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Using R for Introductory Statistics

By John Verzani

Chapman and Hall/CRC – 2004 – 432 pages

Series: Chapman & Hall/CRC The R Series

Purchasing Options:

  • Add to CartHardback: $61.95
    978-1-58488-450-7
    November 29th 2004

Description

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.

Reviews

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.

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

Contents

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

Name: Using R for Introductory Statistics (Hardback)Chapman and Hall/CRC 
Description: By John Verzani. 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...
Categories: Regression Analysis and Multivariate Statistics, Statistics & Probability, Statistics & Computing, Quantitative Methods