Using R for introductory statistics

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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
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Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition

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.