Regression Modelling G (6557)

Available teaching periods	Delivery mode	Location
View teaching periods	On-campus	Bruce, Canberra
0.125	3	Faculty Of Science And Technology
Academic Program Area - Technology	Graduate Level	Band 1 2021 (Commenced After 1 Jan 2021) Band 1 2021 (Commenced Before 1 Jan 2021)

Introduction

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

Availability for enrolment in 2025 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
2024	Bruce, Canberra	Semester 1	05 February 2024	On-campus	Dr Shuangzhe Liu
2025	Bruce, Canberra	Semester 1	03 February 2025	On-campus	Dr Shuangzhe Liu

Timetable

Not available

Resources

Required texts

Required text: Pardoe, I. (2020) Applied Regression Modeling, 3rd edition, John Wiley & Sons.

It can be purchased from Wiley or The School Locker.

Assessment requirements

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

In order to pass this unit you need to get an overall total of 50%. The assessment items contibute the percentages shown in 5a.

The unit convenor reserves the right to question students on any of their submitted work for moderation and academic integrity purposes, which may result in an adjustment to the marks awarded for a specific task.

Grade calculation

Your grade will be determined on the basis of a composite score obtained by the weightings shown previously. This composite score will then be scaled to a numerical grade consistent with the descriptions:

Grade	Mark range
Pass	50-64
Credit	65-74
Distinction	75-84
High Distinction	85-100

Unit mark = (0.1 * Test 1 mark) + (0.2 * Test 2 mark) + (0.3 * Assignment mark) + (0.4 * Final Test mark)

Academic integrity

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 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 91Porn (Student Conduct) Rules 2023. For further advice, visit Study Skills.

Student responsibility

Learner engagement

There will be a total workload of 150 hours which comprises of 24 hours of lectures, 11 hours of labs, 36 hours of self-study, 45 hours of review/prep time for tests with 6 hours attempt time, and 28 hours of review/prep time and analysis/write-up for the assignment.

Participation requirements

Your participation in both face-to-face and online activities will enhance your understanding of the unit content and therefore the quality of your assessment responses. Lack of participation may result in your inability to satisfactorily pass assessment items. As per University policy, students are expected to be available for all assessment items held during the semester.

Required IT skills

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

This unit involves online meetings in real time using the Virtual Room in your UCLearn teaching site. The Virtual Room allows you to communicate in real time with your lecturer and other students. To participate verbally, rather than just typing, you will need a microphone. For best audio quality we recommend a microphone and speaker headset. For more information and to test your computer, go to the Virtual Room in your UCLearn site and 'Join Course Room'. This will trigger a tutorial to help familiarise you with the functionality of the virtual room.

Work placement, internships or practicums

Not Applicable

Additional information

Provision of information to the group

Notifications through the Canvas Announcements Forum or the Canvas Discussion Forums are deemed to be made to the whole class. It is the responsibility of the student to ensure that they check for announcements on the Unit's Canvas website (Canvas forum messages are also emailed to student email addresses only). Students should ensure they check their student email regularly. The discussion forums will be checked by staff regularly.

Use of student email account

The University Email policy states that "students wishing to contact the University via email regarding administrative or academic matters need to send the email from the University account for identity verification purposes". Therefore all unit enquiries should be emailed using a student university email account. Students should contact servicedesk@canberra.edu.au if they have any issues accessing their university email account.

In all cases of absence, sickness or personal problems it is the student's responsibility to ensure that the unit Convener is informed. The minimum participation requirement must be met in order to pass the unit (regardless of supporting documentation).

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