Learning outcomes

By the end of the course, students will be able to:


  • Understand and explain the basic principles of normal linear models, generalized linear models, and linear mixed models.
  • Apply multivariate analysis methods (PCA, correspondence analysis, multidimensional scaling, clustering, discriminant analysis) to analyze and interpret ecological or environmental data.
  • Critically choose the statistical tool appropriate for a given problem, verify its conditions of application, parameterize the model, and interpret the results.
  • Implement these tools in R, produce reproducible analyses, and clearly communicate results.
  • Develop autonomy in learning more advanced statistical techniques.


Goals

The course aims to:


  • Provide students with methodological foundations in linear statistical modeling and multivariate data analysis.
  • Develop practical skills to solve real statistical problems encountered in life and environmental sciences.
  • Emphasize critical thinking regarding the use of statistical tools.
  • Link theory to concrete examples drawn from ecology.


Content

Module 1 : Linear statistical modeling: Theoretical introduction to generalized and mixed models. 

Module 2 : Multivariate data analysis

Table of contents

Module 1: Linear statistical modeling


  • Chapter 1: Recap of normal linear models
  • Chapter 2: Generalized linear models (random part, linear predictor, link function; parameter estimation, inference)
  • Chapter 3: Linear mixed models (basics; LMMs as multivariate normal distributions; parameter estimation, inference)

Module 2: Multivariate data analysis


  • Chapter 1: Multivariate data and their visualization
  • Chapter 2: Ordination by Principal Component Analysis (PCA)
  • Chapter 3: Ordination of a contingency table: Correspondence Analysis (CA)
  • Chapter 4: Other ordination techniques: Multidimensional scaling
  • Chapter 5: Grouping objects: clustering
  • Chapter 6: Assigning objects to groups: discriminant analysis


Exercices

  • Practical sessions in a computer lab with R.
  • Solving concrete statistical problems related to ecology.
  • Tutorials to apply theoretical concepts.


Teaching methods

  1. Lectures, seminars, and exercise sessions in a computer lab.
  2. Use of real examples and ecological datasets.
  3. Intensive practice of R for model implementation.
  4. Encouragement of interactivity and active student participation.
  5. Focus on autonomy and critical thinking.

Assessment method

  • Open-book written exam including:
  • multiple-choice questions,
  • open-ended questions,
  • practical problem solving using R on a computer.
  • Each module contributes 10/20 to the final grade.
  • The exam takes place on Moodle in a computer lab on campus.
  • Unless otherwise specified in the exam instructions, only the UCLouvain computer may be used to access the exam and electronic documentation, and the use of artificial intelligence is prohibited.


Sources, references and any support material

  • Lecture slides and practical materials available on Moodle / WebCampus.
  • Datasets and R scripts provided.
  • Additional references and resources indicated on the platform.


Language of instruction

English
Training Study programme Block Credits Mandatory
Master in Biology of Organisms and Ecology Finalité approfondie 1 5 Yes
Master in Biology of Organisms and Ecology Finalité didactique 1 5 Yes