Regression Modelling studies the use of linear regression techniques for examining relationships between variables across a broad range of different disciplines including the sciences, arts, business, politics, management and the financial sciences. Further, students use the skills acquired in this course to identify problems, interpret and analyse results, and provide solutions while engaging with external stakeholders.
The course emphasises the principles of statistical modelling through the iterative process of fitting a model, examining the fit to assess imperfections in the model and suggest alternative models, and continuing until a satisfactory model is reached. Both steps in this process require the use of versatile software: model fitting uses various numerical algorithms, and model assessment involves extensive use of graphical displays. The R statistical computing package is used as an integral part of the course.
Students in this course are exposed to a variety of different problems/issues from different disciplines and seek to provide input to these problems through application of quantitative data analysis skills. The data sets and/or problems/issues are introduced through a variety of means from blogs or newspaper articles, and case studies (real ad fictional) to drive and contextualise the data stemming from the variety of different disciplines.
Regression Modelling for Actuarial Students focuses on the use of linear regression techniques to examine relationships between variables in contexts directly relevant to actuarial science, insurance, and financial risk management. While akin to STAT2008, this course is specifically designed to meet the applied modelling needs of actuarial students, with examples and case studies drawn from actuarial practice and financial applications.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Use the skills developed in this course to identify problems in a variety of different disciplines to interpret and analyse results and provide solutions while engaging with external stakeholders from diverse fields of study.
- Work collaboratively in groups to analyse results, provide solutions and orally present findings and discussions to a diverse range of stakeholders from a variety of areas of study.
- Demonstrate a working knowledge of the R statistical computing language, particularly the graphical capabilities.
- Fit linear regression models and interpret model parameters, including relationships between a response variable and a covariate.
- Assess and refine simple and multiple linear regression models based on diagnostic measures, including identifying outlying and influential data points.
- Explore model selection in a linear regression modelling context in a variety of fields of study.
- Define and describe the features of a Generalised Linear Model (GLM), fit GLM models, assess and refine the models based on diagnostic measures, and interpret model output.
Other Information
This course is compulsory for students who enrolled or transferred into the Bachelor of Actuarial Studies. Contact the convener of the Bachelor of Actuarial Studies if you are unsure whether to enrol in STAT2008 or STAT2014.
Indicative Assessment
- The research-based assessment will consist of assignments. Students will work to perform analysis of the dataset in the initial part of the assignment. In the second part of the assignment, students will use the results to suggest possible solutions to the problem raised in the dataset and communicate their solutions. (25) [LO 1,2,3,4,5,6]
- The other assessment may include but is not restricted to: exams, quizzes, presentations and other assessments as appropriate. (75) [LO 1,2,3,4,5,6,7]
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
Students are expected to commit at least 10 hours per week to completing the work in this course. This will include at least 3 contact hours per week and up to 7 hours of private study time.
Requisite and Incompatibility
Prescribed Texts
Information about the prescribed textbook will be available via the class summary.
Assumed Knowledge
The course uses the R statistical package, which uses matrix algebra to implement the regression modelling techniques. An understanding of matrix algebra (equivalent to an introductory mathematics course such as MATH1113) would be helpful in understanding how the R routines work, but such knowledge is not a required prerequisite.Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 1
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
First Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 2568 | 23 Feb 2026 | 02 Mar 2026 | 31 Mar 2026 | 29 May 2026 | In Person | N/A |
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 7564 | 27 Jul 2026 | 03 Aug 2026 | 31 Aug 2026 | 30 Oct 2026 | In Person | N/A |