Last edition Elsevier Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R, Second Edition, connects applied statistics to the environmental and ecological fields. Using examples from published works in the ecological and environmental literature, the book explains the approach to solving a statistical problem, covering model specification, parameter estimation, and model evaluation. It includes many examples to illustrate the statistical methods and presents R code for their implementation. The emphasis is on model interpretation and assessment, and using several core examples throughout the book, the author illustrates the iterative nature of statistical inference.
Last Edition
ISBN 13: 9781498728720
Imprint: Productivity Press Inc
Language: English
Authors: Song S. Qian
Pub Date: 10/2016
Pages: 536
Illus: Illustrated
Weight: 910.000 grams
Size: 130 x 234 mm
Product Type: Hardcover
| List Price |
| grn 3344 |
| $ 113,34 |
| to order |
- • he book starts with a description of commonly used statistical assumptions and exploratory data analysis tools for the verification of these assumptions.
- • It then focuses on the process of building suitable statistical models, including linear and nonlinear models, classification and regression trees, generalized linear models, and multilevel models.
- • It also discusses the use of simulation for model checking, and provides tools for a critical assessment of the developed models.
- • The second edition also includes a complete critique of a threshold model.
- • Environmental and Ecological Statistics with R, Second Edition focuses on statistical modeling and data analysis for environmental and ecological problems.
- • By guiding readers through the process of scientific problem solving and statistical model development, it eases the transition from scientific hypothesis to statistical model.
- Song S. Qian, PhD, is an assistant professor in the Department of Environmental Sciences at the University of Toledo, Ohio, USA
- I Basic Concepts
- Introduction A Crash Course on R Statistical Assumptions Statistical Inference
- II Statistical Modeling
- Linear Models Nonlinear Models Classi cation and Regression Tree Generalized Linear Model
- III Advanced Statistical Modeling
- Simulation for Model Checking and Statistical Inference Multilevel Regression Using Simulation for Evaluating Models Based on Statistical Signicance Testing
- Bibliography
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