生物学者のための実験計画およびデータ分析<br>Experimental Design and Data Analysis for Biologists

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生物学者のための実験計画およびデータ分析
Experimental Design and Data Analysis for Biologists

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  • 製本 Paperback:紙装版/ペーパーバック版／ページ数 553 p.
  • 言語 ENG
  • 商品コード 9780521009768
  • DDC分類 570.2

基本説明

Begins with a revision of hypothesis testing methods, covering classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models. Topics include linear and logistic regression, simple and complex ANOVA models, and log-linear models.

Full Description

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.

Contents

1. Introduction; 2. Estimation; 3. Hypothesis testing; 4. Graphical exploration of data; 5. Correlation and regression; 6. Multiple regression and correlation; 7. Design and power analysis; 8. Comparing groups or treatments - analysis of variance; 9. Multifactor analysis of variance; 10. Randomized blocks and simple repeated measures: unreplicated two-factor designs; 11. Split plot and repeated measures designs: partly nested anovas; 12. Analysis of covariance; 13. Generalized linear models and logistic regression; 14. Analyzing frequencies; 15. Introduction to multivariate analyses; 16. Multivariate analysis of variance and discriminant analysis; 17. Principal components and correspondence analysis; 18. Multidimensional scaling and cluster analysis; 19. Presentation of results.