More flexibility in data analysis: GAMs in practice
In this course, you will learn how to apply Generalized Additive Models (GAMs) to model complex, non-linear relationships in your data. You will expand your knowledge of classical modeling approaches and learn how to use methods such as polynomial functions, splines, and semiparametric models effectively. Using R, you will learn how to implement these methods in practice, visualize the results, and interpret your findings.
Kurz informiert
Prof. Dr. Meinhard Kieser, Leitung Institut für Medizinische Biometrie Heidelberg
This course is aimed at professionals in medical research, biostatistics, biometry, data science, and related fields who want to understand and apply advanced methods, in particular Generalized Additive Models (GAMs).
Basic knowledge of mathematics and statistics (including the classical Linear Model), as well as familiarity with the R programming language, is required.
Registration form and, if applicable, proof of employment when claiming the reduced fee.