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