MULTIVARIATE ANALYSIS

STA3MA

2014

Credit points: 15

Subject outline

Most statistical problems involve many variables and therefore multivariate data analysis has an enormous range of applications. After reviewing key aspects of statistical methods for one variable and multiple regression, students are introduced to common multivariate statistical methods: screening and describing multivariate data, principal component analysis, factor analysis, cluster analysis and logistic regression. En route, you will encounter the multivariate Normal distribution, learn about matrix algebra and how to use SPSS for multivariate data analysis.

Faculty: Faculty of Science, Tech & Engineering

Credit points: 15

Subject Co-ordinator: Graeme Byrne

Available to Study Abroad Students: Yes

Subject year level: Year Level 3 - UG

Exchange Students: Yes

Subject particulars

Subject rules

Prerequisites: MAT2LIN and either STA2FOR or STA3EXD

Co-requisites: N/A

Incompatible subjects: N/A

Equivalent subjects: There are no subjects at other campuses that could be substituted in whole or in substantial part of this unit.

Special conditions: N/A

Learning resources

Readings

Resource Type	Title	Resource Requirement	Author and Year	Publisher
Readings	Analysing multivariate data	Prescribed	Lattin, J.M., Carroll, J.D. and Green, P.E., 2003	THOMAS BROOKS/COLE, PACIFIC GROVE, CA.

Bendigo, 2014, Semester 2, Day

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information:

Subject Instance Co-ordinator: Graeme Byrne

Class requirements

LectureWeek: 31 - 43
Two 1.0 hours lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.

Computer LaboratoryWeek: 31 - 43
One 1.0 hours computer laboratory per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.

Assessments

Assessment element	Comments	%
one 1,000-word assignment		25
one 2,000-word assignment		50
verbal presentation to the class (20 minutes)		25