Using R for Introductory Statistics

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Features

  • 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.

    Table of Contents

    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

    Editorial Reviews

    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 … Overall, 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

    Downloads / Updates

    Resource OS Platform Updated Description Instructions
    Cross Platform February 04, 2005 This is a link to the author's webpage where the reader can find datasets used in the book, R functions, answer to selected problems, and errata.

    http://wiener.math.csi.cuny.edu/UsingR/