A Computational Approach to Statistical Arguments in Ecology and Evolution
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.
1102026373
A Computational Approach to Statistical Arguments in Ecology and Evolution
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.
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A Computational Approach to Statistical Arguments in Ecology and Evolution

A Computational Approach to Statistical Arguments in Ecology and Evolution

by George F. Estabrook
A Computational Approach to Statistical Arguments in Ecology and Evolution

A Computational Approach to Statistical Arguments in Ecology and Evolution

by George F. Estabrook

Overview

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.

Product Details

ISBN-13: 9781107004306
Publisher: Cambridge University Press
Publication date: 09/29/2011
Pages: 266
Product dimensions: 6.20(w) x 9.00(h) x 0.70(d)

About the Author

George 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.

Table of Contents

Acknowledgements; 1. Introduction; 2. Programming and statistical concepts; 3. Choosing a test statistic; 4. Random variables and distributions; 5. More programming and statistical concepts; 6. Parametric distributions; 7. Linear model; 8. Fitting distributions; 9. Dependencies; 10. How to get away with peeking at data; 11. Contingency; References; Index.
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