Abstract
Abstract Chapter 7 introduces one of the most useful statistical frameworks for the modern life scientist: the generalized linear model (GLM). GLMs extend the linear model to an array of non-normally distributed data such as Poisson, negative binomial, binomial, and Gamma distributed data. These models dramatically improve the breadth of data that can be properly analysed without resorting to non-parametric statistics. Using the same RxP dataset, readers learn how to assess the error distribution of their data and evaluate competing models to achieve the best, most robust analysis possible. Diagnostic plots and assessing model fit is continually taught as is how to interpret the model output and calculate summary statistics. Plotting non-normal error distributions with ggplot2 is taught, as is using the predict() function.
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