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Image of - Modern Applied Statistics with S-Plus | 2nd Edition (Statistics and Computing)
Modern Applied Statistics with S-Plus | 2nd Edition (Statistics and Computing)

Modern Applied Statistics with S-Plus | 2nd Edition (Statistics and Computing)

by Venables

S-PLUS is a powerful environment for the statistical and graphical analysis of data. It provides the tools to implement many statistical ideas which have been made possible by the widespread availability of workstations having good graphics and computational capabilities. This is a guide to using S-PLUS to perform statistical analyses. It provides both an introduction to the use of S-PLUS and a course in modern statistical methods. Throughout, the emphasis is on presenting practical problems and full analyses of real data sets. Many of the methods discussed are approaches to topics such as linear and non-linear regression models, robust and smooth regression methods, survival analysis, multivariate analysis, tree-based methods, time series, spatial statistics, and classification.

Highlights

  • binding-icon

    9780387982144

    ISBN:

  • binding-icon

    Venables

    Author:

  • binding-icon

    548

    Pages:

  • binding-icon

    771 gm

    Weight:

  • langauage-icon

    English

    Language:

  • date-icon

    1998

    Year:

  • edition-icon

    2nd Edition

    Edition:

  • binding-icon

    Hardcover

    Binding:

₹636

₹795

S-PLUS is a powerful environment for the statistical and graphical analysis of data. It provides the tools to implement many statistical ideas which have been made possible by the widespread availability of workstations having good graphics and computational capabilities. This is a guide to using S-PLUS to perform statistical analyses. It provides both an introduction to the use of S-PLUS and a course in modern statistical methods. Throughout, the emphasis is on presenting practical problems and full analyses of real data sets. Many of the methods discussed are approaches to topics such as linear and non-linear regression models, robust and smooth regression methods, survival analysis, multivariate analysis, tree-based methods, time series, spatial statistics, and classification.

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