Dear all,
join us in May to unlock the power of Generalised Linear Models in R!
Dates: online, May 6th-10th
Course website: [ https://www.physalia-courses.org/courses-workshops/glm-in-r-1 ]( https://www.physalia-courses.org/courses-workshops/glm-in-r-1 )
Overview: Introductory statistics often present a series of isolated tests and procedures (e.g., t-test, ANOVA, ANCOVA, regression). However, many of these tests can be understood as specific instances of the generalized linear regression model (GLM). In this course, we will present GLMs as a unified, comprehensive, and adaptable framework for analyzing various data types. This includes Normal (Gaussian), binary, and discrete (count) response variables, incorporating both categorical (factors) and continuous predictors.
Who is this course for: The course is aimed at graduate students and researchers with basic statistical knowledge that want to learn how to analyze experimental and observation data with generalized linear regression models in R. Basic knowledge means that we assume knowledge about foundational statistical concepts (e.g. standard error, p-value, hypothesis testing) that are usually covered in a first introductory statistics class. Participants should also be familiar with Rstudio and have some experience in programming R code, including being able to import, manipulate (e.g. modify variables) and visualize data. If you have never used R before, it will be more useful for you to take an introductory R course first.
For the full list of our courses and workshops, please visit: [ https://www.physalia-courses.org/courses-workshops/ ]( https://www.physalia-courses.org/courses-workshops/ )[ ]( https://www.physalia-courses.org/courses-workshops/glm-in-r-1 )
Best regards,
Carlo
ChatGPTIntroductory statistics often present a series of isolated tests and procedures (e.g., t-test, ANOVA, ANCOVA, regression). However, many of these tests can be understood as specific instances of the generalized linear regression model (GLM). In this course, we will present GLMs as a unified, comprehensive, and adaptable framework for analyzing various data types. This includes Normal (Gaussian), binary, and discrete (count) response variables, incorporating both categorical (factors) and continuous predictors.
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Carlo Pecoraro, Ph.D
Physalia-courses DIRECTOR
info at physalia-courses.org
mobile: +49 17645230846