Statistical Computing : An Introduction to Data Analysis Using S-Plus

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Statistical Computing : An Introduction to Data Analysis Using S-Plus

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  • 製本 Hardcover:ハードカバー版／ページ数 761 p.
  • 言語 ENG
  • 商品コード 9780471560401
  • DDC分類 519.50285

Full Description

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.

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