ENV-513 / 4 credits

Teacher: Peter Hannes Markus

Language: English


Summary

Data required for ecosystem assessment is typically multidimensional. Multivariate statistical tools allow us to summarize and model multiple ecological parameters. This course provides a conceptual introduction and guidelines for the use of multivariate statistical tools using the R platform.

Content

Keywords

Multivariable analysis, statistics for ecological data sets, ordination, clustering

 

Learning Outcomes

By the end of the course, the student must be able to:

  • Explore multivariate datasets
  • Select appropriately the methods for multivariate data analysis
  • Explain the basic principles of various tools
  • Interpret obtained results
  • Apply methods in exercices and in a personal project

Transversal skills

  • Communicate effectively with professionals from other disciplines.

Teaching methods

Lectures and computer exercises. Personal projects.

Expected student activities

  • Active participation in lectures and excercises.
  • Application of methods to example and a personal dataset
  • Presentation of results (oral and written)

 

Assessment methods

  • active participation (20%)
  • oral presentation (30%)
  • written exam (50%)

Supervision

Office hours Yes
Assistants Yes
Forum Yes
Others moodle

In the programs

  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Multivariate statistics in R
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Multivariate statistics in R
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks
  • Exam form: Written (winter session)
  • Subject examined: Multivariate statistics in R
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 1 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10GRA330    
10-11    
11-12GRA330    
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     

Monday, 9h - 11h: Lecture GRA330

Monday, 11h - 12h: Exercise, TP GRA330