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.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorLuke Prendergast

Available to Study Abroad StudentsYes

Subject year levelYear Level 5 - Masters

Exchange StudentsYes

Subject particulars

Subject rules

Prerequisites STA3LM or STA4LM and STA3SI or STA4SI

Co-requisitesN/A

Incompatible subjects STA4RA

Equivalent subjectsN/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.

Readings

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsIntroduction to Linear Regression AnalysisRecommendedRecommended 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)

Subject options

Select to view your study options…

Start date between: and    Key dates

Melbourne, 2017, Semester 1, Day

Overview

Online enrolmentNo

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorLuke Prendergast

Class requirements

Lecture/Practical Week: 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 elementComments% ILO*
Final exam (2 hours)70 01, 02, 04
Three assignments (approx. 600 words each)30 01, 02, 03, 04, 05