REGRESSION ANALYSIS
STA5RA
2017
Credit points: 15
Subject outline
The main objective of this unit is to provide an introduction to the theory of regression analysis. The topics for this unit include; multiple linear regression; classical estimation and testing; residual analysis; diagnostics; robust regression and modern dimension reduction. This unit considers both theoretical derivations and practical applications through the use of the freely available statistics software package R (see http://www.r-project.org/). Previous knowledge of R is not required. This subject is co-offered with STA4RA although assumes a deeper level of understanding of the subject content.
School: School Engineering&Mathematical Sciences
Credit points: 15
Subject Co-ordinator: Luke Prendergast
Available to Study Abroad Students: Yes
Subject year level: Year Level 5 - Masters
Exchange Students: Yes
Subject particulars
Subject rules
Prerequisites: STA3LM or STA4LM and STA3SI or STA4SI
Co-requisites: N/A
Incompatible subjects: STA4RA
Equivalent subjects: N/A
Special conditions: Non-LTU students (i.e. RMIT or Monash University students) are expected to have a pre-requisite intermediate knowledge of linear models including associated linear algebra results and also of theory related to statistical inference.
Learning resources
Readings
| Resource Type | Title | Resource Requirement | Author and Year | Publisher |
|---|---|---|---|---|
| Readings | Introduction to Linear Regression Analysis | Recommended | Recommended text: Montgomery, D. C, Peck, E. A. and Vining, G. | WILEY, 4TH EDITION (2006) |
Graduate capabilities & intended learning outcomes
01. Demonstrate specialised theoretical and technical skills in regression analysis.
- Activities:
- Discussed and demonstrated in lectures. Proofs derived by students in practice classes. Assignment questions, with feedback.
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Personal and Professional Skills(Autonomy and independence)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
02. Use specialised cognitive and technical skills to critically analyse, reflect on and synthesise complex information, problems, concepts and theories for regression methodologies.
- Activities:
- Discussed and demonstrated in lectures. Analyses interpreted and critiqued by students in practice classes. Assignment questions, with feedback.
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Personal and Professional Skills(Autonomy and independence)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
03. Apply established theories relevant regression analysis.
- Activities:
- Discussed and demonstrated in lectures. Appropriate theories matched to data sets by students in practice classes. Assignment questions, with feedback.
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Personal and Professional Skills(Autonomy and independence)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
04. Use advanced communication skills to transmit knowledge and ideas of statistics to others.
- Activities:
- Discussed and demonstrated in lectures. Students discuss results with class mates and lecturer in practice classes. Assignment questions, with feedback.
- Related graduate capabilities and elements:
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Personal and Professional Skills(Autonomy and independence)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
05. Demonstrate autonomy, expert judgement, adaptability and responsibility as a statistician.
- Activities:
- Discussed and demonstrated in lectures. Interpretations of results, including transparency of methods, carried out in class. Assignment questions, with feedback.
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Personal and Professional Skills(Autonomy and independence)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
Melbourne, 2017, Semester 1, Day
Overview
Online enrolment: No
Maximum enrolment size: N/A
Enrolment information:
Subject Instance Co-ordinator: Luke Prendergast
Class requirements
Lecture/PracticalWeek: 10 - 22
One 2.0 hours lecture/practical per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
Assessments
| Assessment element | Comments | % | ILO* |
|---|---|---|---|
| Final exam (2 hours) | 70 | 01, 02, 04 | |
| Three assignments (approx. 600 words each) | 30 | 01, 02, 03, 04, 05 |