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
Print Length 页数: 288 pages
ISBN-10: 0198798180
ISBN-13: 9780198798187
Book Description
By finelybook
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
