2nd Edition

Using R for Introductory Statistics

By John Verzani Copyright 2014
    518 Pages 112 B/W Illustrations
    by Chapman & Hall

    518 Pages 112 B/W Illustrations
    by Chapman & Hall

    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:

    • Increased emphasis on more idiomatic R provides a grounding in the functionality of base R.
    • Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible.
    • Use of knitr package makes code easier to read and therefore easier to reason about.
    • Additional information on computer-intensive approaches motivates the traditional approach.
    • Updated examples and data make the information current and topical.

    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.

    DATA
    What Is Data?
    Some R Essentials
    Accessing Data by Using Indices
    Reading in Other Sources of Data
    UNIVARIATE DATA
    Categorical Data
    Numeric Data
    Shape of a Distribution
    BIVARIATE DATA
    Pairs of Categorical Variables
    Comparing Independent Samples
    Relationships in Numeric Data
    Simple Linear Regression
    MULTIVARIATE DATA
    Viewing Multivariate Data
    R Basics: Data Frames and Lists
    Using Model Formula with Multivariate Data
    Lattice Graphics
    Types of Data in R
    DESCRIBING POPULATIONS
    Populations
    Families of Distributions
    The Central Limit Theorem
    SIMULATION
    The Normal Approximation for the Binomial
    for loops
    Simulations Related to the Central Limit Theorem
    Defining a Function
    Investigating Distributions
    Bootstrap Samples
    Alternates to for loops
    CONFIDENCE INTERVALS
    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 TESTS
    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
    LINEAR REGRESSION
    The Simple Linear Regression Model
    Statistical Inference for Simple Linear Regression
    Multiple Linear Regression
    ANALYSIS OF VARIANCE
    One-Way ANOVA
    Using lm() for ANOVA
    ANCOVA
    Two-Way ANOVA
    TWO EXTENSIONS OF THE LINEAR MODEL
    Logistic Regression
    Nonlinear Models
    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
    Rcdmr
    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
    Editing Functions
    Using Functions
    Using Files and a Better Editor
    Object-Oriented Programming with R
    INDEX

    Biography

    John Verzani

    "Overall, I really like the rich examples and data sets that the book provides (through using the R package). I believe this is the strength of the book and I think many educators, especially those teaching first year statistics, would find this aspect highly beneficial to their students...the book is ideal for a trained statistician who has never used R."
    Australian and New Zealand Journal of Statistics, March 2016

    "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."
    Zentralblatt MATH