Statistical Computing: An Introduction to Data Analysis using S-Plus / Edition 1

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Overview

Many statistical modelling and data analysis techniques can be difficult to grasp and apply, and it is often necessary to use computer software to aid the implementation of large data sets and to obtain useful results. S-Plus is recognised as one of the most powerful and flexible statistical software packages, and it enables the user to apply a number of statistical methods, ranging from simple regression to time series or multivariate analysis. This text offers extensive coverage of many basic and more advanced statistical methods, concentrating on graphical inspection, and features step-by-step instructions to help the non-statistician to understand fully the methodology.
* Extensive coverage of basic, intermediate and advanced statistical methods
* Uses S-Plus, which is recognised globally as one of the most powerful and flexible statistical software packages
* Emphasis is on graphical data inspection, parameter estimation and model criticism
* Features hundreds of worked examples to illustrate the techniques described
* Accessible to scientists from a large number of disciplines with minimal statistical knowledge
* Written by a leading figure in the field, who runs a number of successful international short courses
* Accompanied by a Web site featuring worked examples, data sets, exercises and solutions
A valuable reference resource for researchers, professionals, lecturers and students from statistics, the life sciences, medicine, engineering, economics and the social sciences.

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Editorial Reviews

From the Publisher
"...suitable as a reference book for experienced statisticians, a vehicle for learning the S statistical computing language, or a resource for statistics instructors..." (The American Statistician, Vol. 58, No. 1, February 2004)

"...especially useful as an introduction to a wide variety of data analysis techniques." (R News)

"...The book is well written - there is an air of common sense throughout - and is at a level which ensures its usefulness for a wide range of readers..." (Zentralblatt Math, Vol. 1001, No.01, 2003)

"...the book is a useful and practical introduction to many areas of statistical data analysis." (Computational STatistics & Data Analysis)

"...surely not the last statistics book you’ll ever need, but it might well be the first you will ever really use." (Basic Applied Ecology, Vol. 4, No. 3)

"...recommended...contains a wealth of sage advice..." (Technometrics, Vol. 45, No. 4, November 2003)

“...a practical introduction to statistics...does not cover all...sophisticated statistical and graphical features of the S-Plus system, but provides a first class starting point—and, probably, for most readers, a sufficient end point.” (Quarterly of Applied Mathematics, LXI, No. 4, December 2003)

“…a valiant and useful first attempt to present both statistics and S-PLUS together…” (Journal of The Royal Statistical Society Vol.167 No.4)   

From The Critics
Running the gamut of statistical methods, this textbook is adaptable to either an undergraduate or graduate course for non-statisticians and can also serve as a reference for practicing researchers. Crawley (Imperial College of Science, UK) introduces the functions executable in the S-Plus computer package, and walks through worked examples of computer analyses for each technique throughout the book. The 36 chapters cover regression, variance, generalized linear models, tree models, non-parametric smoothing, and time series analysis. No review exercises are provided. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780471560401
  • Publisher: Wiley
  • Publication date: 5/28/2002
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 772
  • Product dimensions: 7.74 (w) x 9.90 (h) x 1.96 (d)

Table of Contents

Statistical methods

Introduction to S-Plus

Experimental design

Central tendency

Probability

Variance

The Normal distribution

Power calculations

Understanding data: graphical analysis

Understanding data: tabular analysis

Classical tests

Bootstrap and jackknife

Statistical models in S-Plus

Regression

Analysis of variance

Analysis of covariance

Model criticism

Contrasts

Split-plot Anova

Nested designs and variance components analysis

Graphs, functions and transformations

Curve fitting and piecewise regression

Non-linear regression

Multiple regression

Model simplification

Probability distributions

Generalised linear models

Proportion data: binomial errors

Count data: Poisson errors

Binary response variables

Tree models

Non-parametric smoothing

Survival analysis

Time series analysis

Mixed effects models

Spatial statistics

Bibliography

Index

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