Regression Modelling G (6557.4)

Please note these are the 2027 details for this unit
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Regression Modelling G
Available teaching periods Delivery mode Location
View teaching periods On-campus
 Bruce, Canberra
EFTSL Credit points Faculty
0.125 3 Faculty Of Science And Technology
Discipline Study level HECS Bands
School Of Information Technology & Systems Graduate Level Band 1 2021 (Commenced After 1 Jan 2021)
Band 1 2021 (Commenced Before 1 Jan 2021)
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.



Graduate attributes

1. UC graduates are professional - communicate effectively
1. UC graduates are professional - display initiative and drive, and use their organisation skills to plan and manage their workload
1. UC graduates are professional - employ up-to-date and relevant knowledge and skills
1. UC graduates are professional - take pride in their professional and personal integrity
1. UC graduates are professional - use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems
2. UC graduates are global citizens - behave ethically and sustainably in their professional and personal lives
2. UC graduates are global citizens - make creative use of technology in their learning and professional lives
3. UC graduates are lifelong learners - adapt to complexity, ambiguity and change by being flexible and keen to engage with new ideas
3. UC graduates are lifelong learners - evaluate and adopt new technology


Prerequisites

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


Corequisites

None.

Incompatible units

6546 Regression Modelling.

Equivalent units

None.

Assumed knowledge

None.
Availability for enrolment in 2027 is subject to change and may not be confirmed until closer to the teaching start date.
Year Location Teaching period Teaching start date Delivery mode Unit convener
2026 Bruce, Canberra Semester 1 16 February 2026 On-campus Dr Sumaira Qureshi
2027 Bruce, Canberra Semester 1 15 February 2027 On-campus Dr Sumaira Qureshi
The information provided should be used as a guide only. Timetables may not be finalised until week 2 of the teaching period and are subject to change. Search for the unit timetable.

Required texts

Required text: Pardoe, I. (2021). Applied Regression Modeling (3rd ed.). John Wiley & Sons.

This text is available through the UC library or can be purchased directly from Wiley.

Submission of assessment items

Extensions & Late submissions

Approval of extenuating circumstances is subject to the provision of supporting documentation and is at the discretion of the unit convener. 

Artificial intelligence
Guided - Students will be guided in how GenAI must/may be used in completing the assessment as detailed in the unit outline and assessment instructions. More detailed information can be found at GenAI and Assessment at UC.

Special assessment requirements

An aggregate mark of 50% or higher is required to pass the unit.

Your final grade will be determined as follows:
Final mark (100%) = Test 1 (10%) + Test 2 (20%) + Test 3 (10%) + Assignment (60%)

The unit convenor reserves the right to question students on any of their submitted work for the purpose of moderation and academic integrity.

Grade Calculation

The final unit mark will be calculated by summing the weighted scores of all assessment items (see Section 5 for assessment items and weightings).

Your final grade will be calculated using the university's standard grading schema:
https://www.canberra.edu.au/content/myuc/home/course/grading.html

Grade Numerical Grade# (%)
Pass (P) 50 — 64
Credit (CR) 65 — 74
Distinction (DI) 75 — 84
High Distinction (HD)## 85 — 100

#Final marks close to grade boundaries (i.e., 84%, 74%, 64%, 49%) may be moderated based on evidence of lab class engagement. This additional consideration will apply only to students who have demonstrated consistent and active participation throughout the semester.
##HD grades are awarded for work of outstanding quality that demonstrates achievement of the unit learning outcomes, evidenced through excellence in areas such as critical analysis, logical argumentation, interpretation of materials, or use of appropriate methodology. This grade may also be awarded to recognise exceptional originality or creativity.

Students must apply academic integrity in their learning and research activities at UC. This includes submitting authentic and original work for assessments and properly acknowledging any sources used.

Academic integrity involves the ethical, honest and responsible use, creation and sharing of information. It is critical to the quality of higher education. Our academic integrity values are honesty, trust, fairness, respect, responsibility and courage.

UC students have to complete the Academic Integrity Module annually to learn about academic integrity and to understand the consequences of academic integrity breaches (or academic misconduct).

UC uses various strategies and systems, including detection software, to identify potential breaches of academic integrity. Suspected breaches may be investigated, and action can be taken when misconduct is found to have occurred.

Information is provided in the Academic Integrity Policy, Academic Integrity Procedure, and University of Canberra (Student Conduct) Rules 2023. For further advice, visit Study Skills.

Learner engagement

Activities Details Hours
Weekly engagement with lectures 1 hour/week (11 weeks) 11
Weekly tutorials 2 hours/week (11 weeks) 22
Weekly study commitments (in addition to the above) ≈2 hours/week (11 weeks) 22
Mastery Test 1 (online) Preparation and completion 10
Mastery Test 2 (in-class) Preparation and completion 10
Mastery Test 3 (online) Preparation and completion 10
Assignment Review, Preparation, analysis, recording, and write-up 65
TOTAL - 150

Participation requirements

Active participation in both class and online activities will enhance your understanding of the unit content and, therefore, the quality of your assessment responses. A lack of participation will affect your ability to satisfactorily complete assessment items.

Attendance at the Week 5 lab is mandatory in order to successfully complete the in-class test.

Required IT skills

It is assumed that students have some familiarity with the use of a computer, Microsoft Excel, and R/RStudio.

Work placement, internships or practicums

Not Applicable

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