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Randomization, Bootstrap and Monte Carlo Methods in Biology: A Practitioner's Guide with R and Python

Maurice Parker

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Published in Randomization Bootstrap And Monte Carlo Methods In Biology (Chapman Hall/CRC Texts In Statistical Science)

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Randomization, bootstrap and Monte Carlo methods are powerful statistical tools that can be used to analyze data and draw s from research. They are particularly useful in biology, where data is often complex and difficult to analyze.

Randomization, Bootstrap and Monte Carlo Methods in Biology (Chapman & Hall/CRC Texts in Statistical Science)

by Carole Mortimer

5 out of 5

Language	:	English
File size	:	4973 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	357 pages

This book provides a practical guide to randomization, bootstrap and Monte Carlo methods in biology. It is written for practitioners who need to use these methods to analyze data and draw s from their research. The book covers a wide range of topics, including:

* The basics of randomization, bootstrap and Monte Carlo methods * How to choose the right method for your data * How to interpret the results of your analysis * How to avoid common pitfalls

The book is written in a clear and concise style and is accompanied by a wealth of examples and exercises. It is the perfect resource for anyone who wants to learn more about randomization, bootstrap and Monte Carlo methods and how to use them in their own research.

Chapter 1: The Basics of Randomization, Bootstrap and Monte Carlo Methods

This chapter provides an overview of the basics of randomization, bootstrap and Monte Carlo methods. It covers the following topics:

* What is randomization? * What is bootstrapping? * What is Monte Carlo simulation? * When should you use randomization, bootstrapping, or Monte Carlo simulation?

Chapter 2: How to Choose the Right Method for Your Data

This chapter provides guidance on how to choose the right method for your data. It covers the following topics:

* What factors should you consider when choosing a method? * How do you compare the different methods? * What are the advantages and disadvantages of each method?

Chapter 3: How to Interpret the Results of Your Analysis

This chapter provides guidance on how to interpret the results of your analysis. It covers the following topics:

* What do the results of your analysis mean? * How can you draw s from your results? * How can you avoid making incorrect s?

Chapter 4: How to Avoid Common Pitfalls

This chapter provides guidance on how to avoid common pitfalls when using randomization, bootstrap and Monte Carlo methods. It covers the following topics:

* What are the common pitfalls? * How can you avoid these pitfalls? * What are some best practices for using randomization, bootstrap and Monte Carlo methods?

Appendix

The appendix contains a number of useful resources, including:

* A glossary of terms * A table of symbols * A list of references * A set of exercises

References

[1] Efron, B., & Tibshirani, R. J. (1993). An to the bootstrap. Chapman & Hall/CRC. [2] Good, P. (2005). Resampling methods: A practical guide to data analysis. Wiley. [3] Manly, B. F. J. (2006). Randomization, bootstrap and Monte Carlo methods in biology. Chapman & Hall/CRC.

Randomization, Bootstrap and Monte Carlo Methods in Biology (Chapman & Hall/CRC Texts in Statistical Science)

by Carole Mortimer

5 out of 5