Computational Statistics: An Introduction to R (Hardback) book cover

Computational Statistics

An Introduction to R

By Günther Sawitzki

© 2009 – Chapman and Hall/CRC

264 pages | 12 Color Illus.

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Suitable for a compact course or self-study, Computational Statistics: An Introduction to R illustrates how to use the freely available R software package for data analysis, statistical programming, and graphics. Integrating R code and examples throughout, the text only requires basic knowledge of statistics and computing.

This introduction covers one-sample analysis and distribution diagnostics, regression, two-sample problems and comparison of distributions, and multivariate analysis. It uses a range of examples to demonstrate how R can be employed to tackle statistical problems. In addition, the handy appendix includes a collection of R language elements and functions, serving as a quick reference and starting point to access the rich information that comes bundled with R.

Accessible to a broad audience, this book explores key topics in data analysis, regression, statistical distributions, and multivariate statistics. Full of examples and with a color insert, it helps readers become familiar with R.


… instructors will find lots of interesting material to use in a variety of courses. In addition, most non-expert users of R will enjoy reading the book and learn a few things they did not know before.

—T. Mildenberger, Statistical Papers, July 2011

For those who want to learn R and have a good statistics background, this book is a good choice. … the book is quite valuable and I am very glad that I have acquired a copy.

—David Booth, Technometrics, August 2010

… a fresh perspective on teaching statistics. … The book introduces its topics and the corresponding methodologies well. … the book is well put together and quite enjoyable for its purpose of serving a small course on computational statistics. …

Journal of Statistical Software, December 2009

… a well-written and nicely organized book suitable for quantitatively and computationally sophisticated readers. … it is the integration of interesting examples and associated R code that make the text a pleasure to read and work through. The examples are neither overly trivial … nor excessively complicated, and the R code is similarly accessible without being either too simple or complex. … Computational Statistics: An Introduction to R will be most useful to computer savvy readers with at least some skill in statistical programming who would like a succinct introduction to R. It could also be useful as a supplementary text for upper-level undergraduate or graduate courses with labs that use R. …

—Ronald D. Fricker, Jr., The American Statistician

Table of Contents


Basic Data Analysis

R Programming Conventions

Generation of Random Numbers and Patterns

Case Study: Distribution Diagnostics

Moments and Quantiles


General Regression Model

Linear Model

Variance Decomposition and Analysis of Variance

Simultaneous Inference

Beyond Linear Regression


Shift/Scale Families and Stochastic Order

QQ Plot, PP Plot, and Comparison of Distributions

Tests for Shift Alternatives

A Road Map

Power and Confidence

Qualitative Features of Distributions

Dimensions 1, 2, 3, …, infinity




Sections, Conditional Distributions, and Coplots

Transformations and Dimension Reduction

Higher Dimensions

High Dimensions

Appendix: R as a Programming Language and Environment

Help and Information

Names and Search Paths

Administration and Customization

Basic Data Types

Output for Objects

Object Inspection

System Inspection

Complex Data Types

Accessing Components

Data Manipulation



Debugging and Profiling

Control Structures

Input and Output to Data Streams; External Data

Libraries, Packages

Mathematical Operators and Functions; Linear Algebra

Model Descriptions

Graphic Functions

Elementary Statistical Functions

Distributions, Random Numbers, Densities …

Computing on the Language


Functions and Variables by Topic

Function and Variable Index

Subject Index

R Complements, a Statistical Summary, and Literature and Additional References are included with most chapters.

Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General