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. Download Citation on ResearchGate | On Nov 15, , Emily Silverman and others published Statistics for Ecologists Using R and Excel, 2nd Edition. Statistics for Ecologists using R and Excel. Data Collection, Exploration, Analysis and Presentation. Available from Pelagic Publishing. Get a 20% discount using.

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Editorial Reviews. Review. This book is a superb way in for all those looking at how to design How to Do Ecology: A Concise Handbook - Second Edition. Statistics For Ecologists Using R And Excel Data Collection Analysis And Presentation Data In The Wild [PDF] [EPUB] Introduction. Download PDF Statistics for Ecologists Using R and Excel: Data Collection, Exploration.

This chapter deals with tests of association, specifically variations on the chi squared test. These tests use data that is categorical. The chapter deals with the basic chi squared test as well as goodness of fit testing, where you match one set of categories with another.

How to carry out the tests in both Excel and R is covered. When you have two sets of categories you can examine for associations using the chi squared test. This section deals with the chi squared test in general with a worked example. When you have a 2 x 2 contingency table the Yates correction can be used and this is also described. A table of critical values for the chi squared statistic is provided.

The text also describes how to determine Pearson residuals, which are useful in presenting and interpreting results of chi squared tests of association.

If you have two sets of categorical data you can match them using a goodness of fit test.

The test is illustrated using some genetic data, a classic use of the goodness of fit test, where you compare the offspring of pea plants to the theoretical ratio expected under genetic theory. When you have more than two samples to compare you will need a more complicated analytical approach.

This chapter covers the two main methods of analysis:. ANOVA is used when you have normally distributed data.

Statistics for Ecologists Using R and Excel. Sample (Ch.6)

When the data are not normally distributed the Kruskal-Wallis test is used. Use of both Excel and R is illustrated in the text. R is able to carry out very complex analyses quite easily but Excel is limited. This section provides the reader with more skills in using R to carry out these more complicated analyses.

ANOVA allows you to compare more than two samples. This is described in the text as well as a method of post-hoc testing.

The use of R is described for the Kruskal-Wallis test. Excel is unable to carry out the test. When you have several variables to correlate you need a more complicated analytical tool. Multiple regression is the one you require; this uses the properties of the normal distribution. In this chapter multiple regression is described in detail. Curved linear regression is also described this was first mentioned in chapter 8. Multiple regression is introduced and illustrated using both Excel and R.

The use of R is extended by demonstrating how to carry out stepwise regression - this is a method of building the most appropriate regression for your data.

Logistic regression is another form of regression and is used when you have binary data e. Excel cannot easily carry out logistic regression but R can do this and this is illustrated using two different examples.

This chapter is concerned with the reporting of results. There are sections covering some of the conventions for reporting of statistical tests as well as notes about writing reports. Much of the chapter is devoted to the presentation of graphical results and there are sections detailing how to produce graphs using R and Excel.

This section shows a range of graph types and illustrates their uses. There are also sections covering use of graphics using R and Excel. This section shows the reader how to unlock the potential of R to produce a range of basic graphs:. This section covers the use of Excel for producing graphs. There are step-by-step guides to the following graphs:.

This section takes the production of graphs in R to a new level. There are instructions on a range of topics including:. This section covers a range of graphs using R and illustrated with worked examples. There are notes on customizing graphs including:. This section provides some notes about the use of posters as a means to disseminate your results. Data files to accompany the book view instructions.

Mark Gardener. Data Analysis. To see and download data files that accompany the book click here. Back to top. Statistics for Ecologists: Outline This is a book about the scientific process and how we apply it to data in ecology.

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.

Statistics for Ecologists Using R and Excel. Sample (Ch.6)

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 The book follows the theme of the scientific process and is split into four broad themes: Planning Data Recording Data Exploration Reporting Results The sections are rather uneven in size and focus on the analysis side somewhat.

The following outline covers each chapter of the book.

Chapter 1. Planning This chapter is about the preparation stages required before starting to collect data or carry out any analyses. Chapter 2. Data recording This chapter is brief yet important! Chapter 3. Beginning data exploration - using software tools This chapter is aimed at getting the reader more familiar with the software that they will use for data analysis, specifically Excel and R.

The section includes notes on various topics including: Getting Help Basic Maths Inputting Data Summary Statistics Saving Work By the end of this section the reader should be competent and confident with using R and be prepared for more detailed data analysis using the R interface.

Chapter 4. Exploring data - looking at numbers This chapter begins the actual process of data analysis.

Methods covered include: Averages Dispersion Confidence Intervals 4. Flag for inappropriate content.

Related titles. Beginning R: Community Ecology: Analytical Methods Using R and Excel. Sample Chapter Ch. Koch I. Analysis of Multivariate and High-Dimensional Data Jump to Page. Search inside document. Mark Gardener. Ayen Javiniar. Sunil Kamat.

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Read [pdf] statistics for ecologists using r and excel data collection exploration analysis and presentation data in the wild. Upcoming SlideShare. Like this presentation? Why not share! An annual analsite Rapids Fun stories for kids on the go. In this chapter multiple regression is described in detail.

Kevin Davis. How to use R for data analysis and graphs. You will learn how to plan for data collection, how to assemble data, how to analyse data and finally how to present the results.

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