Environmental and Ecological Statistics with R

Song S. Qian

August 19, 2009 by Chapman and Hall/CRC
Textbook - 440 Pages - 165 B/W Illustrations
ISBN 9781420062069 - CAT# C6206
Series: Chapman & Hall/CRC Applied Environmental Statistics

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  • Describes each type of statistical model through examples
  • Explains how to conduct data analysis
  • Discusses simulation for model checking, an important aspect of model development and assessment
  • Presents multilevel regression models, such as multilevel ANOVA, multilevel linear regression, and generalized multilevel
  • Shows how the methods can be implemented using R
  • Offers the data sets and R scripts used in the book along with exercises and solutions on http://www.duke.edu/~song/eeswithr.htm


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.