Foundations of Linear and Generalized Linear Models, Alan Agresti
Автор: Nachtsheim;Neter;Kutner Название: Applied Linear Statistical Models with Student CD ISBN: 0071122214 ISBN-13(EAN): 9780071122214 Издательство: McGraw-Hill Рейтинг: Цена: 9265.00 р. Наличие на складе: Поставка под заказ.
Описание: "Applied Linear Statistical Models", 5e, is the long established leading authoritative text and reference on statistical modeling. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text includes brief introductory and review material, and then proceeds through regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Notes" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, and projects are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and where methods can be automated within software without loss of understanding, it is so done.
Описание: Offers a cohesive framework for statistical modeling. Emphasizing numerical and graphical methods, this work enables readers to understand the unifying structure that underpins GLMs. It discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, and longitudinal analysis.
Автор: Wood, Simon Название: Generalized Additive Models: An Introduction with R ISBN: 1584884746 ISBN-13(EAN): 9781584884743 Издательство: Taylor&Francis Рейтинг: Цена: 10717.00 р. Наличие на складе: Поставка под заказ.
Описание: An Introduction to Generalized Additive Models with R provides readers with a thorough understanding of the theory and practical applications of GAMs to enable informed use of these very flexible tools and other advanced related models. The author's approach is based on a framework of penalized regression splines, and he provides a gentle introduction through motivating chapters on linear and generalized linear models. The author uses the freely available R software throughout to explain the underlying theory and illustrate the practicalities of linear, generalized linear, and generalized additive models. The text is accompanied by a supporting Web site that contains R code and the datasets used in the book.
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