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Using R for Introductory Statistics

 (Google eBook)
Front Cover
9 Reviews
CRC Press, Nov 29, 2004 - Mathematics - 432 pages
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.
  

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Review: Using R for Introductory Statistics

User Review  - Darin Brezeale - Goodreads

Overall this is a pretty good basic introduction to statistics that uses R for calculating values and producing graphs. My one problem with the book is when the author states the following in the ... Read full review

Review: Using R for Introductory Statistics

User Review  - Earo - Goodreads

Although some commands and symbols have been discarded, it did a good job to combine stat with R. The best prog bk must include problems. Read full review

All 5 reviews »

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Contents

1 Data
1
2 Univariate Data
31
3 Bivariate data
67
4 Multivariate Data
102
5 Describing Populations
138
6 Simulation
161
7 Confidence Intervals
178
8 Significance Tests
207
11 Analysis of Variance
298
12 Two Extensions of the Linear Model
327
Appendix A Getting Installing and Running R
343
Appendix B Graphical User Interfaces and R
348
Appendix C Teaching with R
354
Appendix D More on Graphics with R
356
Appendix E Programming in R
369
Index
392

9 Goodness of Fit
239
10 Linear Regression
264

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