Dear all,
We are pleased to announce our upcoming online course “Generalised Linear Models in R”, taking place 25–29 May.
Generalized Linear Models (GLMs) provide a unified framework for analyzing many types of data, including Gaussian, binary, and count responses. In this course, participants will learn how to specify, fit, interpret, and visualize GLMs in R, and how common statistical approaches such as regression, ANOVA, and logistic regression fit within this framework.
The course is aimed at graduate students and researchers with basic statistical knowledge and some experience with R who want to analyze experimental or observational data using generalized linear regression models.
Sessions will run online from 14:00–20:00 (Berlin time) and combine lectures, discussions, and hands-on exercises.
Key learning outcomes include:
- Fitting and interpreting generalized linear models in R
- Choosing appropriate distributions and link functions
- Model selection and regression formula specification
- Model diagnostics and visualization of results
- Foundations for more advanced models (GLMMs, GAMs, Bayesian models)
More information and registration details are available on our website: https://www.physalia-courses.org/cou...ps/glm-in-r-1/
Best regards,
Carlo
--------------------
Carlo Pecoraro, Ph.D
Physalia-courses DIRECTOR
[email protected]
We are pleased to announce our upcoming online course “Generalised Linear Models in R”, taking place 25–29 May.
Generalized Linear Models (GLMs) provide a unified framework for analyzing many types of data, including Gaussian, binary, and count responses. In this course, participants will learn how to specify, fit, interpret, and visualize GLMs in R, and how common statistical approaches such as regression, ANOVA, and logistic regression fit within this framework.
The course is aimed at graduate students and researchers with basic statistical knowledge and some experience with R who want to analyze experimental or observational data using generalized linear regression models.
Sessions will run online from 14:00–20:00 (Berlin time) and combine lectures, discussions, and hands-on exercises.
Key learning outcomes include:
- Fitting and interpreting generalized linear models in R
- Choosing appropriate distributions and link functions
- Model selection and regression formula specification
- Model diagnostics and visualization of results
- Foundations for more advanced models (GLMMs, GAMs, Bayesian models)
More information and registration details are available on our website: https://www.physalia-courses.org/cou...ps/glm-in-r-1/
Best regards,
Carlo
--------------------
Carlo Pecoraro, Ph.D
Physalia-courses DIRECTOR
[email protected]