A computational approach to statistical arguments in ecology and evolution

Title:

Personal Author:

Estabrook, George F.

Publication Information:

Cambridge, ENK. ; New York : Cambridge University Press, 2011.

Physical Description:

viii, 257 p. : ill. ; 24 cm.

ISBN:

9781107004306

Subject Term:

Biometry -- Data processing

Ecology -- Statistical methods -- Data processing

Evolution (Biology) -- Statistical methods -- Data processing

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010297217	QH323.5 E88 2011	Open Access Book	Book	Searching... Unknown

Scientists need statistics. Increasingly this is accomplished using computational approaches. Freeing readers from the constraints, mysterious formulas and sophisticated mathematics of classical statistics, this book is ideal for researchers who want to take control of their own statistical arguments. It demonstrates how to use spreadsheet macros to calculate the probability distribution predicted for any statistic by any hypothesis. This enables readers to use anything that can be calculated (or observed) from their data as a test statistic and hypothesize any probabilistic mechanism that can generate data sets similar in structure to the one observed. A wide range of natural examples drawn from ecology, evolution, anthropology, palaeontology and related fields give valuable insights into the application of the described techniques, while complete example macros and useful procedures demonstrate the methods in action and provide starting points for readers to use or modify in their own research.

Author Notes

George F. Estabrook is a Professor of Botany in the Department of Ecology and Evolutionary Biology at the University of Michigan, Ann Arbor. He is interested in the application of mathematics and computing to biology, and has taught graduate courses on the subject for more than 30 years.

Acknowledgments	p. vii
1 Introduction	p. 1
1.1 About the book	p. 1
1.2 Basic principles	p. 10
1.3 Scientific argument	p. 14
2 Programming and statistical concepts	p. 20
2.1 Computer programming	p. 20
2.2 You start programming	p. 31
2.3 Completing the service berry example	p. 36
2.4 Sub CARPEL	p. 49
2.5 You practice	p. 53
3 Choosing a test statistic	p. 59
3.1 Significance of what	p. 59
3.2 Implement the program	p. 63
3.3 Sub PERIOD	p. 71
4 Random variables and distributions	p. 77
4.1 Random variables	p. 77
4.2 Distributions	p. 81
4.3 Arithmetic with random variables	p. 88
4.4 Expected value and variance	p. 94
5 More programming and statistical concepts	p. 101
5.1 Re-sampling data	p. 101
5.2 Procedures	p. 110
5.3 Testing procedures	p. 115
6 Parametric distributions	p. 122
6.1 Basic concepts	p. 122
6.2 Poisson distribution	p. 124
6.3 Normal distribution	p. 131
6.4 Negative binomial, Chi Square, and F distributions	p. 135
6.5 Percentiles	p. 137
7 Linear model	p. 141
7.1 Linear model	p. 141
7.2 Quantifying error	p. 142
7.3 Linear model in matrix form	p. 145
7.4 Using a linear model	p. 150
7.5 Hypotheses of random for a linear model	p. 155
7.6 Two-way analysis of variance	p. 160
8 Fitting distributions	p. 169
8.1 Estimation of parameters	p. 169
8.2 Goodness of fit	p. 176
9 Dependencies	p. 182
9.1 Interpreting mixtures	p. 182
9.2 Series of dependent random variables	p. 187
9.3 Analysis of covariance	p. 196
9.4 Confounding dependencies	p. 201
9.5 Sub SEXDIMO	p. 207
10 How to get away with peeking at data	p. 213
11 Contingency	p. 220
11.1 What is contingency	p. 220
11.2 ACTUS2	p. 223
11.3 Spreadsheet ACTUS	p. 241
11.4 Sub ACTUS	p. 244
References	p. 253
Index	p. 256

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Summary

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Author Notes

Table of Contents