## You are here

# Using R for Introductory Statistics

The Basic Library List Committee suggests that undergraduate mathematics libraries consider this book for acquisition.

See our review of the first edition. The major changes for the second edition have to do with R: the latest version is now used, students and instructors are encouraged to use RStudio and "more idiomatic" R. On the statistics side, a small amount of material dealing with Bayesian analysis, resampling methods, and permutation tests has been added.

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

## Dummy View - NOT TO BE DELETED