Students

ACST3061 – Actuarial Statistics

2022 – Session 1, In person/Online-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff
Pavel Shevchenko
pavel.shevchenko@mq.edu.au
Deanna Tracy
deanna.tracy@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
STAT272 or STAT2372
Corequisites Corequisites
Co-badged status Co-badged status
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 STAT2372, STAT2371 and ACST3061 (minimum mark of 60 on all three 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.

General Assessment Information

Late submissions of assessments

Unless a Special Consideration request has been submitted and approved, no extensions will be granted. There will be a deduction of 10% of the total available assessment-task marks made from the total awarded mark for each 24-hour period or part thereof that the submission is late. Late submissions will only be accepted up to 96 hours after the due date and time.

No late submissions will be accepted for timed assessments – e.g., quizzes, online tests.

 

Table 1: Penalty calculation based on submission time

Submission time after the due date (including weekends)

Penalty (% of available assessment task mark)

Example: for a non-timed assessment task marked out of 30

< 24 hours

10%

10% x 30 marks = 3-mark deduction

24-48 hours

20%

20% x 30 marks = 6-mark deduction

48-72 hours

30%

30% x 30 marks = 9-mark deduction

72-96 hours

40%

40% x 30 marks = 12-mark deduction

> 96 hours

100%

Assignment won’t be accepted

Special Consideration

To request an extension on the due date/time for a timed or non-timed assessment task, you must submit a Special Consideration application. An application for Special Consideration does not guarantee approval.

The approved extension date for a student becomes the new due date for that student. The late submission penalties above then apply as of the new due date.

Assessment Tasks

Name Weighting Hurdle Due
Class Test 20% No Week 7
Assignment 20% No Week 9 and Week 12
Final Exam 60% No During university examination period

Class Test

Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 20 hours
Due: Week 7
Weighting: 20%

The test will be approximately 90 minutes, to be held during class time.
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).

Assignment

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 20 hours
Due: Week 9 and Week 12
Weighting: 20%

There are two individual written assignments on problem solving using R.
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.

Final Exam

Assessment Type 1: Examination
Indicative Time on Task 2: 28 hours
Due: During university examination period
Weighting: 60%

The final examination will be a three-hour written paper with ten minutes reading time, to be held during the University Examination period.
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.

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
  • the Writing Centre 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

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 ask.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/

The Writing Centre

The Writing Centre 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 AskMQ, 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.

Unit information based on version 2022.05 of the Handbook