📄 Documents & Materials
Four analysis methods are presented, each with a handbook and an interactive analysis tool:
- Principal component analysis
- Factor analysis
- Discriminant analysis
- Cluster analysis
In addition, an overview tool is available from which all analysis tools can also be launched.
| Document | Type | Pages | Action |
|---|---|---|---|
| 📄 Handbook Principal Component Analysis | Document | 8 | 📥 Download |
| 📄 Handbook Factor Analysis | Document | 6 | 📥 Download |
| 📄 Handbook Discriminant Analysis | Document | 6 | 📥 Download |
| 📄 Handbook Cluster Analysis | Document | 6 | 📥 Download |
🛠️ Tools & Calculators
Interactive tools for performing statistical analyses and calculations.
Overview
Multivariate methods analyse multiple variables simultaneously and uncover latent structures, natural groups or classification rules in datasets.
Principal Component Analysis (PCA)
This method reduces the number of variables by combining correlated variables into principal components. It is commonly used for data visualisation and cluster formation.
Factor Analysis
The aim is to identify latent variables that explain the correlations between observable variables.
Discriminant Analysis
Discriminant analysis is a multivariate statistical method used to distinguish between two or more groups described by multiple characteristics (also known as variables).
Cluster Analysis
Data points are divided into groups (clusters) so that members of a cluster are as similar as possible.