Presents a truly comprehensive introductory treatment of statistics using RCovers univariate, bivariate, and multivariate data illustrating both the basic usage and a more powerful usageProvides the necessary probabilistic intuition to make proper statistical inferences without the demands of a more formal treatmentOffers 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 varianceIllustrates R's flexible and consistent approach to statistical modeling by showing two extensions to the standard linear modelIncludes an accompanying R package, UsingR, that can be easily downloaded and installed providing many of the data sets used in the textContains 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
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
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
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.
, 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.
||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.
||September 28, 2016
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