Provides an excellent introductory text for students on the principles and methods of statistical analysis in the life sciences, helping them choose and analyse statistical tests for their own problems and present their findings.
An understanding of statistical principles and methods is essential for any scientist but is particularly important for those in the life sciences. The field biologist faces very particular problems and challenges with statistics as "real life" situations such as collecting insects with a sweep net or counting seagulls on a cliff face can hardly be expected to be as reliable or controllable as a laboratory-based experiment. Acknowledging the peculiarities of field-based data and its interpretation, Practical Statistics for Field Biology introduces readers to the principles and methods of statistical analysis allowing them to understand research reports in journals, decide on the most appropriate statistical tests for their own problems and finally to analyse and present their findings. To enhance the usefulness of this book, the new edition incorporates the more advanced method of multivariate analysis, introducing the nature of multivariate problems and describing the techniques of principal components analysis, cluster analysis and discriminant analysis which are all applied to biological examples. An appendix detailing the statistical computing packages available has also been included. Practical Statistics for Field biology will be of interest to undergraduates studying ecology, biology, and earth and environmental sciences. In addition, it will be useful for postgraduates who are not familiar with the application of multivariate techniques and practising field biologists working in these areas.
Review of the First Edition
"In summary, Practical Statistics for Field Biology is an excellent introductory text for first and second year undergraduate courses in biology." Trends in Ecology & Evolution
What the lecturers said about the First Edition: "An excellent introductory guide to statistics." "Applicable examples in an understandable text."
Inhaltsverzeichnis
Measurement and Sampling Concepts.
Processing Data.
Presenting Data.
Measuring the Average.
Measuring Variability.
Probability.
Probability Distributions as Models of Dispersion.
The Normal Distribution.
Data Transformation.
How Good are Our Estimates?
The Basis of Statistical Testing.
Analysing Frequencies.
Measuring Correlations.
Regression Analysis.
Comparing Averages.
Analysis of Variance - ANOVA.
Multivariate Analysis.
Appendices.
Bibliography and Further Reading.
Index.
