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Introduction to Statistics through Resampling Methods and R/S-Plusยฎ

โœ Scribed by Phillip I. Good(auth.)


Publisher
John Wiley & Sons, Inc. All rights reserved.
Year
2005
Tongue
English
Leaves
240
Category
Library

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โœฆ Synopsis


With its emphasis on the discovery method, this book allows readers to discover solutions on their own rather than simply copy answers or apply a formula by rote. Readers will quickly master and learn to apply statistical methods, such as bootstrap, decision trees, and permutations, to better characterize, report, test, and classify their research findings. In addition to traditional methods, specialized methods are covered, allowing readers to select and apply the most effective method for their research, including:

  • Tests and estimation procedures for one, two, and multiple samples
  • Model building
  • Multivariate analysis
  • Complex experimental design

Throughout the text, the R programming language is used to illustrate new concepts and assist readers in completing exercises. Readers may download the freely available R programming language from the Internet or take advantage of the menu-driven S-PLUSยฎ program.

Written in an informal, highly accessible style, this text is an excellent guide to descriptive statistics, estimation, testing hypotheses, and model building. All the pedagogical tools needed to facilitate quick learning are provided:

  • More than two hundred exercises scattered throughout the text stimulate readers' thinking and actively engage them in applying their newfound skills
  • Companion FTP site provides access to all data sets and programs discussed in the text
  • Dozens of thought-provoking questions in the final chapter, Problem Solving, assist readers in applying statistics to address real-life problems
  • Instructor's manual provides answers to exercises
  • Helpful appendices include an introduction to S-PLUSยฎ features

This text serves as an excellent introduction to statistics for students in all disciplines. The accessible style and focus on real-life problem solving are perfectly suited for both students and practitioners.

Content:
Chapter 1 Variation (pages 1โ€“28):
Chapter 2 Probability (pages 29โ€“51):
Chapter 3 Distributions (pages 52โ€“75):
Chapter 4 Testing Hypotheses (pages 76โ€“95):
Chapter 5 Designing an Experiment or Survey (pages 96โ€“128):
Chapter 6 Analyzing Complex Experiments (pages 129โ€“154):
Chapter 7 Developing Models (pages 155โ€“191):
Chapter 8 Reporting Your Findings (pages 192โ€“207):
Chapter 9 Problem Solving (pages 208โ€“217):


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