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  • Course Catalogue Number: 78085-01
  • Credit Points: 1 ECTS
  • Date: 24.03.26 + 25.03.26
  • Location: TBA
  • Program: TBA

Handouts

  • TBA

Overview

This site provides all the resources and information you need to make the most of the course. Over the next two days, we will explore key multivariate statistical methods, such as principal component analysis (PCA), cluster analysis and canonical correlation analysis.

Here you will find:

  • The detailed course schedule and session outlines

  • Lecture slides and supplementary reading materials

  • Hands-on exercises and datasets for practical learning

  • Helpful software installation guides and code examples (using R).

  • FAQs and tips for troubleshooting common issues.

Whether you are refreshing your knowledge or exploring multivariate techniques for the first time, this site will support you in deepening your understanding and successfully applying these methods to your own data.

Why Multivariate Statistics?

Ecological systems are complex, so describing them using just one response variable is rarely enough. For instance, rather than measuring the abundance of a single species, ecologists often consider several species simultaneously to gain a better understanding of a community's dynamics.

To explain patterns in the data, multiple explanatory variables are also included — such as temperature, soil type or human disturbance. This helps us to understand what might be driving changes in the system.

In these situations, multivariate methods are often preferred to running many separate univariate tests. This is because multivariate approaches save time and help conserve statistical power, which can be quickly lost when carrying out lots of individual tests. Furthermore, by analysing multiple variables together, we may discover patterns or relationships that would be overlooked if we examined each variable individually.

Textbooks

  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Free PDF
  • Legendre, P., & Legendre, L. (2012). Numerical Ecology. (Comprehensive for ecological data)
  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Free PDF
  • Legendre, P. & Legendre, L. (2012). Numerical Ecology (3rd ed.). Elsevier.
  • Borcard, D. et al. (2018). Numerical Ecology with R (2nd ed.). Springer.

Online Resources

Instructor

  • Jean-Claude Walser (jean-claude.walser[🙈]usys.ethz.ch)