Open AccessBook
Experimental Design and Data Analysis for Biologists
Gerry P. Quinn,Michael J. Keough +1 more
TLDR
An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data is as discussed by the authors, 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.Abstract:
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 softwareread more
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Generalized linear mixed models: a practical guide for ecology and evolution
Benjamin M. Bolker,Mollie Elizabeth Brooks,Connie J. Clark,Shane W. Geange,John R. Poulsen,M. Henry H. Stevens,Jada-Simone S. White +6 more
TL;DR: The use (and misuse) of GLMMs in ecology and evolution are reviewed, estimation and inference are discussed, and 'best-practice' data analysis procedures for scientists facing this challenge are summarized.
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A protocol for data exploration to avoid common statistical problems
TL;DR: A protocol for data exploration is provided; current tools to detect outliers, heterogeneity of variance, collinearity, dependence of observations, problems with interactions, double zeros in multivariate analysis, zero inflation in generalized linear modelling, and the correct type of relationships between dependent and independent variables are discussed; and advice on how to address these problems when they arise is provided.
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Regression Diagnostics: Identifying Influential Data and Sources of Collinearity
TL;DR: This chapter discusses Detecting Influential Observations and Outliers, a method for assessing Collinearity, and its applications in medicine and science.
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Effect size, confidence interval and statistical significance: a practical guide for biologists.
TL;DR: This article extensively discusses two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta‐analysis.
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Simple means to improve the interpretability of regression coefficients
TL;DR: In this article, the authors focus on parameter estimation (point estimates as well as confidence intervals) rather than on significance thresholds for linear regression models and propose a simple alternative to the more complicated calculation of standard errors from contrasts and main effects.
References
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Book
Statistical Power Analysis for the Behavioral Sciences
TL;DR: The concepts of power analysis are discussed in this paper, where Chi-square Tests for Goodness of Fit and Contingency Tables, t-Test for Means, and Sign Test are used.
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Controlling the false discovery rate: a practical and powerful approach to multiple testing
Yoav Benjamini,Yosef Hochberg +1 more
TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
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A power primer.
TL;DR: A convenient, although not comprehensive, presentation of required sample sizes is providedHere the sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests.
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.