Statistical Methods for Field and Laboratory Studies in Behavioral Ecology

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.