject CS1 of the Actuaries Institute.

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: Apply the method of maximum likelihood estimation in a range of contexts and understand associated statistical distribution theory.
  • ULO2: Explain and apply both simple and multiple linear regression methodology.
  • ULO3: Develop an understanding of the theory and practice of generalised linear modelling (GLMs).
  • ULO4: Explain and apply the fundamental concepts of Bayesian statistics.
  • ULO5: Apply credibility theory to insurance problems.
  • ULO6: Apply these statistical techniques in solving practical insurance problems.

General Assessment Information

Late Submission Penalties

If you submit your assessment late, 5% of the total possible marks will be deducted for each day (including weekends), up to 7 days. Submissions more than 7 days late will receive a mark of 0.

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 20 hours late, you will lose 1.5 marks and receive 25.5/30.

 

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. Need help? Review the Special Consideration page for further details.

Assessment Tasks

Name Weighting Hurdle Due Groupwork/Individual Short Extension AI Approach
Skills development: Solving problems with R 25% No 27/05/2026 Individual Yes Open AI
Formal examination: Test 15% No Week 7 Individual No Observed
Formal examination 60% No During university examination period Individual No Observed

Skills development: Solving problems with R

Assessment Type 1: Problem-based task
Indicative Time on Task 2: 20 hours
Due: 27/05/2026
Weighting: 25%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open AI

The purpose of this assessment is for you to develop your digital skills in using R to solve problems.

 

You will develop statistical modelling and data analysis skills using R. You will apply maximum likelihood estimation, implement linear regression methods, and explore generalised linear models (GLMs). Additionally, you will apply Bayesian statistics and credibility theory to real-world problems, particularly in insurance and actuarial contexts.

 

Skills in focus:

  • Digital skills
  • Critical thinking and problem solving 
  • Discipline knowledge 

Deliverable(s): Written report

Individual assessment
On successful completion you will be able to:
  • Apply the method of maximum likelihood estimation in a range of contexts and understand associated statistical distribution theory.
  • Explain and apply both simple and multiple linear regression methodology.
  • Develop an understanding of the theory and practice of generalised linear modelling (GLMs).
  • Explain and apply the fundamental concepts of Bayesian statistics.
  • Apply credibility theory to insurance problems.
  • Apply these statistical techniques in solving practical insurance problems.

Formal examination: Test

Assessment Type 1: Examination
Indicative Time on Task 2: 20 hours
Due: Week 7
Weighting: 15%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Observed

The purpose of this assessment is for you to demonstrate your understanding and knowledge of key topics from the unit.

 

You will participate in a formal test.

 

Deliverable(s): Test

Individual assessment
On successful completion you will be able to:
  • Apply the method of maximum likelihood estimation in a range of contexts and understand associated statistical distribution theory.
  • Explain and apply both simple and multiple linear regression methodology.
  • Develop an understanding of the theory and practice of generalised linear modelling (GLMs).

Formal examination

Assessment Type 1: Examination
Indicative Time on Task 2: 28 hours
Due: During university examination period
Weighting: 60%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Observed

The purpose of this assessment is for you to demonstrate the expertise they have gained in Actuarial Statistics.

 

You will participate in a 3-hour exam held during the University Examination period. Important information about the exam will be made available on the unit iLearn page. You should also review the MQ Exams website for general tips. 

 

Deliverable(s): Formal exam

Individual assessment
On successful completion you will be able to:
  • Apply the method of maximum likelihood estimation in a range of contexts and understand associated statistical distribution theory.
  • Explain and apply both simple and multiple linear regression methodology.
  • Develop an understanding of the theory and practice of generalised linear modelling (GLMs).
  • Explain and apply the fundamental concepts of Bayesian statistics.
  • Apply credibility theory to insurance problems.
  • Apply these statistical techniques in solving practical insurance problems.

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

Please refer to iLearn

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 since First Published

Date Description
22/02/2026 as requested, in section about penalty for late submission, I added info about extensions: ******************************************************* 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. Need help? Review the Special Consideration page for further details.

Unit information based on version 2026.02 of the Handbook