Experimental Design and Data Analysis for Biologists
eBook - PDF

Experimental Design and Data Analysis for Biologists

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Experimental Design and Data Analysis for Biologists

About this book

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.

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Yes, you can access Experimental Design and Data Analysis for Biologists by Gerry P. Quinn,Michael J. Keough in PDF and/or ePUB format, as well as other popular books in Biological Sciences & Ecology. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half-title
  3. Title
  4. Copyright
  5. Contents
  6. Preface
  7. Chapter 1 Introduction
  8. Chapter 2 Estimation
  9. Chapter 3 Hypothesis testing
  10. Chapter 4 Graphical exploration of data
  11. Chapter 5 Correlation and regression
  12. Chapter 6 Multiple and complex regression
  13. Chapter 7 Design and power analysis
  14. Chapter 8 Comparing groups or treatments – analysis of variance
  15. Chapter 9 Multifactor analysis of variance
  16. Chapter 10 Randomized blocks and simple repeated measures: unreplicated two factor designs
  17. Chapter 11 Split-plot and repeated measures designs: partly nested analyses of variance
  18. Chapter 12 Analyses of covariance
  19. Chapter 13 Generalized linear models and logistic regression
  20. Chapter 14 Analyzing frequencies
  21. Chapter 15 Introduction to multivariate analyses
  22. Chapter 16 Multivariate analysis of variance and discriminant analysis
  23. Chapter 17 Principal components and correspondence analysis
  24. Chapter 18 Multidimensional scaling and cluster analysis
  25. Chapter 19 Presentation of results
  26. References
  27. Index