- English
- ePUB (mobile friendly)
- Available on iOS & Android
About this book
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.
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Information
Table of contents
- Cover
- Title
- Copyright
- R quick reference card
- General key to statistical methods
- 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