1. Edition - April 2010
51.90 Euro
2010. 576 Pages, Softcover
ISBN-10: 1-4051-9008-6
ISBN-13: 978-1-4051-9008-4 - John Wiley & Sons








Detailed description
R -- the statistical and graphical environment is rapidly emerging as an important set of teaching and research tools for biologists. This book draws upon the popularity and free availability of R to couple the theory and practice of biostatistics into a single treatment, so as to provide a textbook for biologists learning statistics, R, or both. An abridged description of biostatistical principles and analysis sequence keys are combined together with worked examples of the practical use of R into a complete practical guide to designing and analyzing real biological research.

Topics covered include:
* simple hypothesis testing, graphing
* exploratory data analysis and graphical summaries
* regression (linear, multi and non-linear)
* simple and complex ANOVA and ANCOVA designs (including nested, factorial, blocking, spit-plot and repeated measures)
* frequency analysis and generalized linear models.

Linear mixed effects modeling is also incorporated extensively throughout as an alternative to traditional modeling techniques.

The book is accompanied by a companion website www.wiley.com/go/logan/r with an extensive set of resources comprising all R scripts and data sets used in the book, additional worked examples, the biology package, and other instructional materials and links.

From the contents
1 Introduction to R 1

2 Datasets

3 Introductory statistical principles

4 Sampling and experimental design with R

5 Graphical data presentation

6 Simple hypothesis testing - one and two population tests

7 Introduction to Linear models

8 Correlation and simple linear regression

9 Multiple and curvilinear regression

10 Single factor classification (ANOVA)

11 Nested ANOVA

12 Factorial ANOVA

13 Unreplicated factorial designs - randomized block and simple repeated measures

14 Partly nested designs: split plot and complex repeated measures

15 Analysis of covariance (ANCOVA)

16 Simple Frequency Analysis

17 Generalized linear models (GLM)