Statistical Methods for Field and Laboratory Studies in Behavioral Ecology

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""--Provided by publisher. Abstract: ""--Provided by publisher Content: Cover Half Title Title Page Copyright Page Table of Contents Preface Acknowledgments About the Authors Chapter 1: Statistical Foundations Some Probability Concepts Some Statistical Concepts Key Points for Chapter 1 Chapter 2: Binary Results: Single Samples and 2 Ã#x97 2 Tables General Ideas Single Proportion 2 Ã#x97 2 Tables Examples with R Code Single Proportion 2 Ã#x97 2 Tables Theoretical Aspects Single Proportion 2 Ã#x97 2 Tables Key Points for Chapter 2 Chapter 3: Continuous Variables General Ideas Examples with R Code Theoretical Aspects Key Points for Chapter 3. Chapter 4: The Linear Model: Continuous VariablesGeneral Ideas Examples with R Code Theoretical Aspects Key Points for Chapter 4 Chapter 5: The Linear Model: Discrete Regressor Variables General Ideas Examples with R Code More Than One Treatment: Multiple Factors Blocking Factors ANOVA and Permutation Tests Nested Factors Analysis of Covariance: Models with Both Discrete and Continuous Regressors Theoretical Aspects Multiple Groupings: One-Way ANOVA Key Points for Chapter 5 Chapter 6: The Linear Model: Random Effects and Mixed Models General Ideas. Simple Case: One Fixed and One Random EffectExamples with R Code More Complex Case: Multiple Fixed and Random Effects Theoretical Aspects Key Points for Chapter 6 Chapter 7: Polytomous Discrete Variables: R Ã#x97 C Contingency Tables General Ideas Independence of Two Discrete Variables Examples with R Code A Goodness-of-Fit Test A Special Goodness-of-Fit Test: Test for Random Allocation Theoretical Aspects Key Points for Chapter 7 Chapter 8: The Generalized Linear Model: Logistic and Poisson Regression General Ideas Binary Logistic Regression Examples with R Code. The Logit TransformationPoisson Regression Overdispersion Zero-Inflated Data and Poisson Regression Theoretical Aspects Logistic Regression Poisson Regression Overdispersed Poisson Zero-Inflated Poisson Key Points for Chapter 8 Chapter 9: Multivariate Analyses: Dimension Reduction, Clustering, and Discrimination General Ideas Dimension Reduction: Principal Components Clustering Discrimination MANOVA Examples with R Code Dimension Reduction: Principal Components Clustering Discrimination MANOVA Theoretical Aspects Principal Components Discrimination MANOVA. Key Points for Chapter 9Chapter 10: Bayesian and Frequentist Philosophies General Ideas Bayesâ#x80 #x99 Theorem: Not Controversial Conjugacy Beta, Binomial Poisson, Gamma Normal, Normal Monte Carlo Markov Chain (MCMC) Method Examples with R Code Exponential, Gamma Bayesian Regression Analysis Markov Chain Monte Carlo Theoretical Aspects Bayesian Regression Analysis A Slightly More Complicated Model An Afterword about Bayesian Methods Key Points for Chapter 10 Chapter 11: Decision and Game Theory General Ideas Examples with R Code Discrete Choices, Discrete States.