sta4ra regression analysis
REGRESSION ANALYSIS
STA4RA
2016
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.
SchoolSchool Engineering&Mathematical Sciences
Credit points15
Subject Co-ordinatorLuke Prendergast
Available to Study Abroad StudentsYes
Subject year levelYear Level 4 - UG/Hons/1st Yr PG
Exchange StudentsYes
Subject particulars
Subject rules
Prerequisites STA3LM and STA3AS
Co-requisitesN/A
Incompatible subjectsN/A
Equivalent subjects STA41RA
Special conditionsN/A
Graduate capabilities & intended learning outcomes
01. Demonstrate advanced theoretical and technical knowledge 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 advanced cognitive and technical skills to select and apply methods to critically analyse, evaluate and interpret output from various 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. Use advanced cognitive and technical skills to analyse, generate and transmit solutions to complex data sets suitable for linear models.
- Activities:
- Discussed and demonstrated in lectures. Analyses communicated 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)
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, well-developed 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)
Subject options
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Melbourne, 2016, Semester 1, Day
Overview
Online enrolmentNo
Maximum enrolment sizeN/A
Enrolment information
Subject Instance Co-ordinatorLuke 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* |
|---|---|---|---|
| one 2-hour examination. | 70 | 01, 02, 03, 05 | |
| three assignments | 30 | 01, 02, 03, 04, 05 |
Melbourne, 2016, Semester 2, Day
Overview
Online enrolmentNo
Maximum enrolment sizeN/A
Enrolment information
Subject Instance Co-ordinatorLuke Prendergast
Class requirements
Lecture/PracticalWeek: 31 - 43
One 2.0 hours lecture/practical per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
Assessments
| Assessment element | Comments | % | ILO* |
|---|---|---|---|
| one 2-hour examination. | 70 | 01, 02, 03, 05 | |
| three assignments | 30 | 01, 02, 03, 04, 05 |