An Introduction to Statistical Inference and Its Applications with R

1st Edition

Michael W. Trosset

Chapman and Hall/CRC
Published June 23, 2009
Textbook - 496 Pages - 72 B/W Illustrations
ISBN 9781584889472 - CAT# C9470
Series: Chapman & Hall/CRC Texts in Statistical Science

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  • Explains how statistical methods are used for data analysis
  • Uses the elementary functions of R to perform the individual steps of statistical procedures
  • Includes amusing anecdotes and trivia, such as Ambrose Bierce’s definition of insurance
  • Introduces basic concepts of inference through a careful study of several important procedures, including parametric and nonparametric methods, analysis of variance, and regression
  • Presents many applications along with supporting data sets
  • Contains exercises at the end of each chapter
  • Offers R code and data sets available for download online

Solutions manual available upon qualified course adoption


Emphasizing concepts rather than recipes, An Introduction to Statistical Inference and Its Applications with R provides a clear exposition of the methods of statistical inference for students who are comfortable with mathematical notation. Numerous examples, case studies, and exercises are included. R is used to simplify computation, create figures, and draw pseudorandom samples—not to perform entire analyses.

After discussing the importance of chance in experimentation, the text develops basic tools of probability. The plug-in principle then provides a transition from populations to samples, motivating a variety of summary statistics and diagnostic techniques. The heart of the text is a careful exposition of point estimation, hypothesis testing, and confidence intervals. The author then explains procedures for 1- and 2-sample location problems, analysis of variance, goodness-of-fit, and correlation and regression. He concludes by discussing the role of simulation in modern statistical inference.

Focusing on the assumptions that underlie popular statistical methods, this textbook explains how and why these methods are used to analyze experimental data.


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