Using R for Introductory Statistics

John Verzani

November 29, 2004 by Chapman and Hall/CRC
Textbook - 432 Pages - 104 B/W Illustrations
ISBN 9781584884507 - CAT# C4509
Series: Chapman & Hall/CRC The R Series

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  • Presents a truly comprehensive introductory treatment of statistics using R
  • Covers univariate, bivariate, and multivariate data illustrating both the basic usage and a more powerful usage
  • Provides the necessary probabilistic intuition to make proper statistical inferences without the demands of a more formal treatment
  • Offers treatment of traditional topics in statistical inference, including confidence intervals, significance tests, goodness of fit, and a unified presentation of linear models, broken up into linear regression and analysis of variance
  • Illustrates R's flexible and consistent approach to statistical modeling by showing two extensions to the standard linear model
  • Includes an accompanying R package, UsingR, that can be easily downloaded and installed providing many of the data sets used in the text
  • Contains numerous examples from a variety of contexts and exercises to test the understanding and learning progress, with many of the answers provided online and a full solutions manual available for instructors
  • Summary

    The cost of statistical computing software has precluded many universities from installing these valuable computational and analytical tools. R, a powerful open-source software package, was created in response to this issue. It has enjoyed explosive growth since its introduction, owing to its coherence, flexibility, and free availability. While it is a valuable tool for students who are first learning statistics, proper introductory materials are needed for its adoption.

    Using R for Introductory Statistics fills this gap in the literature, making the software accessible to the introductory student. The author presents a self-contained treatment of statistical topics and the intricacies of the R software. The pacing is such that students are able to master data manipulation and exploration before diving into more advanced statistical concepts. The book treats exploratory data analysis with more attention than is typical, includes a chapter on simulation, and provides a unified approach to linear models.

    This text lays the foundation for further study and development in statistics using R. Appendices cover installation, graphical user interfaces, and teaching with R, as well as information on writing functions and producing graphics. This is an ideal text for integrating the study of statistics with a powerful computational tool.