Modern Statistics for the Life Sciences

by Alan Grafen, Rosie Hails

Model formulae represent a powerful methodology for describing, discussing, understanding, and performing the component of statistical tests known as linear statistics. It was developed for professional statisticians in the 1960s and has become increasingly available as the use of computers has grown and software has advanced. Modern Statistics for Life Scientists puts this methodology firmly within the grasp of undergraduates for the first time. The authors assume a basic knowledge of statistics--up to and including one and two sample t-tests and their non-parametric equivalents. They provide the conceptual framework needed to understand what the method does--but without mathematical proofs--and introduce the ideas in a simple and steady progression with worked examples and exercises at every stage.

This innovative text offers students a single conceptual framework for a wide range of tests-including t-tests, oneway and multiway analysis of variance, linear and polynomial regressions, and analysis of covariance-that are usually introduced separately. More importantly, it gives students a language in which they can frame questions and communicate with the computers that perform the analyses. A companion website, www.oup.com/grafenhails, provides a wealth of worked exercises in the three statistical languages; Minitab, SAS, and SPSS. Appropriate for use in statistics courses at undergraduate and graduate levels, Modern Statistics for the Life Sciences is also a helpful resource for students in non-mathematics-based disciplines using statistics, such as geography, psychology, epidemiology, and ecology.

Genres: Textbooks

368 pages, Paperback

First published May 9, 2002

Reviews

[Arseny Khakhalin · Rating 5 out of 5]

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.

[Sam · Rating 3 out of 5]

This book is definitely not written for the likes of me. It has a few easy intro pages and then whack, its straight into general linear models. Given how much stats stuff I've forgotten over the years, I need more of a lead in (which I am working on) but some of the explanations did make more sense than others I've read recently but not quite as much as others. I think I'll be coming back to this one as time goes on and I start using stats again and I need to check specific points, which I suspect is more what this is written and designed for.