# TILM3540 Introduction to multivariate methods

This course has a moodle2 page

### Objectives:

A practical introduction to classical analysis of multivariate data  when the response variable is multivariate and continuous. Basic understanding of the mathematical background and use of the traditional multivariate   analysis tools.

### Outline:

1. Description of multivariate data: data matrix, mean vector, covariance matrix, other descriptive statistics, Mahalanobis distance, scatter plots
2. Multivariate normal distribution and its properties
3. Multivariate location tests: Hotelling’s T2, MANOVA and multivariate linear regression
4. The use of covariance matrix: Principal component analysis (PA), canonical correlation (CA)
5. Factor analysis (FA) and independent component analysis (ICA)
6. Discriminant analysis and cluster analysis
• Target group: Student is statistics (A) and doctoral students in other disciplines
• Prerequisites: A good knowledge of univariate statistical methods
• Teaching methods: Lectures (20 hours) and  practicals in pc class (10 hours)
• Lectures on Mondays at 10-14, Pub3, Publicum, 2.9.2013 – 16.9.201
• Lectures on Thursdays at 12-16, Pub2, Publicum, 5.9.2013 – 12.9.2013
• 10 hours of practicals, two per week, detailed schedule will be updated during August. There will be multiple groups depending on the number of participants.
• Evaluation: Active participation in practicals and an essay (other disciplines: passed/failed) or active participation and an exam (students in statistics, 0-5).

# TILM3540 Introduction to multivariate methods

This course has a moodle2 page

### Objectives:

A practical introduction to classical analysis of multivariate data  when the response variable is multivariate and continuous. Basic understanding of the mathematical background and use of the traditional multivariate   analysis tools.

### Outline:

1. Description of multivariate data: data matrix, mean vector, covariance matrix, other descriptive statistics, Mahalanobis distance, scatter plots
2. Multivariate normal distribution and its properties
3. Multivariate location tests: Hotelling’s T2, MANOVA and multivariate linear regression
4. The use of covariance matrix: Principal component analysis (PA), canonical correlation (CA)
5. Factor analysis (FA) and independent component analysis (ICA)
6. Discriminant analysis and cluster analysis
• Target group: Student is statistics (A) and doctoral students in other disciplines
• Prerequisites: A good knowledge of univariate statistical methods
• Teaching methods: Lectures (20 hours) and  practicals in pc class (10 hours)
• Lectures on Mondays at 10-14, Pub3, Publicum, 2.9.2013 – 16.9.201
• Lectures on Thursdays at 12-16, Pub2, Publicum, 5.9.2013 – 12.9.2013
• 10 hours of practicals, two per week, detailed schedule will be updated during August. There will be multiple groups depending on the number of participants.
• Evaluation: Active participation in practicals and an essay (other disciplines: passed/failed) or active participation and an exam (students in statistics, 0-5).