Using Modern Linear Statistical Models in the Natural Sciences

10-14 février 2025

Stephanie GRAND

Dre Stéphanie Grand, UNIL

Dre Stéphanie Grand, UNIL

This course is designed for researchers wanting to improve their proficiency in the statistical analysis of complex datasets, in which there are many sources of variation potenially influencing the response variable. This is common in fields such as environmental science, soil science, natural resources sciences, ecology, biogeochemistry, hydrology, geomorphology, etc.

The objective of this course is to enable participants to apply linear statistical models successfully and rigorously in their research, by:
1) Providing an overview of available linear statistical models and their applications;
2) Pointing out practical problems encountered in using these models, and providing solutions;
3) Guiding the interpretation of model output and advanced statistical tests.

This course seeks to blend theory and application to provide a sound understanding of statistical tools appropriate for the analysis of real empirical data. Participants are encouraged to bring their own dataset to class. At the end of the course, participants should be able to:
(1) Select a class of statistical models which is appropriate for their objective
(2) Understand when a linear model is indicated and when it is not
(3) Evaluate model performance and choose a model structure that fits their data
(4) List the assumptions associated with their chosen model and check for violation
(5) Test a range of hypotheses about model parameters and their functions
(6) Understand what the p-value tells us and what it does not
(7) Use their statistical model output to support correct and robust interpretations of their data.

UNIL

1.5

12