Synopsis:
An Introduction to Basic Statistics for Biologists using R provides biologists with a uniquely easy-to-understand introduction to conducting statistical analysis in R. It uses a series of practical exercises based on easy-to-follow flow diagrams to show biologists exactly how to do a variety of key tasks.
These include: 1. Importing data sets and preparing them for analysis; 2. Creating graphs from biological data; 3. Assessing and transforming distributions; 4. Using statistical analysis to compare groups; 5. Using correlations and regression analysis.
Together, these represent the key tasks biologists need to be able to carry out to start analysing their data using R. This means it provides the perfect practical introduction for any biologist interested in learning how to use R to do statistics.
This book is also well-suited to act as a course text for those who wish to teach introductory statistics courses using R to undergraduate, masters or postgraduate students in any biological field. It not only provides a practical guide suitable for accompany the teaching of statistical theory, it also provides off-the-shelf exercises, complete with suitable data sets, that can be used as the basis for practical classes.
From the Back of the Book:
This workbook introduces biologists to the key practical skills they need to conduct basic data processing and statistical analysis in R. It is aimed both at students who are taking their first courses on statistics and at biologists who wish to teach themselves how to use R. As well as providing a quick-start guide to using R itself, this workbook provides a series of practical exercises based on easy-to-follow flow diagrams that show biologists exactly how to do a variety of key tasks in it. These include:
1. Preparing biological data for statistical analysis by importing them into R, error-checking them, subsetting them, joining data sets together and calculating summary statistics such as means, medians, standard deviations and confidence intervals.
2. Creating graphs from biological data, such as frequency distribution histograms, bar graph of count data, bar graphs with error bars, box plots and scatter plots.
3. Assessing and transforming the distribution of biological data, including using Shapiro-Wilk and other normality tests, and applying mathematical transformations to right-skewed and left-skewed data.
4. Using statistical analysis to compare data from different groups, including t-tests, Mann-Whitney U tests, ANOVAs, Kruskal-Wallis tests and chi-squared tests.
5. Using correlations and regressions to analyse biological data.
This is the first workbook in the Practical Statistics for Biologists Workbooks series. This series aims to fill the gap between learning about statistical theory and learning how to actually use statistics in a practical and biological meaningful way. The focus of this series is, therefore, on the development of practical skills, based on learning appropriate workflows, rather than teaching statistical theory.
The PSLS series of books uses Task-Oriented Learning (TOL) to teach the practical application of research skills to the life sciences. This involves demonstrating how these skills can be used in the specific circumstances in which they are likely to be required rather than concentrating on teaching theoretical frameworks or on teaching skills in a generic or abstract manner. By seeing how the similar processes are used to achieve a variety of different goals within a specific field, it becomes easier for the reader to identify the general rules behind the practical application of these processes and, therefore, to transfer them to novel situations they may encounter in the future.
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