• Offered by Rsch Sch of Finance, Actuarial Studies & App Stats
  • ANU College ANU College of Business and Economics
  • Classification Transitional
  • Course subject Statistics
  • Areas of interest Statistics

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 thorough understanding of the R statistical computing language, particularly the graphical capabilities;
  2. Fit simple linear regression models, interpret model parameters and relate theses back to the underlying research question;
  3. Analyse and interpret relationships between a response variable and a covariate;
  4. Analyse and interpret relationships between a response variable and several covariates;
  5. Assess and refine simple and multiple linear regression models based on diagnostic measures, including identifying and discuss the implications of outlying and influential data points; and,
  6. Select and discuss a useful multiple linear regression model from a number of competing models.

Other Information

Offerings of this course outside of Semester 1 and Semester 2 are available only to students enrolled in the M. Applied Data Analytics. 

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 130 hours of work in completing this course. This includes time spent in scheduled classes and self-directed study time.

Inherent Requirements

Not applicable

Requisite and Incompatibility

To enrol in this course, students must have completed STAT7055 (or STAT1003 or STAT1008) or be enrolled in the M. Actuarial Studies, M. Actuarial Practice, M. Statistics, MSc in Quantitative Biology and Bioinformatics, or MSc in QBB advanced version. Incompatible with STAT2008, STAT2014, STAT4038, STAT6014 and STAT7001.

Prescribed Texts

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

Specialisations

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
2352 24 Feb 2020 02 Mar 2020 31 Mar 2020 29 May 2020 In Person N/A

Autumn Session

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
5602 25 May 2020 29 May 2020 12 Jun 2020 24 Jul 2020 In Person N/A

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