Statistical computing : an introduction to data analysis using S-Plus
Author(s)
Bibliographic Information
Statistical computing : an introduction to data analysis using S-Plus
Wiley, 2002
- : hbk
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Note
Includes bibliographical references (p. 731-733) and index
Description and Table of Contents
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.
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
by "Nielsen BookData"
