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,
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
"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
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