株式会社極東書店トップ商品一覧Environmental and Ecological Statistics with R. 2nd edition.

商品詳細

Environmental and Ecological Statistics with R.

Environmental and Ecological Statistics with R. 2nd edition.

・ISBN 978-0-367-73675-0 paper GB£ 47.99

¥15,203.- (税込) (※)価格はご注文時の参考価格となります。
納品価格につきましては書籍の入荷時点で確定となります。
版元の原価改定、外国為替の変動等により異なる場合がございますので、予めご了承下さい。

お気に入り
電子版あり 大学・学術機関向け電子ブック(eBook)ISBN 9781315370262
著者・編者Qian, Song S.,
シリーズChapman & Hall/CRC Applied Environmental Statistics
出版社(Chapman & Hall/CRC, UK)
出版年月2020
ページ数560 pp.
言語ENG
ニュース番号<M25-1902>

解説

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.

The 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.