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Table of Contents

 

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.

 

Introduction

 

Basic Data Analysis

 

R Programming Conventions

 

Generation of Random Numbers and Patterns

 

Case Study: Distribution Diagnostics

 

Moments and Quantiles

 

Regression

 

General Regression Model

 

Linear Model

 

Variance Decomposition and Analysis of Variance

 

Simultaneous Inference

 

Beyond Linear Regression

 

Comparisons

 

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

 

Dimensions

 

Selections

 

Projections

 

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

 

Operators

 

Functions

 

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

 

References

 

Functions and Variables by Topic

 

Function and Variable Index

 

Subject Index

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.