Experimental Design and Data Analysis for Biologists
Nonfiction, Science & Nature, Nature, Environment, Ecology, Mathematics, Science
| Author: | Gerry P. Quinn, Michael J. Keough | ISBN: | 9781107085664 |
| Publisher: | Cambridge University Press | Publication: | March 21, 2002 |
| Imprint: | Cambridge University Press | Language: | English |
| Author: | Gerry P. Quinn, Michael J. Keough |
| ISBN: | 9781107085664 |
| Publisher: | Cambridge University Press |
| Publication: | March 21, 2002 |
| Imprint: | Cambridge University Press |
| Language: | English |
An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data. The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models. Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models. Multivariate techniques, including classification and ordination, are then introduced. Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results. The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature. The book is supported by a website that provides all data sets, questions for each chapter and links to software.
An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data. The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models. Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models. Multivariate techniques, including classification and ordination, are then introduced. Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results. The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature. The book is supported by a website that provides all data sets, questions for each chapter and links to software.
