Table of contents

The course is connected to the following study programs

Teaching language

English, unless all participants are Norwegian.

Prerequisites

Basic skills in R programming and a working understanding of linear models, equivalent to the content covered in BIO603

Learning outcomes

After successful completion of the course, students will be able to:

  • apply advanced statistical methods to real-world problems in ecology, evolution, and environmental science
  • critically evaluate the strengths and limitations of different statistical approaches
  • build and interpret mixed models and generalised additive models
  • apply and interpret multivariate methods such as ordination and clustering
  • design and carry out reproducible statistical analyses using their own research data
  • communicate statistical reasoning clearly through code, visualisation, and oral presentation
  • prepare high-quality teaching materials on advanced statistical topics

In doing so, students will develop the following practical skills using R, RStudio, and Git:

  • implement and interpret linear mixed models (LMMs) and generalised linear mixed models (GLMMs)
  • apply model diagnostics and model selection in complex modelling situations
  • explore and visualise multivariate responses using ordination and clustering techniques
  • organise, document, and share fully reproducible analysis projects
  • peer review and give feedback on teaching materials and code

Contents

This course provides advanced training in statistical methods used in ecology, evolution, and environmental sciences, with a strong emphasis on reproducibility and communication. Students build on prior experience with R and statistical modelling to explore more complex techniques such as mixed models, generalised additive models (GAMs), and multivariate approaches including ordination. Participants develop and present teaching materials on a chosen topic and critically engage with each other’s work through peer review. The course emphasises independence, collaboration, and clear communication of quantitative reasoning. While some topics may be tailored to individual students’ research needs, core content is taught through structured lectures, exercises, and seminars in coordination with the second half of BIO415.

Teaching and learning methods

Teaching includes lectures, seminars, practical exercises, and peer-led presentations. Students develop and present teaching materials and review each other’s work.

Estimated student workload: 135 hours

Examination requirements

Approval of all mandatory assignments, as specified in Canvas.

Examinations

Portfolio, graded pass/fail.. More information will be given in Canvas at the start of the course.

Student evaluation

The course will be evaluated in accordance with the quality system for education, chapter 4.1.

Reduction of Credits

This course’s contents overlap with the following courses. A reduction of credits will occur if one of these courses is taken in addition:

Course Reduction of Credits
BIO415 – Study Design, Data Analysis, and Reproducible Science. 5
Last updated from FS (Felles studentsystem) Oct. 15, 2026 7:01:01 PM