Regression Modelling G (6557.4)
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 |
Academic Program Area - Technology | Graduate Level | Band 1 2021 (Commenced After 1 Jan 2021) Band 1 2021 (Commenced Before 1 Jan 2021) |
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. 海角社区 graduates are professional - communicate effectively1. 海角社区 graduates are professional - display initiative and drive, and use their organisation skills to plan and manage their workload
1. 海角社区 graduates are professional - employ up-to-date and relevant knowledge and skills
1. 海角社区 graduates are professional - take pride in their professional and personal integrity
1. 海角社区 graduates are professional - use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems
2. 海角社区 graduates are global citizens - behave ethically and sustainably in their professional and personal lives
2. 海角社区 graduates are global citizens - make creative use of technology in their learning and professional lives
3. 海角社区 graduates are lifelong learners - adapt to complexity, ambiguity and change by being flexible and keen to engage with new ideas
3. 海角社区 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.Year | Location | Teaching period | Teaching start date | Delivery mode | Unit convener |
---|---|---|---|---|---|
2025 | Bruce, Canberra | Semester 1 | 03 February 2025 | On-campus | Dr Shuangzhe Liu |
2026 | Bruce, Canberra | Semester 1 | 16 February 2026 | On-campus | Dr Shuangzhe Liu |
Required texts
Required text: Pardoe, I. (2021) Applied Regression Modeling, 3rd edition, John Wiley & Sons.
It is available at the 海角社区 library or can be purchased from Wiley.
Submission of assessment items
Extensions & Late submissions
Approval of extenuating circumstances will be dependent upon the production of supporting documentation and at the discretion of the unit convener.
Special assessment requirements
An aggregate mark of 50% overall is required to pass the unit.
Your final grade will be determined as follows:
Final mark (100%) = Test 1 (10%) + Test 2 (15%) + Test 3 (15%) + Assignment (60%)
The unit convenor reserves the right to question students on any of their submitted work for moderation and academic integrity purposes.
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.
海角社区 students have to complete the annually to learn about academic integrity and to understand the consequences of academic integrity breaches (or academic misconduct).
海角社区 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 , , and University of Canberra (Student Conduct) Rules 2023. For further advice, visit Study Skills.
Learner engagement
There will be a total workload of 150 hours which comprises of 24 hours of lectures, 11 hours of labs, 34 hours of review/prep time for tests with 6 hours attempt time, and 75 hours of review/prep time and analysis/write-up for the assignment.
Participation requirements
Your participation in both class and online activities will enhance your understanding of the unit content and therefore the quality of your assessment responses. Lack of participation will result in your inability to satisfactorily pass assessment items.
Attendance at the Week 5 and the Week 12 labs is mandatory in order to successfully complete the in-class test (Week 5) and the assignment presentation (Week 12).
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
- Semester 1, 2025, On-campus, 海角社区 - Canberra, Bruce (224493)
- Semester 1, 2024, On-campus, 海角社区 - Canberra, Bruce (218402)
- Semester 2, 2023, On-campus, 海角社区 - Canberra, Bruce (214071)
- Semester 2, 2022, On-campus, 海角社区 - Canberra, Bruce (207421)
- Semester 2, 2021, On-campus, 海角社区 - Canberra, Bruce (202271)
- Semester 2, 2020, On-campus, 海角社区 - Canberra, Bruce (195758)
- Semester 1, 2019, On-campus, 海角社区 - Canberra, Bruce (185238)
- Semester 1, 2018, On-campus, 海角社区 - Canberra, Bruce (181726)