Generalized Linear Modelling in Genstat for the analysis of non-Normal data

This webinar recording is an informative session on generalized linear models, generalized linear mixed models and hierarchical generalized linear models with Genstat. Generalized linear models (GLMs) are a powerful and popular tool in statistics for the analysis of non-Normal data, such as counts and proportions. These are key tools in a wide range of applications including agriculture, biology, medical research, quality control and insurance. However, a major limitation of GLMs is that they only cater for one source of random variation: the residual error. In practice, many data sets involve more than one source of random variability. For example, when data are collected from a designed experiment with blocking variables, or when repeated measurements are taken on the same experimental unit (or subject). Generalized linear mixed models (GLMMs) and hierarchical generalized linear models (HGLMs) extend GLMs to accommodate for such additional sources of random variation. Dr Vanessa Cave, Applied Statistician, demonstrates, with examples, how Genstat offers comprehensive and user-friendly methods for fitting GLMs, GLMMs, and HGLMs, making it easy for practitioners to use these advanced statistical techniques. https://vsni.co.uk/software/genstat