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£ GBP
The R 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 availability of R to couple the theory and practice of biostatistics into a single treatment so as to provide a resource 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.
1 Introduction to R
2 Data sets
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)
Bibliography
R index
Statistics index
Murray Logan is a lecturer and researcher in the School of Biological Sciences, Monash University, Melbourne, Australia. He teaches a range of zoological and ecological courses in addition to biostatistical and R courses to undergraduate and graduate students. He also provides research design and analysis advice to a range of university, government and private organizations.
