• Offered by Rsch Sch of Finance, Actuarial Studies & App Stats
  • ANU College ANU College of Business and Economics
  • Course subject Statistics
  • Areas of interest Statistics
  • Academic career UGRD
  • Course convener
    • Dr Anton Westveld
  • Mode of delivery In Person
  • Co-taught Course
  • Offered in First Semester 2020
    Second Semester 2020
    See Future Offerings

Regression Modelling is a course in applied statistics that studies the use of linear regression techniques for examining relationships between variables. 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 a computer: 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.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Demonstrate a working knowledge of the R statistical computing language, particularly the graphical capabilities;
  2. Fit simple linear regression models and interpret model parameters;
  3. Summarise and analyse relationships between a response variable and a covariate;
  4. Summarise and analyse relationships between a response variable and several covariates;
  5. Assess and refine simple and multiple linear regression models based on diagnostic measures, including identifying outlying and influential data points; and,
  6. Carry out model selection in a multiple linear regression modelling context.

Other Information

Students who commenced enrolment in the Bachelor of Actuarial Studies in 2019 or later, and students considering transferring to the Bachelor of Actuarial Studies, should take STAT2014 rather than STAT2008. Contact the convener of the Bachelor of Actuarial Studies if you unsure whether to enrol in STAT2008 or STAT2014.

Indicative Assessment

  1. Typical assessment may include, but is not restricted to: exams, assignments, quizzes, presentations and other assessment as appropriate (100) [LO 1,2,3,4,5,6]

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.

Inherent Requirements

Not applicable

Requisite and Incompatibility

To enrol in this course you must have completed STAT1003 or STAT1008. Or be enrolled in the Bachelor of Applied Data Analytics. Incompatible with STAT2014 and STAT6014.

Prescribed Texts

Information about the prescribed textbook will be available via the Class Summary

Majors

Minors

Fees

Tuition fees are for the academic year indicated at the top of the page.  

If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Student Contribution Band:
2
Unit value:
6 units

If you are an undergraduate student and 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). You can find your student contribution amount for each course 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
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

The list of offerings for future years is indicative only.
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
2333 24 Feb 2020 02 Mar 2020 31 Mar 2020 29 May 2020 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
8620 27 Jul 2020 03 Aug 2020 31 Aug 2020 30 Oct 2020 In Person N/A

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions