Statistical Computing: An Introduction to Data Analysis using S–Plus

S–Plus is a first–rate graphical environment, used by thousands worldwide to perform basic, intermediate and advanced statistical analysis. It is remarkably powerful, yet relatively simple to use, once you have the basics at your fingertips. Statistical Computing: An Introduction to Data Analysis using S–Plus provides a pragmatic introduction to analysing data using S–Plus, whilst covering a huge breadth of topics, and assuming minimal statistical knowledge.
∗ Provides an accessible yet comprehensive introduction to statistical computing, and can be used as a reference volume for S–Plus.
∗ Covers a breadth of topics, including the basics, such as sampling and measures of central tendency and variation; the intermediate, such as analysis of variance and regression; and the most advanced modern methods, such as nonlinear mixed effects modelling and tree models.
∗ Develops each concept from first principles in small steps, with worked examples and implementation advice throughout.
∗ Assumes minimal experience of statistics and computing.
∗ Emphasises graphical data inspection, parameter estimation and model criticism.
∗ Supported by a Web site featuring all the data–frames, along with problems and worked examples.
This is very much an introductory statistics book for all scientists. It is based on the premise that effective data analysis requires the mastery of a core of central ideas and methods, and that these cut across the boundaries of academic disciplines. It is suitable for advanced undergraduate, graduate students, researchers, and industry professionals from science, medicine, engineering, economics, the social sciences, and many other disciplines that have a need for statistical data analysis.

Spis treści:
Statistical methods
Introduction to S–Plus
Experimental design
Central tendency
Probability
Variance
The Normal distr ibution
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

Okładka tylna:
S–Plus is a first–rate graphical environment, used by thousands worldwide to perform basic, intermediate and advanced statistical analysis. It is remarkably powerful, yet relatively simple to use, once you have the basics at your fingertips. Statistical Computing: An Introduction to Data Analysis using S–Plus provides a pragmatic introduction to analysing data using S–Plus, whilst covering a huge breadth of topics, and assuming minimal statistical knowledge.
∗ Provides an accessible yet comprehensive introduction to statistical computing, and can be used as a reference volume for S–Plus.
∗ Covers a breadth of topics, including the basics, such as sampling and measures of central tendency and variation; the intermediate, such as analysis of variance and regression; and the most advanced modern methods, such as nonlinear mixed effects modelling and tree models.
∗ Develops each concept from first principles in small steps, with worked examples and implementation advice throughout.
∗ Assumes minimal experience of statistics and computin g.
∗ Emphasises graphical data inspection, parameter estimation and model criticism.
∗ Supported by a Web site featuring all the data–frames, along with problems and worked examples.
This is very much an introductory statistics book for all scientists. It is based on the premise that effective data analysis requires the mastery of a core of central ideas and methods, and that these cut across the boundaries of academic disciplines. It is suitable for advanced undergraduate, graduate students, researchers, and industry professionals from science, medicine, engineering, economics, the social sciences, and many other disciplines that have a need for statistical data analysis.