Practical Statistics for Field BiologyProvides 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 peculiarites of field-based data and its interpretation, this book provides a superb introduction to statistical analysis helping students relate to their particular and often diverse data with confidence and ease. 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 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. It will be extremely useful to undergraduates studying ecology, biology, and earth and environmental sciences and of interest to postgraduates who are not familiar with the application of multiavirate techniques and practising field biologists working in these areas. |
Contents
ANALYSING FRE UENCIES | 4-13 |
ANOVA | 4-17 |
HOW GOOD ARE OUR ESTIMATES? | 9-11 |
MULTIVARIATE ANALYSIS | 9-18 |
MEASURING CORRELATIONS | 9-57 |
REGRESSION ANALYSIS | 9-69 |
APPENDICES | 16 |
BIBLIOGRAPHY AND FURTHER READING | 36 |
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Common terms and phrases
analysis ANOVA Appendix applied attica autumnalis binomial probability distribution biologists calculated value class interval clusters column computation confidence interval contingency table correlation coefficient count data critical value degrees of freedom equal estimate Example expected frequencies female formula frequency class frequency distribution G-test histogram individuals instar interval scales large samples larger length male mass mathematical meadow measurements median multiplying negative binomial distribution non-parametric normal curve normally distributed normally distributed population Null Hypothesis number of degrees number of observations observed frequencies obtained one-tailed test orchids otolith Poisson distribution population mean possible outcomes principal components proportion pupae quadrats random dispersion randomly ranks ratio regression line sample data sample is drawn sample mean sample of count sample statistics sampling error sampling units scattergram scientific calculator significant skewed small samples species standard deviation standard error Step sum of squares symmetrical techniques test statistic total number transformation two-tailed test variables whole number z-score