# STAT242 Multivariate Methods

Tests of significance for multivariate data, Fisher discriminant function analysis, testing multivariate distances, cluster analysis, principal component analysis, factor analysis both exploratory and confirmatory, discrimination using logistic models when categorical predictors present, canonical correlation analysis, multidimensional scaling and other methods of ordination, correspondence analysis.

This is a paper in advanced statistical methods. Applications are widespread in the analysis of psychological, sociological and other types of behavioural data, including market research. Other areas of application include medicine, ecology, environmental science, geography and the biological sciences in general. Rather than concentrating on the mathematical aspects of the methods covered, the paper emphasises applications and data analysis through the use of the statistics package SPSS 22 and the AMOS 22 package with a use of R for some of the procedures.

Paper title Multivariate Methods STAT242 Statistics 0.1500 18 points Second Semester \$851.85 \$3,585.00
Prerequisite
STAT 110 or STAT 115 or BSNS 102 or BSNS 112
Restriction
STAT 342
Schedule C
Arts and Music, Science
Eligibility
Undergraduate students in any of the subjects listed above. It is also a paper for anyone majoring in Statistics. The paper is a very useful quantitative paper for graduate and research students in all areas.

These students, who enrol in the paper as part of their research programme, will find they are already meeting the techniques in their own reading and data analysis. These students can instead be registered for the equivalent paper STAT 342, with the only difference being that STAT 342 has an additional project worth 25% of the assessment, with the final exam counting for 75%.
Contact
twang@maths.otago.ac.nz or jharraway@maths.otago.ac.nz
Teaching staff
Associate Professor John Harraway
Paper Structure
Main topics:
• Multivariate analysis of variance
• Fisher Discriminant function analysis
• Logistic and multinomial regression for discrimination
• Cluster analysis
• Principal component analysis
• Exploratory factor analysis
• Confirmatory factor analysis using AMOS 22
• Discrimination with logistic models if some categorical predictors
• Canonical correlation analysis
• Measures of distance
• Methods of scaling and ordination
• Correspondence analysis
• Repeated measures
Teaching Arrangements
32 lectures and 12 tutorials
Textbooks
Multivariate Statistical Methods, a Primer, B.F.J. Manly (this book is on close reserve in the Science Library)

A course reader is available at the start of the second semester free of charge online (through the course resource page), and purchase of a hard copy through Uniprint is, therefore, optional.
Course outline
Critical thinking, Information literacy.
Learning Outcomes
Students who successfully complete the paper will develop an ability to explore and summarise large data sets.

## Timetable

### Second Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Other

#### Lecture

Stream Days Times Weeks
Attend
L1 Tuesday 13:00-13:50 28-34, 36-41
Thursday 13:00-13:50 28-34, 36-41
Friday 13:00-13:50 28-34, 36-41

#### Tutorial

Stream Days Times Weeks
Attend one stream from
T1 Monday 14:00-14:50 29-34, 36-40
T2 Wednesday 14:00-14:50 29-34, 36-40
T3 Thursday 14:00-14:50 29-34, 36-40

Tests of significance for multivariate data, Fisher discriminant function analysis, testing multivariate distances, cluster analysis, principal component analysis, factor analysis both exploratory and confirmatory, discrimination using logistic models when categorical predictors present, canonical correlation analysis, multidimensional scaling and other methods of ordination, correspondence analysis.

This is a paper in advanced statistical methods. Applications are widespread in the analysis of psychological, sociological and other types of behavioural data, including market research. Other areas of application include medicine, ecology, environmental science, geography and the biological sciences in general. Rather than concentrating on the mathematical aspects of the methods covered, the paper emphasises applications and data analysis through the use of the statistics package SPSS 22 and the AMOS 22 package with a use of R for some of the procedures.

Paper title Multivariate Methods STAT242 Statistics 0.1500 18 points Second Semester Tuition Fees for 2018 have not yet been set Tuition Fees for international students are elsewhere on this website.
Prerequisite
STAT 110 or STAT 115 or BSNS 102 or BSNS 112
Restriction
STAT 342
Schedule C
Arts and Music, Science
Eligibility
Undergraduate students in any of the subjects listed above. It is also a paper for anyone majoring in Statistics. The paper is a very useful quantitative paper for graduate and research students in all areas.

These students, who enrol in the paper as part of their research programme, will find they are already meeting the techniques in their own reading and data analysis. These students can instead be registered for the equivalent paper STAT 342, with the only difference being that STAT 342 has an additional project worth 25% of the assessment, with the final exam counting for 75%.
Contact
twang@maths.otago.ac.nz
Teaching staff
To be confirmed.
Teaching Arrangements
32 lectures and 12 tutorials.
Textbooks
Multivariate Statistical Methods, a Primer, B.F.J. Manly (this book is on close reserve in the Science Library)

A course reader is available at the start of the second semester free of charge online (through the course resource page), and purchase of a hard copy through Uniprint is, therefore, optional.
Paper Structure
Main topics:
• Multivariate analysis of variance
• Fisher Discriminant function analysis
• Logistic and multinomial regression for discrimination
• Cluster analysis
• Principal component analysis
• Exploratory factor analysis
• Confirmatory factor analysis using AMOS 22
• Discrimination with logistic models if some categorical predictors
• Canonical correlation analysis
• Measures of distance
• Methods of scaling and ordination
• Correspondence analysis
• Repeated measures
Course outline
View course outline for STAT 242
Critical thinking, Information literacy.
Learning Outcomes
Students who successfully complete the paper will develop an ability to explore and summarise large data sets.

## Timetable

### Second Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Other

#### Lecture

Stream Days Times Weeks
Attend
L1 Tuesday 13:00-13:50 28-34, 36-41
Thursday 13:00-13:50 28-34, 36-41
Friday 13:00-13:50 28-34, 36-41

#### Tutorial

Stream Days Times Weeks
Attend one stream from
T1 Monday 14:00-14:50 29-34, 36-40
T2 Wednesday 14:00-14:50 29-34, 36-40
T3 Thursday 14:00-14:50 29-34, 36-40