Regression Modelling (6546.4)
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 | Level 3 - Undergraduate Advanced Unit | Band 1 2021 (Commenced After 1 Jan 2021) Band 1 2021 (Commenced Before 1 Jan 2021) |
This unit may be cotaught with 6557 Regression Modelling G.
Learning outcomes
On successful 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; and
6. Produce and interpret the results of an estimated model and predict the consequences of these results.
Graduate attributes
1. UC graduates are professional - communicate effectively1. 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
6540 Introduction to Statistics OR 5123 Business Statistics OR 1809 Data Analysis in Science OR 11723 Data Analysis Skills for ScienceCorequisites
None.Incompatible units
6557 Regression Modelling GEquivalent 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 |
Required texts
Required text: Pardoe, I. (2021) Applied Regression Modeling, 3rd edition, John Wiley & Sons.
It is available at the UC 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.
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
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, UC - Canberra, Bruce (224487)
- Semester 1, 2024, On-campus, UC - Canberra, Bruce (218391)
- Semester 2, 2023, On-campus, UC - Canberra, Bruce (213989)
- Semester 2, 2022, On-campus, UC - Canberra, Bruce (207351)
- Semester 2, 2021, On-campus, UC - Canberra, Bruce (202200)
- Semester 2, 2020, On-campus, UC - Canberra, Bruce (195750)
- Semester 1, 2019, On-campus, UC - Canberra, Bruce (185231)
- Semester 1, 2018, On-campus, UC - Canberra, Bruce (181705)