Using R for Introductory Statistics

Using R for Introductory Statistics

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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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Product details

  • Hardback | 432 pages
  • 160 x 234 x 28mm | 739.37g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • New ed.
  • 104 black & white illustrations, 56 black & white tables
  • 1584884509
  • 9781584884507
  • 482,119

Review quote

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
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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, u 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
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43 ratings
4.04 out of 5 stars
5 37% (16)
4 37% (16)
3 21% (9)
2 2% (1)
1 2% (1)
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