Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists
About the author
Howard B. Stauffer, PhD, is Professor of Applied Statistics and former chairperson of the Mathematics Department at Humboldt State University. Dr. Stauffer has over thirtyfive years of experience in academia, government, and industry specializing in sampling and experimental design and analysis, in addition to the current methodologies in statistical analysis, such as generalized linear modeling, mixedeffects modeling, Bayesian statistical analysis, and capturerecapture analysis.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, mixedeffects modeling, and Bayesian statistical analysis and inference, remain relatively unknown among researchers and practitioners in this field. Through its inclusive, handson treatment of realworld 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, easytofollow 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 decisionmaking, 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 strategiesâ€”the a posteriori model selection strategy and the a priori parsimonious model selection strategy using AIC and DICâ€”to model selection and inference

The ideas of generalized linear modeling (GLM), focusing on the most popular GLM of logistic regression

An introduction to mixedeffects modeling in SPlusÂ® 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 SPlusÂ® 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 upperundergraduate or graduate level and also serves as a valuable problemsolving guide for natural resource scientists across a broad range of disciplines, including biology, wildlife management, forestry management, fisheries management, and the environmental sciences.
less418 pages; ISBN 9780470185070
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Title: Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists
Author: Howard B. Stauffer
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