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Published online by Cambridge University Press: 05 June 2012
In the January 2003 issue of Trends in Ecology and Evolution, Andrew Read (Read 2003) reviewed two books on modern statistical methods (Crawley 2002, Grafen and Hails 2002). The title of his review is “Simplicity and serenity in advanced statistics” and begins as follows:
One of the great intellectual triumphs of the 20th century was the discovery of the generalized linear model (GLM). This provides a single elegant and very powerful framework in which 90% of data analysis can be done. Conceptual unification should make teaching much easier. But, at least in biology, the textbook writers have been slow to get rid of the historical baggage. These two books are a huge leap forward.
A generalized linear model involves a response variable (for example, the number of juvenile fish found in a survey) that is described by a specified probability distribution (for example, the gamma distribution, which we shall discuss in this chapter) in which the parameter (for example, the mean of the distribution) is a linear function of other variables (for example, temperature, time, location, and so on).
The books of Crawley, and Grafen and Hails, are indeed good ones, and worth having in one's library. They feature in this chapter for the following reason. On p. 15 (that is, still within the introductory chapter), Grafen and Hails refer to the t-distribution (citing an appendix of their book).
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