Venables ,. 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.
Modern Applied Statistics with S
This book is a guide to using S environments to perform statistical analyses and provides both an introducti S is a powerful environment for the statistical and graphical analysis of data. 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.
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.
The introductory material has been rewritten to emphasis the import, export and manipulation of data. Get A Copy.
Hardcover , 4th , pages. Published September 2nd by Springer first published January 1st More Details Original Title.
- Modern applied statistics with S-Plus - William N. Venables, Brian D. Ripley - Google книги.
- by W. N. Venables and B. D. Ripley.
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This book is not yet featured on Listopia. Community Reviews. Showing Rating details. More filters. Sort order. Mar 24, Kelly rated it really liked it Shelves: grad-school , science. This is an very good resource for learning how to implement various statistical methods in R which is based on S. The authors do provide some theoretical and mathematical explanation of the various methods they cover, which was helpful as a refresher, but I found it was not as effective for learning the basis of methods I didn't already know, especially because the authors go through things rather quickly.
Modern Applied Statistics with S-Plus
They have a wealth of examples from a wide range of fields, which is helpful for people This is an very good resource for learning how to implement various statistical methods in R which is based on S. Throughout, modern techniques such as robust methods, non-parametric smoothing, and bootstrapping are used where appropriate. The material has been extensively rewritten using new examples and the latest computationally intensive methods. The companion volume on S Programming will provide an in-depth guide for those writing software in the S language.
There are extensive on-line complements covering advanced material, user-contributed extensions, further exercises, and new features of S-PLUS as they are introduced. Professor Ripley holds the Chair of Applied Statistics at the University of Oxford, and is the author of four other books on spatial statistics, simulation, pattern recognition, and neural networks.
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