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E-grāmata: Statistics for Ecologists Using R and Excel: Data Collection, Exploration, Analysis and Presentation

4.24/5 (33 ratings by Goodreads)
  • Formāts: 324 pages
  • Sērija : Data in the Wild
  • Izdošanas datums: 01-Jan-2012
  • Izdevniecība: Pelagic Publishing
  • Valoda: eng
  • ISBN-13: 9781907807275
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Statistics for Ecologists Using R and Excel: Data Collection, Exploration, Analysis and PresentationPalielināt
  • Formāts: 324 pages
  • Sērija : Data in the Wild
  • Izdošanas datums: 01-Jan-2012
  • Izdevniecība: Pelagic Publishing
  • Valoda: eng
  • ISBN-13: 9781907807275
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This is a book about the scientific process and how it is applied to data in ecology. We will learn how to plan for data collection, how to assemble data, how to analyse data and finally how to present the results. The book uses Microsoft Excel and the powerful Open Source R program.



This is a book about the scientific process and how we apply it to data in ecology. We will learn how to plan for data collection, how to assemble data, how to analyse 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.

Who this book is for

Students of ecology and environmental science will find this book aimed at them although many other scientists will find the text useful as the principles and data analysis are the same in many disciplines. No prior knowledge is assumed and the reader can develop their skills to degree level and beyond.

What you will learn from this book

How to plan ecological projects How to record and assemble your data How to use Excel for data analysis and graphs How to use R for data analysis and graphs How to carry out a wide range of statistical analyses How to create professional looking graphs How to present your results

Recenzijas

The text that I have found most helpful in getting back to using R has been Mark Gardener's Statistics for Ecologists Using R and Excel. This excellent little book leads the reader nicely through the basics. Starting with how to down load R and getting data into the programme through exploratory statistics and into basic analysis with a section on reporting results which includes visualising data. It also makes 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 *

Introduction viii
1 Planning
1(22)
1.1 The scientific method
1(2)
1.2 Types of experiment/project
3(1)
1.3 Getting data -- using a spreadsheet
3(1)
1.4 Hypothesis testing
4(1)
1.5 Data types
4(3)
1.6 Sampling effort
7(5)
1.7 Tools of the trade
12(1)
1.8 The R program
13(6)
1.9 Excel
19(4)
2 Data recording
23(6)
2.1 Collecting data -- who, what, where, when
23(2)
2.2 How to arrange data
25(4)
3 Beginning data exploration -- using software tools
29(28)
3.1 Beginning to use R
29(8)
3.2 Manipulating data in a spreadsheet
37(18)
3.3 Getting data from Excel into R
55(2)
4 Exploring data -- looking at numbers
57(34)
4.1 Summarising data
58(3)
4.2 Distribution
61(6)
4.3 A numerical value for the distribution
67(8)
4.4 Statistical tests for normal distribution
75(1)
4.5 Distribution type
76(5)
4.6 Transforming data
81(3)
4.7 When to stop collecting data? The running average
84(4)
4.8 Statistical symbols
88(3)
5 Exploring data -- which test is right?
91(4)
5.1 Hypothesis testing
91(1)
5.2 Choosing the correct test
92(3)
6 Exploring data -- using graphs
95(8)
6.1 Exploratory graphs
95(3)
6.2 Graphs to illustrate differences
98(1)
6.3 Graphs to illustrate links
99(3)
6.4 Graphs -- a summary
102(1)
7 Tests for differences
103(20)
7.1 Differences: t-test
103(9)
7.2 Differences: U-test
112(5)
7.3 Paired tests
117(6)
8 Tests for linking data -- correlations
123(24)
8.1 Correlation: Spearman's rank test
123(7)
8.2 Pearson's product moment
130(4)
8.3 Correlation tests using Excel
134(5)
8.4 Correlation tests using R
139(4)
8.5 Curved linear correlation
143(4)
9 Tests for linking data -- associations
147(14)
9.1 Association: Chi-squared test
147(6)
9.2 Goodness of fit test
153(1)
9.3 Using R for Chi-squared tests
154(3)
9.4 Using Excel for Chi-squared tests
157(4)
10 Differences between more than two samples
161(34)
10.1 Using R for more complex statistical analyses
161(3)
10.2 Analysis of variance
164(22)
10.3 Kruskal--Wallis test
186(9)
11 Tests for linking several factors
195(44)
11.1 Multiple regression
195(17)
11.2 Curved-linear regression
212(27)
12 Reporting results
239(76)
12.1 Presenting findings
239(1)
12.2 Publishing
239(1)
12.3 Reporting results of statistical analyses
240(1)
12.4 Graphs
241(32)
12.5 More about graphs in R
273(23)
12.6 Worked example graph data in R
296(13)
12.7 Graphs: a summary
309(1)
12.8 Writing papers
310(1)
12.9 Plagiarism
311(1)
12.10 References
312(1)
12.11 Poster presentations
313(1)
12.12 Giving a talk (PowerPoint)
314(1)
13 Summary
315(2)
Glossary 317(5)
Index 322
Mark Gardener (www.gardenersown.co.uk) is an ecologist, lecturer, and writer working in the UK. His primary area of research was in pollination ecology and he has worked in the UK and around the word (principally Australia and the United States). Since his doctorate he has worked in many areas of ecology, often as a teacher and supervisor. He believes that ecological data, especially community data, is the most complicated and ill-behaved and is consequently the most fun to work with. He was introduced to R by a like-minded pedant whilst working in Australia during his doctorate. Learning R was not only fun but opened up a new avenue, making the study of community ecology a whole lot easier. He is currently self-employed and runs courses in ecology, data analysis, and R for a variety of organizations. Mark lives in rural Devon with his wife Christine, a biochemist who consequently has little need of statistics.
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