Regression Modelling (6546.4)
|Available teaching periods||Delivery mode||Location|
|View teaching periods|| On-Campus
|| UC - Canberra, Bruce
|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)
Learning outcomesOn 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 attributes1. 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 - 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
Prerequisites6540 Introduction to Statistics OR 5123 Business Statistics OR 1809 Data Analysis in Science
|Year||Location||Teaching period||Teaching start date||Delivery mode||Unit convener|
|2021||UC - Canberra, Bruce||Semester 2||02 August 2021||On-Campus||Dr Shuangzhe Liu|
|2022||UC - Canberra, Bruce||Semester 2||01 August 2022||On-Campus||Dr Shuangzhe Liu|
Pardoe, I. (2012) Applied Regression Modeling, 2nd edition, John Wiley & Sons (available at the UC library).
James, G., Witten, D., Hastie, T., Tibshirani, R. (2021) An Introduction to Statistical Learning, Springer (available online).
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 contribute the percentages shown in 5a.
Use of text matching software
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:
Unit mark = (0.20 * Quiz 1 mark) + (0.30 * Quiz 2 mark) + (0.50 * Final Project mark)
There will be a total workload of 150 hours which comprises of 24 hours of lectures, 11 hours of labs, 51 hours of review/prep time for quizzes with 4 hours attempt time, and 60 hours of review/prep time and analysis/write-up for the final project.
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
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 firstname.lastname@example.org 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).
- 6557 Regression Modelling G.