Atjaunināt sīkdatņu piekrišanu

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

4.30/5 (22 ratings by Goodreads)
  • Formāts: 406 pages
  • Sērija : Data in the Wild
  • Izdošanas datums: 16-Jan-2017
  • Izdevniecība: Pelagic Publishing
  • Valoda: eng
  • ISBN-13: 9781784271411
  • Formāts - EPUB+DRM
  • Cena: 43,82 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
  • Formāts: 406 pages
  • Sērija : Data in the Wild
  • Izdošanas datums: 16-Jan-2017
  • Izdevniecība: Pelagic Publishing
  • Valoda: eng
  • ISBN-13: 9781784271411

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

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 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

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. (This review refers to the first edition.) -- Mark Edwards * EcoBlogging * This book is a superb way in for all those looking at how to design investigations and collect data to support their findings. (This review refers to the first edition.) -- Sue Townsend, Biodiversity Learning Manager, Field Studies Council

Preface xi
1 Planning
1(18)
1.1 The scientific method
1(2)
1.2 Types of experiment/project
3(1)
1.3 Getting data -- using a spreadsheet
4(1)
1.4 Hypothesis testing
4(1)
1.5 Data types
5(3)
1.6 Sampling effort
8(5)
1.7 Tools of the trade
13(1)
1.8 The R program
13(2)
1.9 Excel
15(4)
2 Data recording
19(7)
2.1 Collecting data -- who, what, where, when
19(2)
2.2 How to arrange data
21(5)
3 Beginning data exploration -- using software tools
26(28)
3.1 Beginning to use R
26(8)
3.2 Manipulating data in a spreadsheet
34(16)
3.3 Getting data from Excel into R
50(4)
4 Exploring data -- looking at numbers
54(46)
4.1 Summarizing data
55(8)
4.2 Distribution
63(10)
4.3 A numerical value for the distribution
73(9)
4.4 Statistical tests for normal distribution
82(1)
4.5 Distribution type
83(5)
4.6 Transforming data
88(3)
4.7 When to stop collecting data? The running average
91(5)
4.8 Statistical symbols
96(4)
5 Exploring data -- which test is right?
100(7)
5.1 Types of project
100(1)
5.2 Hypothesis testing
101(1)
5.3 Choosing the correct test
102(5)
6 Exploring data -- using graphs
107(75)
6.1 Introduction to data visualization
107(10)
6.2 Exploratory graphs
117(6)
6.3 Graphs to illustrate differences
123(25)
6.4 Graphs to illustrate correlation and regression
148(14)
6.5 Graphs to illustrate association
162(14)
6.6 Graphs to illustrate similarity
176(2)
6.7 Graphs -- a summary
178(4)
7 Tests for differences
182(24)
7.1 Differences: t-test
182(9)
7.2 Differences: U-test
191(6)
7.3 Paired tests
197(9)
8 Tests for linking data -- correlations
206(24)
8.1 Correlation: Spearman's rank test
207(6)
8.2 Pearson's product moment
213(3)
8.3 Correlation tests using Excel
216(6)
8.4 Correlation tests using R
222(4)
8.5 Curved linear correlation
226(4)
9 Tests for linking data -- associations
230(17)
9.1 Association: chi-squared test
230(6)
9.2 Goodness of fit test
236(1)
9.3 Using R for chi-squared tests
237(4)
9.4 Using Excel for chi-squared tests
241(6)
10 Differences between more than two samples
247(38)
10.1 Analysis of variance
248(24)
10.2 Kruskal--Wallis test
272(13)
11 Tests for linking several factors
285(44)
11.1 Multiple regression
285(19)
11.2 Curved-linear regression
304(10)
11.3 Logistic regression
314(15)
12 Community ecology
329(30)
12.1 Diversity
329(12)
12.2 Similarity
341(18)
13 Reporting results
359(14)
13.1 Presenting findings
359(1)
13.2 Publishing
360(1)
13.3 Reporting results of statistical analyses
360(2)
13.4 Graphs
362(3)
13.5 Writing papers
365(2)
13.6 Plagiarism
367(1)
13.7 References
368(1)
13.8 Poster presentations
369(1)
13.9 Giving a talk (PowerPoint)
370(3)
14 Summary
373(2)
Glossary 375(6)
Appendices 381(13)
Index 394
Mark Gardener began his career as an optician but returned to science and trained as an ecologist. His research is in the area of pollination ecology. He has worked extensively in the UK as well as Australia and the United States. Currently he works as an associate lecturer for the Open University and also runs courses in data analysis for ecology and environmental science.