News:Generalized Linear Models (GLM) course in R
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3 months ago
carlopecoraro2 ★ 2.3k

Dear all,

We are excited to announce our upcoming course on Generalized Linear Models (GLM) as a Unified Framework for Data Analysis in R. This intensive and comprehensive course will equip you with the skills to analyze various types of data using GLMs, enabling you to unlock new possibilities in your research.

Course Details:

Title: Generalized Linear Models as a Unified Framework for Data Analysis in R

Dates: 19-23 June 2023

Format: Online (to foster international participation)

Remaining seats: 2

Course Overview: Introductory statistics often teach concepts as isolated tests and protocols, but many of these tests can be seen as special cases of the GLM. In this course, we will introduce GLMs as a unified, coherent, and easily extendable framework for analyzing different types of data, including Normal (Gaussian), binary, and discrete (count) response variables, with both categorical (factors) and continuous predictors.

Target Audience and Assumed Background: This course is designed for graduate students and researchers with a basic understanding of statistics who are eager to learn how to analyze experimental and observational data using generalized linear regression models in R. Participants should have foundational knowledge of statistical concepts (e.g., standard error, p-value, hypothesis testing) typically covered in introductory statistics courses. Familiarity with RStudio and experience in programming R code, including data import, manipulation, and visualization, are also required. If you have never used R before, we recommend taking an introductory R course prior to attending this course.

Learning Outcomes: By the end of this course, participants will:

• Be able to specify and fit generalized linear regression models in R, selecting appropriate distributions and link functions for their data.
• Interpret parameter estimates, including the correct interpretation of categorical predictors and calculate predictions from the fitted models.
• Understand principles of model selection and causal inference to choose the correct regression formula for their research questions.
• Visualize fitted models to assess assumptions, effectively communicate results, and deepen understanding.
• Gain foundational knowledge and explore further avenues for complex regression models (e.g., Generalized - Linear Mixed Models, Generalized Additive Models, Bayesian modeling) in the future.
• Don't miss this exceptional opportunity to enhance your data analysis skills and broaden your research horizons. Secure your spot today by registering for our Generalized Linear Models course!
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