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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Michael J. Crawley, FRS, is with the Department of Biological Sciences, Imperial College of Science, Technology and Medicine. He is the author of three bestselling Wiley statistics titles and five life science books.
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.
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.
"About this title" may belong to another edition of this title.
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