Computational Statistics: An Introduction to R / Edition 1 by Gunther Sawitzki | 9781420086782 | Hardcover | Barnes & Noble
Computational Statistics: An Introduction to R / Edition 1

Computational Statistics: An Introduction to R / Edition 1

5.0 1
by Gunther Sawitzki
     
 

ISBN-10: 1420086782

ISBN-13: 9781420086782

Pub. Date: 01/27/2009

Publisher: Taylor & Francis

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

Overview

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.

Product Details

ISBN-13:
9781420086782
Publisher:
Taylor & Francis
Publication date:
01/27/2009
Edition description:
New Edition
Pages:
264
Product dimensions:
6.30(w) x 9.30(h) x 0.80(d)

Table of Contents

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

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