Let us allow our response variable to be have non-normal errors
A significant limitation of linear models is that they cannot accommodate response variables that do not have a normal error distribution. Most biological data do not follow the assumption of normality.
In this workshop, you will learn how to use generalized linear models, which are important tools to overcome the distributional assumptions of linear models.
You will learn the major distributions used depending on the nature of the response variables, the concept of the link function, and how to verify assumptions of such models.
Note: The wiki for this workshop was converted to Bookdown in February 2021.
The wiki pages for this workshop will no longer be updated (Archive: EN, FR).
This workshop was originally developed by Cédric Frenette Dussault, Vincent Fugère, Thomas Lamy, and Zofia Taranu.
Since 2014, several QCBS members contributed to consistently and collaboratively develop and update this workshop, as part of the Learning and Development Award from the Québec Centre for Biodiversity Science. They were:
|2022 - 2021 - 2020||2019 - 2018 - 2017||2016 - 2015 - 2014|
|Pedro Henrique P. Braga||Azenor Bideault||Cédric Frenette Dussault|
|Katherine Hébert||Willian Vieira||Thomas Lamy|
|Alex Arkilanian||Pedro Henrique P. Braga||Zofia Taranu|
|Mathieu Vaillancourt||Marie Hélène Brice||Vincent Fugère|
|Laurie Maynard||Kevin Cazelles|
|Esteban Góngora||Marc-Olivier Beausoleil|