© 2014 – Chapman and Hall/CRC
518 pages | 112 B/W Illus.
The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version.
See What’s New in the Second Edition:
The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text.
The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.
"Now in its second edition, the book introduces the reader to exploratory data analysis and manipulation, statistical inference, and statistical models. Particular attention is given to thoroughly learning base R before extending R’s capabilities with packages. … interesting, topical, and challenging examples. … a stimulating read for the classroom-based student …"
—Significance, April 2015
Praise for the First Edition:
"The author has made a very serious effort to introduce entry-level students of statistics to the open-source software package R. One mistake most authors of similar texts make is to assume some basic level of familiarity, either with the subject to be taught, or the tool (the software package) to be used in teaching the subject. This book does not fall into either trap. … the examples and exercises are well-chosen …"
—MAA Reviews, October 2010
"The book presents each new concept in a gentle manner. Numerous examples serve to illustrate both the R commands and the general statistical concepts. … Every chapter contains sample code for plotting … The book also has a rich supply of homework problems that are straightforward and data-focused … I found the book enjoyable to read. Even as an experienced user of R, I learned a few things. … Without hesitation I would use it for an introductory statistics course or an introduction to R for a general audience. Indeed, Verzani’s book may prove a useful travel guide through the sometimes exasperating territory of statistical computing."
—E. Andres Houseman (Harvard School of Public Health), Statistics in Medicine, Vol. 26, 2007
"This book sets out to kill two birds with one stone—introducing R and statistics at the same time. The author accomplishes his twin goals by presenting an easy-to-follow narrative mixed with R codes, formulae, and graphs … [He] clearly has a great command of R, and uses its strength and versatility to achieve statistical goals that cannot be easily reached otherwise … this book contains a cornucopia of information for beginners in statistics who want to learn a computer language that is positioned to take the statistics world by storm."
—Significance, September 2005
"Anyone who has struggled to produce his or her own notes to help students use R will appreciate this thorough, careful and complete guide aimed at beginning students."
—Journal of Statistical Software, November 2005
"This is an ideal text for integrating the study of statistics with a powerful computation tool."
What Is Data?
Some R Essentials
Accessing Data by Using Indices
Reading in Other Sources of Data
Shape of a Distribution
Pairs of Categorical Variables
Comparing Independent Samples
Relationships in Numeric Data
Simple Linear Regression
Viewing Multivariate Data
R Basics: Data Frames and Lists
Using Model Formula with Multivariate Data
Types of Data in R
Families of Distributions
The Central Limit Theorem
The Normal Approximation for the Binomial
Simulations Related to the Central Limit Theorem
Defining a Function
Alternates to for loops
Confidence Interval Ideas
Confidence Intervals for a Population Proportion, p
Confidence Intervals for the Population Mean, µ
Other Confidence Intervals
Confidence Intervals for Differences
Confidence Intervals for the Median
Significance Test for a Population Proportion
Significance Test for the Mean (t-Tests)
Significance Tests and Confidence Intervals
Significance Tests for the Median
Two-Sample Tests of Proportion
Two-Sample Tests of Center
GOODNESS OF FIT
The Chi-Squared Goodness-of-Fit Test
The Chi-Squared Test of Independence
Goodness-of-Fit Tests for Continuous Distributions
The Simple Linear Regression Model
Statistical Inference for Simple Linear Regression
Multiple Linear Regression
ANALYSIS OF VARIANCE
Using lm() for ANOVA
TWO EXTENSIONS OF THE LINEAR MODEL
APPENDIX A: GETTING, INSTALLING, AND RUNNING R
Installing and Starting R
Extending R Using Additional Packages
APPENDIX B: GRAPHICAL USER INTERFACES AND R
The Windows GUI
The Mac OS X GUI
APPENDIX C: TEACHING WITH R
APPENDIX D: MORE ON GRAPHICS WITH R
Low- and High-Level Graphic Functions
Creating New Graphics in R
APPENDIX E: PROGRAMMING IN R
Using Files and a Better Editor
Object-Oriented Programming with R