Environmental and Ecological Statistics with R

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CRC Press, Aug 19, 2009 - Mathematics - 440 pages
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Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R connects applied statistics to the environmental and ecological fields. It follows the general approach to solving a statistical modeling problem, covering model specification, parameter estimation, and model evaluation. The author uses many examples to illustrate the statistical models and presents R implementations of the models.

The book first builds a foundation for conducting a simple data analysis task, such as exploratory data analysis and fitting linear regression models. It then focuses on statistical modeling, including linear and nonlinear models, classification and regression tree, and the generalized linear model. The text also discusses the use of simulation for model checking, provides tools for a critical assessment of the developed model, and explores multilevel regression models, which are a class of models that can have a broad impact in environmental and ecological data analysis.

Based on courses taught by the author at Duke University, this book focuses on statistical modeling and data analysis for environmental and ecological problems. By guiding readers through the processes of scientific problem solving and statistical model development, it eases the transition from scientific hypothesis to statistical model.

 

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Contents

Statistical Modeling
115
Advanced Statistical Modeling
315
Bibliography
409

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About the author (2009)

Song S. Qian is an associate research professor in the Nicholas School of the Environment at Duke University. Dr. Qian’s research consists of adaptive management strategies for watershed TMDL, GIS-assisted watershed modeling, water quality assessments, modeling marine mammal habitats, environmental sampling design, and more.

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