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Regression Modelling G (6557.4)

Level: Graduate Level
Credit Points: 3
HECS Bands:

Band 1 2021 (Commenced After 1 Jan 2021) Band 1 2021 (Commenced Before 1 Jan 2021) Band 2 2013-2020 (Expires 31 Dec 2020)

Faculty: Faculty of Science and Technology
Discipline: Academic Program Area - Technology


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As a result of the Australian Government's and or the ACT Government’s directives requiring physical distancing and restrictions on movement because of the COVID-19 pandemic, you may find that learning activities and/or assessment items in some units you are studying have changed. These changes will not be updated in the published Unit Outline but will be communicated to you via your unit’s UCLearn(Canvas) teaching site. The new learning activities and/or assessment items will continue to meet the unit's learning outcomes, as described in the Unit Outline.

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Unit Outlines

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  • Semester 2, 2020, ON-CAMPUS, BRUCE (195758) - View
  • Semester 1, 2019, ON-CAMPUS, BRUCE (185238) - View
  • Semester 1, 2018, ON-CAMPUS, BRUCE (181726) - View
  • Semester 2, 2016, ON-CAMPUS, BRUCE (151232) - View

If a link to your Unit Outline is not displayed, please check back later. Unit Outlines are generally published by Week One of the relevant teaching period.


This unit explores linear regression techniques for examining relationships between a variety of variables, including both continuous and discrete response variables. Emphasis will be placed on the practical aspects of analysing large data sets, fitting a model and assessing a model using a statistical package. The simple regression model will be reviewed. Multiple regression models will be introduced, together with logistic regression and other generalised linear models. Applications to business, natural and social sciences and other areas will be illustrated.
This unit may be cotaught with 6546 Regression Modelling.

Learning Outcomes

On completion of this unit, students will be able to:

1. Describe the principles of linear modelling in data analysis;

2. Formulate an appropriate model;

3. Estimate the parameters of a model using a statistical computer package;

4. Apply and explain statistical inference to a model using a statistical computer package;

5. Evaluate the appropriateness and validity of a model;

6. Interpret the results of an estimated model and predict the consequences of these results;

7. Produce the results of analyses in a form which is suitable for publication; and

8. Apply important extensions to the linear regression model.

Assessment Items

Contact Hours

A 2-hour lecture and a 2-hour lab per week.


6275 Statistical Analysis and Decision Making G OR 6554 Introduction to Statistics G OR 1809 Data Analysis in Science.



Assumed Knowledge


Incompatible Units

6546 Regression Modelling.

Equivalent Units


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