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Publisher:

Chapman & Hall/CRC

Publication Date:

2005

Number of Pages:

414

Format:

Hardcover

Price:

53.95

ISBN:

9781584884507

Category:

Textbook

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

[Reviewed by , on ]

Miklós Bóna

10/28/2010

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. About one-fifth of the book is a collection of Appendices that explains how to install the software package in your computer for three different operating systems, how to use interfaces, graphics, and how to program. This is great, since this will broaden the reach of the book by attracting self-instructing readers who otherwise would not know how to start.

As far as the text itself goes, the examples and exercises are well-chosen; they concern questions to which we would actually like to know the answer. Topical coverage is comparable to more traditional introductory textbooks, though the treatment is somewhat less formal than the average.

My only critical remark is that none of the exercises and problems have their solution included in the book. I do understand that there is a website with selected solutions, and that instructors can receive a full solution manual. I also understand that the book involves the use of R, and therefore, the student using the book will spend a lot of time at a computer, so that it's not unreasonable to assume that he or she might as well check the solutions on the web. Still, we do not want to send the message that without immediate access to a software package, our fresh knowledge is useless, and indeed, there are a few exercises in the book that can be solved just by thinking. A few of these should have their solutions in the book, to encourage that kind of learning as well.

Miklós Bóna is Professor of Mathematics at the University of Florida.

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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