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    Modern Applied Statistics with S (Statistics and Computing) (Paperback) By (author) W.N. Venables, By (author) Brian D. Ripley

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    DescriptionA guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods. The emphasis is on presenting practical problems and full analyses of real data sets.


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    Title
    Modern Applied Statistics with S
    Authors and contributors
    By (author) W.N. Venables, By (author) Brian D. Ripley
    Physical properties
    Format: Paperback
    Number of pages: 510
    Width: 156 mm
    Height: 234 mm
    Thickness: 26 mm
    Weight: 711 g
    Language
    English
    ISBN
    ISBN 13: 9781441930088
    ISBN 10: 1441930086
    Classifications

    BIC E4L: COM
    Nielsen BookScan Product Class 3: S10.2
    B&T Book Type: NF
    LC subject heading:
    B&T Merchandise Category: TXT
    LC subject heading:
    BIC subject category V2: PBT
    DC22: 519.5
    LC subject heading:
    B&T General Subject: 710
    LC classification: QA
    Ingram Subject Code: MA
    BIC subject category V2: UFM
    Warengruppen-Systematik des deutschen Buchhandels: 16280
    BISAC V2.8: MAT029000
    Abridged Dewey: 519
    BIC subject category V2: UNC
    BISAC V2.8: COM077000
    DC22: 519.50285
    BISAC V2.8: MAT006000
    LC classification: QA1-939, QA274-274.9, QA273.A1-274.9
    LC subject heading:
    LC classification: QA276-280, QA71-90
    LC subject heading:
    LC classification: QA76.75-76.765
    Thema V1.0: PBT, UNC, UFM
    Edition
    4
    Edition statement
    4th ed. Softcover of orig. ed. 2002
    Illustrations note
    biography
    Publisher
    Springer-Verlag New York Inc.
    Imprint name
    Springer-Verlag New York Inc.
    Publication date
    01 December 2010
    Publication City/Country
    New York, NY
    Review quote
    "Modern Applied Statistics With S meets its goal of serving as an introduction to S for new users, as well as a reference and resource for those with more S experience." Journal of the American Statistical Association, December 2005
    Back cover copy
    S is a powerful environment for the statistical and graphical analysis of data. It provides the tools to implement many statistical ideas that have been made possible by the widespread availability of workstations having good graphics and computational capabilities. This book is a guide to using S environments to perform statistical analyses and provides both an introduction to the use of S and a course in modern statistical methods. Implementations of S are available commercially in S-PLUS(R) workstations and as the Open Source R for a wide range of computer systems. The aim of this book is to show how to use S as a powerful and graphical data analysis system. Readers are assumed to have a basic grounding in statistics, and so the book is intended for would-be users of S-PLUS or R and both students and researchers using statistics. Throughout, the emphasis is on presenting practical problems and full analyses of real data sets. Many of the methods discussed are state of the art approaches to topics such as linear, nonlinear and smooth regression models, tree-based methods, multivariate analysis, pattern recognition, survival analysis, time series and spatial statistics. Throughout modern techniques such as robust methods, non-parametric smoothing and bootstrapping are used where appropriate. This fourth edition is intended for users of S-PLUS 6.0 or R 1.5.0 or later. A substantial change from the third edition is updating for the current versions of S-PLUS and adding coverage of R. The introductory material has been rewritten to emphasis the import, export and manipulation of data. Increased computational power allows even more computer-intensive methods to be used, and methods such as GLMMs,
    Table of contents
    Introduction * Data Manipulation * The S Language * Graphics * Univariate Statistics * Linear Statistical Models * Generalized Linear Models * Non-linear and Smooth Regression * Tree-based Methods * Random and Mixed Effects * Exploratory Multivariate Analysis * Classification * Survival Analysis * Time Series Analysis * Spatial Statistics * Optimization