Experimental Design and Data Analysis for Biologists mobi 网盘 高速 下载地址大全 免费

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.

1 Introduction 1.1 Scientific method 1.2 Experiments and other tests 1.3 Data, observations and variables 1.4 Probability 1.5 Probability distributions 2 Estimation 2.1 Samples and populations 2.2 Common parameters and statistics 2.3 Standard errors and confidence intervals for the mean 2.4 Methods for estimating parameters 2.5 Resampling methods for estimation 2.6 Bayesian inference – estimation 3 Hypothesis testing 3.1 Statistical hypothesis testing 3.2 Decision errors 3.3 Other testing methods 3.4 Multiple testing 3.5 Combining results from statistical tests 3.6 Critique of statistical hypothesis testing 3.7 Bayesian hypothesis testing 4 Graphical exploration of data 4.1 Exploratory data analysis 4.2 Analysis with graphs 4.3 Transforming data 4.4 Standardizations 4.5 Outliers 4.6 Censored and missing data 4.7 General issues and hints for analysis 5 Correlation and regression 5.1 Correlation analysis 5.2 Linear models 5.3 Linear regression analysis 5.4 Relationship between regression and correlation 5.5 Smoothing 5.6 Power of tests in correlation and regression 5.7 General issues and hints for analysis 6 Multiple and complex regression 6.1 Multiple linear regression analysis 6.2 Regression trees 6.3 Path analysis and structural equation modeling 6.4 Nonlinear models 6.5 Smoothing and response surfaces 6.6 General issues and hints for analysis 7 Design and power analysis 7.1 Sampling 7.2 Experimental design 7.3 Power analysis 7.4 General issues and hints for analysis 8 Comparing groups or treatments – analysis of variance 8.1 Single factor (one way) designs 8.2 Factor effects 8.3 Assumptions 8.4 ANOVA diagnostics 8.5 Robust ANOVA 8.6 Specific comparisons of means 8.7 Tests for trends 8.8 Testing equality of group variances 8.9 Power of single factor ANOVA 8.10 General issues and hints for analysis 9 Multifactor analysis of variance 9.1 Nested (hierarchical) designs 9.2 Factorial designs 9.3 Pooling in multifactor designs 9.4 Relationship between factorial and nested designs 9.5 General issues and hints for analysis 10 Randomized blocks and simple repeated measures: unreplicated two factor designs 10.1 Unreplicated two factor experimental designs 10.2 Analyzing RCB and RM designs 10.3 Interactions in RCB and RM models 10.4 Assumptions 10.5 Robust RCB and RM analyses 10.6 Specific comparisons 10.7 Efficiency of blocking (to block or not to block?) 10.8 Time as a blocking factor 10.9 Analysis of unbalanced RCB designs 10.10 Power of RCB or simple RM designs 10.11 More complex block designs 10.12 Generalized randomized block designs 10.13 RCB and RM designs and statistical software 10.14 General issues and hints for analysis 11 Split-plot and repeated measures designs: partly nested analyses of variance 11.1 Partly nested designs 11.2 Analyzing partly nested designs 11.3 Assumptions 11.4 Robust partly nested analyses 11.5 Specific comparisons 11.6 Analysis of unbalanced partly nested designs 11.7 Power for partly nested designs 11.8 More complex designs 11.9 Partly nested designs and statistical software 11.10 General issues and hints for analysi 12 Analyses of covariance 12.1 Single factor analysis of covariance (ANCOVA) 12.2 Assumptions of ANCOVA 12.3 Homogeneous slopes 12.4 Robust ANCOVA 12.5 Unequal sample sizes (unbalanced designs) 12.6 Specific comparisons of adjusted means 12.7 More complex designs 12.8 General issues and hints for analysis 13 Generalized linear models and logistic regression 13.1 Generalized linear models 13.2 Logistic regression 13.3 Poisson regression 13.4 Generalized additive models 13.5 Models for correlated data 13.6 General issues and hints for analysis 14 Analyzing frequencies 14.1 Single variable goodness-of-fit tests 14.2 Contingency tables 14.3 Log-linear models 14.4 General issues and hints for analysis 15 Introduction to multivariate analyses 15.1 Multivariate data 15.2 Distributions and associations 15.3 Linear combinations, eigenvectors and eigenvalues 15.4 Multivariate distance and dissimilarity measures 15.5 Comparing distance and/or dissimilarity matrices 15.6 Data standardization 15.7 Standardization, association and dissimilarity 15.8 Multivariate graphics 15.9 Screening multivariate data sets 16 Multivariate analysis of variance and discriminant analysis 16.1 Multivariate analysis of variance (MANOVA) 16.2 Discriminant function analysis 16.3 MANOVA vs discriminant function analysis 16.4 General issues and hints for analysis 17 Principal components and correspondence analysis 17.1 Principal components analysis 17.2 Factor analysis 17.3 Correspondence analysis 17.4 Canonical correlation analysis 17.5 Redundancy analysis 17.6 Canonical correspondence analysis 17.7 Constrained and partial “ordination” 17.8 General issues and hints for analysis 18 Multidimensional scaling and cluster analysis 18.1 Multidimensional scaling 18.2 Classification 18.3 Scaling (ordination) and clustering for biological data 18.4 General issues and hints for analysis 19 Presentation of results 19.1 Presentation of analyses 19.2 Layout of tables 19.3 Displaying summaries of the data 19.4 Error bars 19.5 Oral presentations 19.6 General issues and hints

Gerry Quinn is a Senior Lecturer in the School of Biological Sciences at Monash University, and Program Leader in the Cooperative Research Centre for Freshwater Ecology. He has taught experimental design and analysis courses for a number of years and has provided advice on the design and analysis of sampling and experimental programs in ecology and environmental monitoring to a wide range of university and government scientists. Gerry Quinn is a co-author of Monitoring Ecological Impacts: Concepts and Practice in Flowing Waters, Cambridge University Press, 2002. Michael Keough is a Reader in Zoology at the University of Melbourne. His research interests lie in marine ecology, environmental science, and conservation biology. He has extensive experience teaching experimental design and analysis courses at a number of universities. He has also provided advice on design and analysis for environmental monitoring to a wide range of environmental consultants, and state and federal governments in Australia. Michael Keough is a co-author of Monitoring Ecological Impacts: Concepts and Practice in Flowing Waters, Cambridge University Press, 2002.

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  来源：豆瓣 发布时间：2025-05-16 22:03:07