The New Statistics with R An Introduction for Biologists 1st Edition by Andy Hector ISBN 0198729057 9780198729051

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ISBN 10: 0198729057 
ISBN 13: 9780198729051
Author: Andy Hector

Statistical methods are a key tool for all scientists working with data, but learning the basic mathematical skills can be one of the most challenging components of a biologist’s training. This accessible book provides a contemporary introduction to the classical techniques and modern extensions of linear model analysis: one of the most useful approaches in the analysis of scientific data in the life and environmental sciences. It emphasizes an estimation-based approach that accounts for recent criticisms of the over-use of probability values, and introduces alternative approaches using information criteria. Statistics are introduced through worked analyses performed in R, the free open source programming language for statistics and graphics, which is rapidly becoming the standard software in many areas of science and technology. These analyses use real data sets from ecology, evolutionary biology and environmental science, and the data sets and R scripts are available as support material. The book’s structure and user friendly style stem from the author’s 20 years of experience teaching statistics to life and environmental scientists at both the undergraduate and graduate levels.

The New Statistics with R is suitable for senior undergraduate and graduate students, professional researchers, and practitioners in the fields of ecology, evolution, environmental studies, and computational biology.

The New Statistics with R An Introduction for Biologists 1st Table of contents:

1: Introduction
1.1 Introduction to the second edition
1.2 The aim of this book
1.3 Changes in the second edition
1.4 The R programming language for statistics and graphics
1.5 Scope
1.6 What is not covered
1.7 The approach
1.8 The new statistics?
1.9 Getting started
1.10 References
2: Motivation
2.1 A matter of life and death
2.2 Summary: Statistics
2.3 Summary: R
2.4 References
3: Description
3.1 Introduction
3.1.1 r packages
3.2 Darwin’s maize pollination data
3.2.1 know your data
3.2.2 summarizing and describing data
3.2.3 comparing groups
3.3 Summary: Statistics
3.4 Summary: R
3.5 References
4: Reproducible Research
4.1 The reproducibility crisis
4.1.1 R packages
4.2 R scripts
4.3 Analysis notebooks
4.4 R Markdown
4.5 Summary: Statistics
4.6 Summary: R
4.7 References
5: Estimation
5.1 Introduction
5.1.1 R Packages
5.2 Quick tests
5.3 Differences between groups
5.4 Standard deviations and standard errors
5.5 The normal distribution and the central limit theorem
5.6 Confidence intervals
5.7 Summary: Statistics
5.8 Summary: R
Appendix 5a: R code for Fig. 5.1
6: Linear Models
6.1 Introduction
6.1.1 R Packages
6.2 A linear-model analysis for comparing groups
6.3 Standard error of the difference
6.4 Confidence intervals
6.5 Answering Darwin’s question
6.6 Relevelling to get the other treatment mean and standard error
6.7 Assumption checking
6.7.1 Normality
6.7.2 Equal Variance
6.8 Summary: Statistics
6.9 Summary: R
6.10 Reference
Appendix 6a: R graphics
Appendix 6b: Robust linear models
Appendix 6c: Exercise
7: Regression
7.1 Introduction
7.1.1 R Packages
7.2 Linear regression
7.3 The Janka timber hardness data
7.4 Correlation
7.5 Linear regression in R
7.6 Assumptions
7.7 Summary: Statistics
7.8 Summary: R
7.9 Reference
Appendix 7a: R graphics
Appendix 7b: Least squares linear regression
8: Prediction
8.1 Introduction
8.1.1 R Packages
8.2 Predicting timber hardness from wood density
8.3 Confidence intervals and prediction intervals
8.4 Summary: Statistics
8.5 Summary: R
9: Testing
9.1 Significance testing: Time for t
9.1.1 R Packages
9.2 Student’s t-test: Darwin’s maize
9.3 Summary: Statistics
9.4 Summary: R
9.5 References
10: Intervals
10.1 Comparisons using estimates and intervals
10.1.1 R Packages
10.2 Estimation-based analysis
10.3 Descriptive statistics
10.3.1 Error Bars
10.3.2 Standard-deviation Error Bars
10.4 Inferential statistics
10.4.1 Standard Error Bars
10.4.2 Confidence Intervals
10.4.3 Confidence Intervals For Differences Between Means
10.4.4 Least Significant Differences
10.4.5 Multi-interval Plots
10.4.6 Prediction Intervals
10.5 Relating different types of interval and error bar
10.5.1 Interpreting Confidence Intervals
10.5.2 Point Estimates And Confidence Intervals For Research Synthesis And Meta-analysis
10.6 Summary: Statistics
10.7 Summary: R
10.8 References
11: Analysis of Variance
11.1 ANOVA tables
11.1.1 R packages
11.2 ANOVA tables: Darwin’s maize
11.3 Hypothesis testing: F-values
11.4 Two-way ANOVA
11.5 Summary
11.6 Reference
12: Factorial Designs
12.1 Introduction
12.1.1 R Packages
12.2 Factorial designs
12.3 Comparing three or more groups
12.4 Two-way ANOVA (no interaction)
12.5 Additive treatment effects
12.6 Interactions: Factorial ANOVA
12.6.1 Factorial Anova In R
12.7 Summary: Statistics
12.8 Summary: R
12.9 References
Appendix 12a: Code for Fig. 12.3
13: Analysis of Covariance
13.1 ANCOVA
13.1.1 R Packages
13.2 The agricultural pollution data
13.2.1 Warning: R Regenerates!
13.3 ANCOVA with water stress and low-level ozone
13.4 Interactions in ANCOVA
13.5 General linear models
13.6 Summary
13.7 References
14: Linear Model Complexities
14.1 Introduction
14.2 Analysis of variance for balanced designs
14.3 Analysis of variance with unbalanced designs
14.4 ANOVA tables versus coefficients: When F and t can disagree
14.5 Marginality of main effects and interactions
14.6 Summary
14.7 References
15: Generalized Linear Models
15.1 GLMs
15.2 The trouble with transformations
15.3 The Box–Cox power transform
15.4 Generalized Linear Models in R
15.5 Summary: Statistics
15.6 Summary: R
15.7 References
16: GLMs for Count Data
16.1 Introduction
16.2 GLMs for count data
16.3 Quasi-maximum likelihood
16.4 Summary
17: Binomial GLMs
17.1 Binomial counts and proportion data
17.2 The beetle data
17.3 GLM for binomial counts
17.4 Alternative link functions
17.5 Summary: Statistics
17.6 Summary: R
17.7 Reference
18: GLMs for Binary Data
18.1 Binary data
18.1.1 R Packages
18.2 The wells data set for the binary GLM example
18.3 Centering
18.4 Summary
18.5 References
19: Conclusions
19.1 Introduction
19.2 A binomial GLM analysis of the Challenger binary data
19.3 Recommendations
19.4 Where next?
19.5 Further reading
19.6 The R café
19.7 References
20: A Very Short Introduction to R
20.1 Installing R
20.2 Installing RStudio
20.3 R packages
20.4 The R language
20.4.1 Functions
20.4.2 Arguments
20.4.3 Objects
20.4.4 Dataframes

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Tags: Andy Hector, Statistics, Biologists