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The first all-inclusive introduction to modern statistical research methods in the natural resource sciences
he 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 strategies-the a posteriori model selection strategy and the a priori parsimonious model selection strategy using AIC and DIC-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-Plus(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-Plus(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.
he 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 strategies-the a posteriori model selection strategy and the a priori parsimonious model selection strategy using AIC and DIC-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-Plus(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-Plus(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.