1st Edition

Computational Statistics An Introduction to R

By Günther Sawitzki Copyright 2009
    274 Pages 12 Color Illustrations
    by Chapman & Hall

    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.

    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.


    Gunther Sawitzki (Author)

    … 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