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E-raamat: Biostatistics with R: An Introductory Guide for Field Biologists

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(University of South Bohemia, Czech Republic), (University of South Bohemia, Czech Republic)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 30-Jul-2020
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781108572286
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Biostatistics with R: An Introductory Guide for Field BiologistsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 30-Jul-2020
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781108572286
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Biostatistics with R provides a straightforward introduction on how to analyse data from the wide field of biological research, including nature protection and global change monitoring. The book is centred around traditional statistical approaches, focusing on those prevailing in research publications. The authors cover t-tests, ANOVA and regression models, but also the advanced methods of generalised linear models and classification and regression trees. Chapters usually start with several useful case examples, describing the structure of typical datasets and proposing research-related questions. All chapters are supplemented by example datasets, step-by-step R code demonstrating analytical procedures and interpretation of results. The authors also provide examples of how to appropriately describe statistical procedures and results of analyses in research papers. This accessible textbook will serve a broad audience, from students, researchers or professionals looking to improve their everyday statistical practice, to lecturers of introductory undergraduate courses. Additional resources are provided on www.cambridge.org/biostatistics.

Arvustused

'We will never have a textbook of statistics for biologists that satisfies everybody. However, this book may come closest. It is based on many years of field research and the teaching of statistical methods by both authors. All useful classic and advanced statistical concepts and methods are explained and illustrated with data examples and R programming procedures. Besides traditional topics that are covered in the premier textbooks of biometry/biostatistics (e.g. R. R. Sokal & F. J. Rohlf, J. H. Zar), two extensive chapters on multivariate methods in classification and ordination add to the strength of this book. The text was originally published in Czech in 2016. The English edition has been substantially updated and two new chapters 'Survival Analysis' and 'Classification and Regression Trees' have been added. The book will be essential reading for undergraduate and graduate students, professional researchers, and informed managers of natural resources.' Marcel Rejmánek, Department of Evolution and Ecology, University of California, Davis, CA, USA

Muu info

A straightforward introduction to a wide range of statistical methods for field biologists, using thoroughly explained R code.
Preface xiii
Acknowledgements xvii
1 Basic Statistical Terms, Sample Statistics
1(18)
1.1 Cases, Variables and Data Types
1(2)
1.2 Population and Random Sample
3(1)
1.3 Sample Statistics
4(5)
1.4 Precision of Mean Estimate, Standard Error of Mean
9(1)
1.5 Graphical Summary of Individual Variables
10(1)
1.6 Random Variables, Distribution, Distribution Function, Density Distribution
10(3)
1.7 Example Data
13(1)
1.8 How to Proceed in R
13(4)
1.9 Reporting Analyses
17(1)
1.10 Recommended Reading
18(1)
2 Testing Hypotheses, Goodness-of-Fit Test
19(20)
2.1 Principles of Hypothesis Testing
19(2)
2.2 Possible Errors in Statistical Tests of Hypotheses
21(5)
2.3 Null Models with Parameters Estimated from the Data: Testing Hardy--Weinberg Equilibrium
26(1)
2.4 Sample Size
26(1)
2.5 Critical Values and Significance Level
27(2)
2.6 Too Good to Be True
29(1)
2.7 Bayesian Statistics: What is It?
30(2)
2.8 The Dark Side of Significance Testing
32(3)
2.9 Example Data
35(1)
2.10 How to Proceed in R
35(2)
2.11 Reporting Analyses
37(1)
2.12 Recommended Reading
37(2)
3 Contingency Tables
39(16)
3.1 Two-Way Contingency Tables
39(5)
3.2 Measures of Association Strength
44(2)
3.3 Multidimensional Contingency Tables
46(1)
3.4 Statistical and Causal Relationship
47(2)
3.5 Visualising Contingency Tables
49(1)
3.6 Example Data
50(1)
3.7 How to Proceed in R
50(4)
3.8 Reporting Analyses
54(1)
3.9 Recommended Reading
54(1)
4 Normal Distribution
55(10)
4.1 Main Properties of a Normal Distribution
55(1)
4.2 Skewness and Kurtosis
56(1)
4.3 Standardised Normal Distribution
57(1)
4.4 Verifying the Normality of a Data Distribution
58(2)
4.5 Example Data
60(1)
4.6 How to Proceed in R
60(3)
4.7 Reporting Analyses
63(1)
4.8 Recommended Reading
64(1)
5 Student's t Distribution
65(19)
5.1 Use Case Examples
65(1)
5.2 T Distribution and its Relation to the Normal Distribution
66(1)
5.3 Single Sample Test and Paired t Test
67(3)
5.4 One-Sided Tests
70(2)
5.5 Confidence Interval of the Mean
72(1)
5.6 Test Assumptions
73(1)
5.7 Reporting Data Variability and Mean Estimate Precision
74(3)
5.8 How Large Should a Sample Size Be?
77(2)
5.9 Example Data
79(1)
5.10 How to Proceed in R
79(3)
5.11 Reporting Analyses
82(1)
5.12 Recommended Reading
83(1)
6 Comparing Two Samples
84(8)
6.1 Use Case Examples
84(1)
6.2 Testing for Differences in Variance
85(2)
6.3 Comparing Means
87(1)
6.4 Example Data
88(1)
6.5 How to Proceed in R
88(3)
6.6 Reporting Analyses
91(1)
6.7 Recommended Reading
91(1)
7 Non-parametric Methods for Two Samples
92(12)
7.1 Mann-Whitney Test
93(2)
7.2 Wilcoxon Test for Paired Observations
95(2)
7.3 Using Rank-Based Tests
97(1)
7.4 Permutation Tests
97(2)
7.5 Example Data
99(1)
7.6 How to Proceed in R
99(3)
7.7 Reporting Analyses
102(1)
7.8 Recommended Reading
103(1)
8 One-Way Analysis of Variance (ANOVA) and Kruskal--Wallis Test
104(25)
8.1 Use Case Examples
104(1)
8.2 ANOVA: A Method for Comparing More Than Two Means
104(1)
8.3 Test Assumptions
105(1)
8.4 Sum of Squares Decomposition and the F Statistic
106(2)
8.5 ANOVA for Two Groups and the Two-Sample t Test
108(1)
8.6 Fixed and Random Effects
108(1)
8.7 F Test Power
109(1)
8.8 Violating ANOVA Assumptions
110(1)
8.9 Multiple Comparisons
111(4)
8.10 Non-parametric ANOVA: Kruskal--Wallis Test
115(1)
8.11 Example Data
116(1)
8.12 How to Proceed in R
117(10)
8.13 Reporting Analyses
127(1)
8.14 Recommended Reading
128(1)
9 Two-Way Analysis of Variance
129(22)
9.1 Use Case Examples
129(1)
9.2 Factorial Design
130(2)
9.3 Sum of Squares Decomposition and Test Statistics
132(2)
9.4 Two-Way ANOVA with and without Interactions
134(1)
9.5 Two-Way ANOVA with No Replicates
135(1)
9.6 Experimental Design
135(2)
9.7 Multiple Comparisons
137(1)
9.8 Non-parametric Methods
138(1)
9.9 Example Data
139(1)
9.10 How to Proceed in R
139(10)
9.11 Reporting Analyses
149(1)
9.12 Recommended Reading
150(1)
10 Data Transformations for Analysis of Variance
151(13)
10.1 Assumptions of ANOVA and their Possible Violations
151(2)
10.2 Log-transformation
153(3)
10.3 Arcsine Transformation
156(1)
10.4 Square-Root and Box-Cox Transformation
156(1)
10.5 Concluding Remarks
157(1)
10.6 Example Data
158(1)
10.7 How to Proceed in R
158(5)
10.8 Reporting Analyses
163(1)
10.9 Recommended Reading
163(1)
11 Hierarchical ANOVA, Split-Plot ANOVA, Repeated Measurements
164(19)
11.1 Hierarchical ANOVA
164(3)
11.2 Split-Plot ANOVA
167(2)
11.3 ANOVA for Repeated Measurements
169(2)
11.4 Example Data
171(1)
11.5 How to Proceed in R
171(10)
11.6 Reporting Analyses
181(1)
11.7 Recommended Reading
182(1)
12 Simple Linear Regression: Dependency Between Two Quantitative Variables
183(23)
12.1 Use Case Examples
183(1)
12.2 Regression and Correlation
184(1)
12.3 Simple Linear Regression
184(3)
12.4 Testing Hypotheses
187(3)
12.5 Confidence and Prediction Intervals
190(1)
12.6 Regression Diagnostics and Transforming Data in Regression
190(5)
12.7 Regression Through the Origin
195(2)
12.8 Predictor with Random Variation
197(1)
12.9 Linear Calibration
197(1)
12.10 Example Data
198(1)
12.11 How to Proceed in R
198(6)
12.12 Reporting Analyses
204(1)
12.13 Recommended Reading
205(1)
13 Correlation: Relationship Between Two Quantitative Variables
206(13)
13.1 Use Case Examples
206(1)
13.2 Correlation as a Dependency Statistic for Two Variables on an Equal Footing
206(3)
13.3 Test Power
209(3)
13.4 Non-parametric Methods
212(1)
13.5 Interpreting Correlations
212(1)
13.6 Statistical Dependency and Causality
213(3)
13.7 Example Data
216(1)
13.8 How to Proceed in R
216(2)
13.9 Reporting Analyses
218(1)
13.10 Recommended Reading
218(1)
14 Multiple Regression and General Linear Models
219(20)
14.1 Use Case Examples
219(1)
14.2 Dependency of a Response Variable on Multiple Predictors
219(4)
14.3 Partial Correlation
223(1)
14.4 General Linear Models and Analysis of Covariance
224(1)
14.5 Example Data
225(1)
14.6 How to Proceed in R
226(11)
14.7 Reporting Analyses
237(1)
14.8 Recommended Reading
238(1)
15 Generalised Linear Models
239(13)
15.1 Use Case Examples
239(1)
15.2 Properties of Generalised Linear Models
240(2)
15.3 Analysis of Deviance
242(1)
15.4 Overdispersion
243(1)
15.5 Log-linear Models
243(1)
15.6 Predictor Selection
244(1)
15.7 Example Data
245(1)
15.8 How to Proceed in R
246(4)
15.9 Reporting Analyses
250(1)
15.10 Recommended Reading
251(1)
16 Regression Models for Non-linear Relationships
252(9)
16.1 Use Case Examples
252(1)
16.2 Introduction
253(1)
16.3 Polynomial Regression
253(2)
16.4 Non-linear Regression
255(1)
16.5 Example Data
256(1)
16.6 How to Proceed in R
256(3)
16.7 Reporting Analyses
259(1)
16.8 Recommended Reading
260(1)
17 Structural Equation Models
261(13)
17.1 Use Case Examples
261(1)
17.2 SEMs and Path Analysis
261(4)
17.3 Example Data
265(1)
17.4 How to Proceed in R
265(7)
17.5 Reporting Analyses
272(1)
17.6 Recommended Reading
272(2)
18 Discrete Distributions and Spatial Point Patterns
274(16)
18.1 Use Case Examples
274(1)
18.2 Poisson Distribution
274(2)
18.3 Comparing the Variance with the Mean to Measure Spatial Distribution
276(3)
18.4 Spatial Pattern Analyses Based on the K-function
279(1)
18.5 Binomial Distribution
280(3)
18.6 Example Data
283(1)
18.7 How to Proceed in R
283(6)
18.8 Reporting Analyses
289(1)
18.9 Recommended Reading
289(1)
19 Survival Analysis
290(13)
19.1 Use Case Examples
290(1)
19.2 Survival Function and Hazard Rate
291(2)
19.3 Differences in Survival Among Groups
293(1)
19.4 Cox Proportional Hazard Model
293(2)
19.5 Example Data
295(1)
19.6 How to Proceed in R
295(7)
19.7 Reporting Analyses
302(1)
19.8 Recommended Reading
302(1)
20 Classification and Regression Trees
303(14)
20.1 Use Case Examples
303(1)
20.2 Introducing CART
304(2)
20.3 Pruning the Tree and Crossvalidation
306(1)
20.4 Competing and Surrogate Predictors
307(1)
20.5 Example Data
308(1)
20.6 How to Proceed in R
309(7)
20.7 Reporting Analyses
316(1)
20.8 Recommended Reading
316(1)
21 Classification
317(9)
21.1 Use Case Examples
317(1)
21.2 Aims and Properties of Classification
317(2)
21.3 Input Data
319(1)
21.4 Similarity and Distance
319(1)
21.5 Clustering Algorithms
320(1)
21.6 Displaying Results
320(1)
21.7 Divisive Methods
321(1)
21.8 Example Data
322(1)
21.9 How to Proceed in R
322(2)
21.10 Other Software
324(1)
21.11 Reporting Analyses
325(1)
21.12 Recommended Reading
325(1)
22 Ordination
326(17)
22.1 Use Case Examples
327(1)
22.2 Unconstrained Ordination Methods
327(3)
22.3 Constrained Ordination Methods
330(1)
22.4 Discriminant Analysis
331(2)
22.5 Example Data
333(1)
22.6 How to Proceed in R
333(7)
22.7 Alternative Software
340(1)
22.8 Reporting Analyses
341(1)
22.9 Recommended Reading
341(2)
Appendix A First Steps with R Software
343(20)
A.1 Starting and Ending R, Command Line, Organising Data
343(6)
A.2 Managing Your Data
349(2)
A.3 Data Types in R
351(6)
A.4 Importing Data into R
357(2)
A.5 Simple Graphics
359(1)
A.6 Frameworks for R
360(2)
A.7 Other Introductions to Work with R
362(1)
Index 363
Jan Lep is Professor of Ecology in the Department of Botany, Faculty of Science, University of South Bohemia, eské Budjovice, Czech Republic. His main research interests include plant functional ecology, particularly the mechanisms of species coexistence and stability, and ecological data analysis. He has taught many ecological and statistical courses and supervised more than 80 student theses, from undergraduate to PhD. Petr milauer is Associate Professor of Ecology in the Department of Ecosystem Biology, Faculty of Science, University of South Bohemia, eské Budjovice, Czech Republic. His main research interests are multivariate statistical analysis, modern regression methods and the role of arbuscular mycorrhizal symbiosis in the functioning of plant communities. He is co-author of multivariate analysis software Canoco 5, CANOCO for Windows 4.5 and TWINSPAN for Windows.
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