Alan Grafen

This book contains the best explanation of general linear models I could find. Moreover, it is somewhat unusual in that it actually starts with the general linear model, before anything else: before probabilities, before t-tests. It start with attempts to explain variance of data, and proceeds from there. Which turns out to be a very productive approach to the material.

Its main drawback, at least for me, is that it doesn't use R, but sticks to old packages like Minitab and SSPS. This is a bit annoying.

Also it has some quirks; for example every now and then it attempts a so called "geometric explanation of the analysis of variance", in which mean sums of squares are illustrated by Pythagorean theorems in a multidimensional space. I am still to find a person who could understand these explanations, and to whom they would actually help. But they are not long, nor are they frequent, and as long as you ignore them, and as long as you find translating the code to R yourself, it's a truly beautiful book.… (mais)

This collection of essays celebrates the contributions of Richard Dawkins. Most of the contributors focus on his selfish gene theory, though there is a short grouping at the back of the book that discuss his ability to generate religious controversy. One section deals solely with his ability to write in a comprehensible, interesting manner. There are a couple of clunkers among the essays, chiefly the one by Matt Ridley, but most of them are intersting and informative. The book is not as interesting to read as books by its subject, however.… (mais)