Biostatistical Design and Analysis Using R
A Practical Guide
1. Auflage April 2010
574 Seiten, Softcover
Wiley & Sons Ltd
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.
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)
"Overall, this is an excellent reference for biologists and biostatisticians; it is also a very good supplemental textbook for a graduate-level biostatistics course." (The Quarterly Review of Biology, 2011)
