Statistics for Ecologists Using R and Excel: Data Collection, Exploration, Analysis and Presentation (Data in the Wild)

✍ Scribed by Mark Gardener

• Publisher: Pelagic Publishing
• Year: 2017
• Tongue: English
• Leaves: 418
• Edition: 2nd Fully Revised and Updated. ed.
• Category: Library

✦ Synopsis

This is a book about the scientific process and how you apply it to data in ecology. You will learn how to plan for data collection, how to assemble data, how to analyze data and finally how to present the results. The book uses Microsoft Excel and the powerful Open Source R program to carry out data handling as well as producing graphs.

Statistical approaches covered include: data exploration; tests for difference t-test and U-test; correlation Spearman s rank test and Pearson product-moment; association including Chi-squared tests and goodness of fit; multivariate testing using analysis of variance (ANOVA) and Kruskal Wallis test; and multiple regression.

Key skills taught in this book include: how to plan ecological projects; how to record and assemble your data; how to use R and Excel for data analysis and graphs; how to carry out a wide range of statistical analyses including analysis of variance and regression; how to create professional looking graphs; and how to present your results.

New in this edition: a completely revised chapter on graphics including graph types and their uses, Excel Chart Tools, R graphics commands and producing different chart types in Excel and in R; an expanded range of support material online, including; example data, exercises and additional notes & explanations; a new chapter on basic community statistics, biodiversity and similarity; chapter summaries and end-of-chapter exercises.

Praise for the first edition:

This book is a superb way in for all those looking at how to design investigations and collect data to support their findings. Sue Townsend, Biodiversity Learning Manager, Field Studies Council

[M]akes it easy for the reader to synthesise R and Excel and there is extra help and sample data available on the free companion webpage if needed. I recommended this text to the university library as well as to colleagues at my student workshops on R. Although I initially bought this book when I wanted to discover R I actually also learned new techniques for data manipulation and management in Excel Mark Edwards, EcoBlogging

A must for anyone getting to grips with data analysis using R and excel. Amazon 5-star review

It has been very easy to follow and will be perfect for anyone. Amazon 5-star review

A solid introduction to working with Excel and R. The writing is clear and informative, the book provides plenty of examples and figures so that each string of code in R or step in Excel is understood by the reader. Goodreads, 4-star review

✦ Table of Contents

Support files
1. Planning
1.1 The scientific method
1.2 Types of experiment/project
1.3 Getting data – using a spreadsheet
1.4 Hypothesis testing
1.5 Data types
1.6 Sampling effort
1.7 Tools of the trade
1.8 The R program
1.9 Excel
2. Data recording
2.1 Collecting data – who, what, where, when
2.2 How to arrange data
3. Beginning data exploration – using software tools
3.1 3.1 Beginning to use R
3.2 Manipulating data in a spreadsheet
3.3 Getting data from Excel into R
4. Exploring data – looking at numbers
4.1 Summarizing data
4.2 Distribution
4.3 A numerical value for the distribution
4.4 Statistical tests for normal distribution
4.5 4.5 Distribution type
4.6 Transforming data
4.7 When to stop collecting data? The running average
4.8 Statistical symbols
5. Exploring data – which test is right?
5.1 Types of project
5.2 Hypothesis testing
5.3 Choosing the correct test
6. Exploring data – using graphs
6.1 Introduction to data visualization
6.2 Exploratory graphs
6.3 Graphs to illustrate differences
6.4 Graphs to illustrate correlation and regression
6.5 6.5 Graphs to illustrate association
6.6 Graphs to illustrate similarity
6.7 Graphs – a summary
7. Tests for differences
7.1 Differences: t-test
7.2 7.2 Differences: U-test
7.3 Paired tests
8. Tests for linking data – correlations
8.1 Correlation: Spearman’s rank test
8.2 Pearson’s product moment
8.3 Correlation tests using Excel
8.4 Correlation tests using R
8.5 Curved linear correlation
9. Tests for linking data – associations
9.1 Association: chi-squared test
9.2 Goodness of fit test
9.3 Using R for chi-squared tests
9.4 Using Excel for chi-squared tests
10. Differences between more than two samples
10.1 Analysis of variance
10.2 Kruskal–Wallis test
11. Tests for linking several factors
11.1 Multiple regression
11.2 Curved-linear regression
11.3 Logistic regression
12. Community ecology
12.1 Diversity
12.2 Similarity
13. Reporting results
13.1 Presenting findings
13.2 Publishing
13.3 13.3 Reporting results of statistical analyses
13.4 Graphs
13.5 Writing papers
13.6 Plagiarism
13.7 References
13.8 Poster presentations
13.9 Giving a talk (PowerPoint)
14. Summary
Glossary
Appendices
Index