The New Statistics with R: An Introduction for Biologists, 2nd Edition


The New Statistics with R: An Introduction for Biologists
by: Andy Hector (Author)
Publisher Finelybook 出版社:OUP Oxford; 2nd edition (17 Jun. 2021)
Language 语言:English
pages 页数:288 pages
ISBN-10 书号:0198798180
ISBN-13 书号:9780198798187


Book Description
Statistical methods are a key tool for all scientists working with data, but learning the basics continues to challenge successive generations of students. This accessible textbook provides an up-to-date introduction to the classical techniques and modern extensions of linear model
analysis-one of the most useful approaches for investigating scientific data in the life and environmental sciences. While some of the foundational analyses (e.g. t tests, regression, ANOVA) are as useful now as ever, best practice moves on and there are many new general developments that offer
great potential. The book emphasizes an estimation-based approach that takes account of recent criticisms of over-use of probability values and introduces the alternative approach that uses information criteria.

This new edition includes the latest advances in R and related software and has been thoroughly “road-tested” over the last decade to create a proven textbook that teaches linear and generalized linear model analysis to students of ecology, evolution, and environmental studies (including worked
analyses of data sets relevant to all three disciplines). While R is used throughout, the focus remains firmly on statistical analysis.

The New Statistics with R is suitable for senior undergraduate and graduate students, professional researchers, and practitioners in the fields of ecology, evolution and environmental studies.
The New Statistics with R:An Introduction for Biologists
Copyright
Dedication
Acknowledgements
Contents
1: Introduction
2:Motivation
3:Description
4:Reproducible Research
5:Estimation
6:Linear Models
7:Regression
8: Prediction
9: Testing
10:Intervals
11: Analysis of Variance
12:Factorial Designs
13:Analysis of Covariance
14: Linear Model Complexities
15:Generalized Linear Models
16:GLMs for Count Data
17:Binomial GLMs
18: GLMs for Binary Data
19: Conclusions
20: A Very Short Introduction to R
Index

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