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New Statistics with R: An Introduction for Biologists 2nd Revised edition [Kõva köide]

4.00/5 (8 hinnangut Goodreads-ist)
(Professor of Ecology, Department of Plant Sciences, University of Oxford, UK)
  • Formaat: Hardback, 278 pages, kõrgus x laius x paksus: 247x175x18 mm, kaal: 696 g, 60 colour line figures and 6 colour illustrations/screenshots
  • Ilmumisaeg: 17-Jun-2021
  • Kirjastus: Oxford University Press
  • ISBN-10: 0198798172
  • ISBN-13: 9780198798170
Teised raamatud teemal:
New Statistics with R: An Introduction for Biologists 2nd Revised editionSuurem pilt
  • Formaat: Hardback, 278 pages, kõrgus x laius x paksus: 247x175x18 mm, kaal: 696 g, 60 colour line figures and 6 colour illustrations/screenshots
  • Ilmumisaeg: 17-Jun-2021
  • Kirjastus: Oxford University Press
  • ISBN-10: 0198798172
  • ISBN-13: 9780198798170
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780198798170.html
Märksõnad:
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.

Arvustused

Review from previous edition The book is suitable for undergraduate and graduate students, researchers and practitioners in biological sciences. I found it refreshing and worthy of wide use. * Basil Jarvis, The Biologist * [ T]his book is of great interest ... it is important to evaluate its value as a teaching tool for R for biologists. ... [ T]he book's strength is that it takes an applied scientist through the necessary basic statistics, and shows step by step how to work with real data. The New Statistics with R is, furthermore, a great textbook for computer exercise sessions in any introductory statistical class (especially for the life sciences). With its help, one should be able to design a very attractive course for both applied and more theoretical students. * Krzysztof Bartoszek, Systematic Biology * ... overall the book gives useful, ecumenical, and reliable statistical advice. I would recommend it for courses that are trying to equip students who already know elementary statistics with the basic tools they need to understand and perform analyses of real, messy data. * Ben Bolker, Quarterly Review of Biology *

Chapter 1 Introduction
1(8)
1.1 Introduction to the second edition
1(1)
1.2 The aim of this book
2(1)
1.3 Changes in the second edition
3(1)
1.4 The R programming language for statistics and graphics
4(1)
1.5 Scope
4(1)
1.6 What is not covered
5(1)
1.7 The approach
5(1)
1.8 The new statistics?
6(1)
1.9 Getting started
6(1)
1.10 References
7(2)
Chapter 2 Motivation
9(6)
2.1 A matter of life and death
9(3)
2.2 Summary: Statistics
12(1)
2.3 Summary: R
13(1)
2.4 References
13(2)
Chapter 3 Description
15(14)
3.1 Introduction
15(1)
3.2 Darwin's maize pollination data
16(12)
3.3 Summary: Statistics
28(1)
3.4 Summary: R
28(1)
3.5 References
28(1)
Chapter 4 Reproducible Research
29(10)
4.1 The reproducibility crisis
29(1)
4.2 R scripts
30(2)
4.3 Analysis notebooks
32(1)
4.4 R Markdown
32(5)
4.5 Summary: Statistics
37(1)
4.6 Summary: R
37(1)
4.7 References
37(2)
Chapter 5 Estimation
39(12)
5.1 Introduction
39(1)
5.2 Quick tests
40(1)
5.3 Differences between groups
41(2)
5.4 Standard deviations and standard errors
43(2)
5.5 The normal distribution and the central limit theorem
45(3)
5.6 Confidence intervals
48(2)
5.7 Summary: Statistics
50(1)
5.8 Summary: R
50(1)
Appendix 5a R code for Fig. 5.1
50(1)
Chapter 6 Linear Models
51(20)
6.1 Introduction
51(1)
6.2 A linear-model analysis for comparing groups
52(5)
6.3 Standard error of the difference
57(1)
6.4 Confidence intervals
58(2)
6.5 Answering Darwin's question
60(2)
6.6 Relevelling to get the other treatment mean and standard error
62(1)
6.7 Assumption checking
63(3)
6.8 Summary: Statistics
66(1)
6.9 Summary: R
67(1)
6.10 Reference
67(4)
Appendix 6a R graphics
67(1)
Appendix 6b Robust linear models
68(1)
Appendix 6c Exercise
68(3)
Chapter 7 Regression
71(14)
7.1 Introduction
71(1)
7.2 Linear regression
72(1)
7.3 The Janka timber hardness data
73(2)
7.4 Correlation
75(1)
7.5 Linear regression in R
75(3)
7.6 Assumptions
78(4)
7.7 Summary: Statistics
82(1)
7.8 Summary: R
83(1)
7.9 Reference
83(2)
Appendix 7a R graphics
83(1)
Appendix 7b Least squares linear regression
84(1)
Chapter 8 Prediction
85(12)
8.1 Introduction
85(1)
8.2 Predicting timber hardness from wood density
85(5)
8.3 Confidence intervals and prediction intervals
90(4)
8.4 Summary: Statistics
94(1)
8.5 Summary: R
95(2)
Chapter 9 Testing
97(10)
9.1 Significance testing: Time for t
97(1)
9.2 Student's f-test: Darwin's maize
98(8)
9.3 Summary: Statistics
106(1)
9.4 Summary: R
106(1)
9.5 References
106(1)
Chapter 10 Intervals
107(20)
10.1 Comparisons using estimates and intervals
107(1)
10.2 Estimation-based analysis
108(1)
10.3 Descriptive statistics
109(4)
10.4 Inferential statistics
113(6)
10.5 Relating different types of interval and error bar
119(5)
10.6 Summary: Statistics
124(1)
10.7 Summary: R
125(1)
10.8 References
125(2)
Chapter 11 Analysis of Variance
127(12)
11.1 ANOVA tables
127(1)
11.2 ANOVA tables: Darwin's maize
128(4)
11.3 Hypothesis testing: F-values
132(3)
11.4 Two-way ANOVA
135(2)
11.5 Summary
137(1)
11.6 Reference
138(1)
Chapter 12 Factorial Designs
139(22)
12.1 Introduction
139(1)
12.2 Factorial designs
139(3)
12.3 Comparing three or more groups
142(3)
12.4 Two-way ANOVA (no interaction)
145(3)
12.5 Additive treatment effects
148(4)
12.6 Interactions: Factorial ANOVA
152(6)
12.7 Summary: Statistics
158(1)
12.8 Summary: R
159(1)
12.9 References
159(2)
Appendix 12a Code for Fig. 12.3
160(1)
Chapter 13 Analysis of Covariance
161(16)
13.1 ANCOVA
161(1)
13.2 The agricultural pollution data
162(3)
13.3 ANCOVA with water stress and low-level ozone
165(6)
13.4 Interactions in ANCOVA
171(1)
13.5 General linear models
172(3)
13.6 Summary
175(1)
13.7 References
176(1)
Chapter 14 Linear Model Complexities
177(18)
14.1 Introduction
177(1)
14.2 Analysis of variance for balanced designs
178(2)
14.3 Analysis of variance with unbalanced designs
180(4)
14.4 ANOVA tables versus coefficients: When F and t can disagree
184(2)
14.5 Marginality of main effects and interactions
186(6)
14.6 Summary
192(1)
14.7 References
192(3)
Chapter 15 Generalized Linear Models
195(14)
15.1 GLMs
195(1)
15.2 The trouble with transformations
196(4)
15.3 The Box-Cox power transform
200(3)
15.4 Generalized Linear Models in R
203(5)
15.5 Summary: Statistics
208(1)
15.6 Summary: R
208(1)
15.7 References
208(1)
Chapter 16 GLMs for Count Data
209(8)
16.1 Introduction
209(1)
16.2 GLMs for count data
210(3)
16.3 Quasi-maximum likelihood
213(2)
16.4 Summary
215(2)
Chapter 17 Binomial GLMs
217(12)
17.1 Binomial counts and proportion data
217(1)
17.2 The beetle data
218(2)
17.3 GLM for binomial counts
220(5)
17.4 Alternative link functions
225(3)
17.5 Summary: Statistics
228(1)
17.6 Summary: R
228(1)
17.7 Reference
228(1)
Chapter 18 GLMs for Binary Data
229(10)
18.1 Binary data
229(1)
18.2 The wells data set for the binary GLM example
230(6)
18.3 Centering
236(2)
18.4 Summary
238(1)
18.5 References
238(1)
Chapter 19 Conclusions
239(12)
19.1 Introduction
239(1)
19.2 A binomial GLM analysis of the Challenger binary data
239(7)
19.3 Recommendations
246(3)
19.4 Where next?
249(1)
19.5 Further reading
249(1)
19.6 The R cafe
249(1)
19.7 References
250(1)
Chapter 20 A Very Short Introduction to R
251(8)
20.1 Installing R
251(2)
20.2 Installing RStudio
253(1)
20.3 R packages
254(1)
20.4 The R language
254(5)
Index 259
Andy Hector is Professor of Ecology at the Department of Plant Sciences, Linacre College, University of Oxford, UK. He is Co-Director of the Plants for the Twenty-First Century Institute. He has convened and taught statistics on the Quantitative Methods for Biologists course for undergraduates. He is a community ecologist interested in biodiversity loss and its consequences for ecosystem functioning, stability and services and scientific PI of the Sabah Biodiversity Experiment. He has contributed to several publications on ecological analysis.