Using R for Introductory Statistics

by John Verzani

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.

Genres: Mathematics, Science

432 pages, Hardcover

First published November 29, 2004

Reviews

[Charles Godfrey Kamukama · Rating 4 out of 5]

With an ever-growing demand for accessible statistical computing software in academic settings, John Verzani's "Using R for Introductory Statistics" emerges as a beacon of hope. Garnering a commendable 3.95-star rating from 57 reviews, this book addresses a crucial gap in the literature by bridging the accessibility divide that often hinders the integration of powerful statistical tools like R into educational curricula.

Verzani’s work begins by acknowledging the financial constraints that have limited the adoption of statistical computing software in many academic institutions. R, an open-source software package, offers a compelling solution, and this book serves as a guide to unlock its potential for introductory statistics students.

The author's approach is laudable for its self-contained treatment of statistical topics and the intricacies of R. The pacing of the material is strategically designed, allowing students to gradually grasp data manipulation and exploration before delving into more advanced statistical concepts. The emphasis on exploratory data analysis sets this book apart, offering a more comprehensive understanding than the typical introductory text.

One of the standout features of "Using R for Introductory Statistics" is the inclusion of a chapter on simulation, demonstrating the practical application of statistical concepts. The unified approach to linear models further enhances the book's coherence, making it a valuable resource for students looking to establish a solid foundation in statistics.

The book also extends its usefulness beyond the core content. The appendices covering installation, graphical user interfaces, and teaching with R provide practical guidance. Information on writing functions and producing graphics adds an extra layer of utility, making this text not just a theoretical guide but a practical manual for navigating the R software.

While the book is undoubtedly a valuable resource, the rating falls short of a perfect score due to certain aspects. Some readers may find that the pace, while beneficial for newcomers, could be perceived as slow by those with prior statistical or programming knowledge. Additionally, the writing style, while clear, might benefit from a more engaging tone to captivate a wider audience.

In conclusion, "Using R for Introductory Statistics" by John Verzani stands out as an ideal text for seamlessly integrating the study of statistics with a powerful computational tool. Its comprehensive coverage, thoughtful pacing, and practical appendices make it a must-have for educators and students alike, setting the stage for further exploration and development in the fascinating world of statistics using R.

[HuggablySoft · Rating 4 out of 5]

I read this as a text book for a data analysis class. This book is probably dated, with better versions of R and R packages released on a regular basis.

The writing can be obtuse at times. This book is better at showing the syntax of doing things, and then going back and deeply reading about what you just did in code, accompanied by your own digging around in the days structures to see how the dates changed after following the book's instructions

[Darin]

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 chapter on confidence intervals (page 181): "There is no guarantee, only a high probability, that a confidence interval will always contain the unknown parameter." Confidence values are the degree of confidence that a parameter lies within the calculated bounds, not the probability that it is between these bounds. This seems to be a common mistake that non-statisticians make and reduces my confidence in other statements made by the author.

[Julia Colleen · Rating 4 out of 5]

Let's hear it for Stats!
"R" is the future! Don't fight the future; walk away from SPSS and StatView! R is based on the S language. Sounds bad, but this tech-phobic tool can now generate awesome box plots, run 2 factor ANOVAs *and* confirm that 2+2=4!

[Kw Estes · Rating 3 out of 5]

Its a book on using a computer program for statistics--how exciting could it be?

For those actually interested, it is actually fairly helpful, with good examples and practice problems. No glaring flaws.

[Emmanuel Garcia · Rating 4 out of 5]

It's a nice introduction to R along with basic statistics. The book covers confidence intervals and hypothesis testing very well. It rightfully skips over a protracted discussion of the normal distribution.

[Earo · Rating 5 out of 5]

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.

[Mikhail]

The Simple package referenced in the book doesn't exist any longer. It was replaced by UsingR package where most of the data sets can be found.