Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists, Online Version

The first all-inclusive introduction to modern statistical research methods in the natural resource sciences The use of Bayesian statistical analysis has become increasingly important to natural resource scientists as a practical tool for solving various research problems. However, many important contemporary methods of applied statistics, such as generalized linear modeling, mixed-effects modeling, and Bayesian statistical analysis and inference, remain relatively unknown among researchers and practitioners in this field. Through its inclusive, hands-on treatment of real-world examples, Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists successfully introduces the key concepts of statistical analysis and inference with an accessible, easy-to-follow approach. The book provides case studies illustrating common problems that exist in the natural resource sciences and presents the statistical knowledge and tools needed for a modern treatment of these issues. Subsequent chapter coverage features: An introduction to the fundamental concepts of Bayesian statistical analysis, including its historical background, conjugate solutions, Bayesian hypothesis testing and decision-making, and Markov Chain Monte Carlo solutions The relevant advantages of using Bayesian statistical analysis, rather than the traditional frequentist approach, to address research problems Two alternative strategiesaEURO"the a posteriori model selection strategy and the a priori parsimonious model selection strategy using AIC and DICaEURO"to model selection and inference The ideas of generalized linear modeling (GLM), focusing on the most popular GLM of logistic regression An introduction to mixed-effects modeling in S-PlusA(r) and R for analyzing natural resource data sets with varying error structures and dependencies Each statistical concept is accompanied by an illustration of its frequentist application in S-PlusA(r) or R as well as its Bayesian application in WinBUGS. Brief introductions to these software packages are also provided to help the reader fully understand the concepts of the statistical methods that are presented throughout the book. Assuming only a minimal background in introductory statistics, Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists is an ideal text for natural resource students studying statistical research methods at the upper-undergraduate or graduate level and also serves as a valuable problem-solving guide for natural resource scientists across a broad range of disciplines, including biology, wildlife management, forestry management, fisheries management, and the environmental sciences.

Table of Contents:
Preface. 1. Introduction. 1.1 Introduction. 1.2 Three Case Studies. 1.3 Overview of Some Solution Strategies. 1.4 Review: Principles of Project Management. 1.5 Applications. 1.6 S-PlusA? and R Orientation I: Introduction. 1.7 S-Plus and R Orientation II: Distributions. 1.8 S-Plus and R Orientation III: Estimation of Mean and Proportion, Sampling Error, and Confidence Intervals. 1.9 S-Plus and R Orientation IV: Linear Regression. 1.10 Summary. Problems. 2. Bayesian Statistical Analysis I: Introduction. 2.1 Introduction. 2.2 Three Methods for Fitting Models to Datasets. 2.3 The Bayesian Paradigm for Statistical Inference: Bayes Theorem. 2.4 Conjugate Priors. 2.5 Other Priors. 2.6 Summary. Problems. 3. Bayesian Statistical Inference II: Bayesian Hypothesis Testing and Decision theory. 3.1 Bayesian Hypothesis Testing: Bayes Factors. 3.2 Bayesian Decision Theory. 3.3 Preview: More Advanced Methods of Bayesian Statistical Analysisa??Markov Chain Monte Carlo (MCMC) Algorithms and WinBUGS Software. 3.4 Summary. Problems. 4. Bayesian Statistical Inference III: MCMC Algorithms and WinBUGS Software Applications. 4.1 Introduction. 4.2 Markov Chain Theory. 4.3 MCMC Algorithms. 4.4 WinBUGS Applications. 4.5 Summary. Problems. 5. Alternative Strategies for Model Selection and Inference Using Information-Theoretic Criteria. 5.1 Alternative Strategies for Model Selection and Influence: Descriptive and Predictive Model Selection. 5.2 Descriptive Model Selection: A Posteriori Exploratory Model Selection and Inference. 5.3 Predictive Model Selection: A Priori Parsimonious Model Selection and Inference Using Information-Theoretic Criteria. 5.4 Methods of Fit. 5.5 Evaluation of Fit: Goodness of Fit. 5.6 Model Averaging. 5.7 Applications: Frequentist Statistical Analysis in S-Plus and R; Bayesian Statistical Analysis in WinBUGS. 5.8 Summary. Problems. 6. An Introduction to Generalized Linear Models: Logistic Regression Models. 6.1 Introduction to Generalized Linear Models (GLMs). 6.2 GLM Design. 6.3 GLM Analysis. 6.4 Logistic Regression Analysis. 6.5 Other Generalized Linear Models (GLMs). 6.6 S-Plus or R and WinBUGS Applications. 6.7 Summary. Problems. 7. Introduction to Mixed-Effects Modeling. 7.1 Introduction. 7.2 Dependent Datasets. 7.3 Linear Mixed-Effects Modeling: Frequentist Statistical Analysis in S-Plus and R. 7.4 Nonlinear Mixed-Effects Modeling: Frequentist Statistical Analysis in S-Plus and R. 7.5 Conclusions: Frequentist Statistical Analysis in S-Plus and R. 7.6 Mixed-Effects Modeling: Bayesian Statistical Analysis in WinBUGS. 7.7 Summary. Problems. 8. Summary and Conclusions. 8.1 Summary of Solutions to Chapter 1 Case Studies. 8.2 Appropriate Application of Statistics in the Natural Resource Sciences. 8.3 Statistical Guidelines for Design of Sample Surveys and Experiments. 8.4 Two Strategies for Model Selection and Inference. 8.5 Contemporary Methods for Statistical Analysis I: Generalized Linear Modeling and Mixed-Effects Modeling. 8.6 Contemporary Methods in Statistical Analysis II: Bayesian Statistical Analysis Using MCMC Methods with WinBUGS Software. 8.7 Concluding Remarks: Effective Use of Statistical Analysis and Inference. 8.8 Summary. Appendix A. review of Linear regression and Multiple Linear regression Analysis. A.1 Introduction. A.2 Least-Square Fit: The Linear Regression Model. A.3 Linear Regression and Multiple Linear Regression Statistics. A.4 Stepwise Multiple Linear Regression Methods. A.5 Best-Subsets Selection Multiple Linear Regression. A.6 Goodness of Fit. Appendix B. Answers to Problems. References. Index.