Computational statistics : an introduction to R

 Content: 1 Basic data analysis --
 R programming conventions --
 Generation of random numbers and patterns --
 Random numbers --
 Patterns --
 Case study: distribution diagnostics --
 Distribution functions --
 Histograms --
 Barcharts --
 Statistics of distribution functions; Kolmogorov-Smirnov tests --
 Monte Carlo confidence bands --
 Statistics of histograms and related plots; X2-tests --
 Moments and quantiles --
 R complements --
 Random numbers --
 Graphical comparisons --
 Functions --
 Enhancing graphical displays --
 R internals --
 parse --
 eval --
 print --
 Executing files --
 Packages --
 Statistical summary --
 Literature and additional references --
 2 Regression --
 General regression model --
 Linear model --
 Factors --
 Least squares estimation --
 Regression diagnostics --
 More examples for linear models --
 Model formulae --
 Gauss-Markov estimator and residuals --
 Variance decomposition and analysis of variance --
 Simultaneous inference --
 Scheff́e's confidence bands --
 Tukey's confidence intervals --
 Case study: titre plates --
 Beyond linear regression --
 Transformations --
 Generalised linear models --
 Local regression --
 R complements --
 Discretisation --
 External data --
 Testing software --
 R data types --
 Classes and polymorphic functions --
 Extractor functions --
 Statistical summary --
 Literature and additional references. 3 Comparisons --
 Shift/scale families, and stochastic order --
 QQ plot, PP plot, and comparison of distributions --
 Kolmogorov-Smirnov tests --
 Tests for shift alternatives --
 Road map --
 Power and confidence --
 Theoretical power and confidence --
 Simulated power and confidence --
 Quantile estimation --
 Qualitative features of distributions --
 Statistical summary --
 Literature and additional references --
 4 Dimensions 1, 2, 3 ..., c --
 R Complements --
 Dimensions --
 Selections --
 Projections --
 Marginal distributions and scatter plot matrices --
 Projection pursuit --
 Projections for dimensions 1, 2, 3 ... 7 --
 Parallel coordinates --
 Sections, conditional distributions and coplots --
 Transformations and dimension reduction --
 Higher dimensions --
 Linear case --
 Partial residuals and added variable plots --
 Non-linear case --
 Example: cusp non-linearity --
 Case study: Melbourne temperature data --
 Curse of dimensionality --
 Case study: body fat --
 High dimensions --
 Statistical summary --
 R as a programming language and environment --
 Help and information --
 Names and search paths --
 Administration and customisation --
 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 --
 High-level graphics --
 Low-level graphics --
 Annotations and legends --
 Graphic parameters and Llyout --
 Elementary statistical functions --
 Distributions, random numbers, densities ... --
 Computing on the language.
 Abstract:             
              Suitable for a compact course or self-study, this book illustrates how to use the R software package for data analysis, statistical programming, and graphics. It covers one-sample analysis and distribution diagnostics, regression, two-sample problems and comparison of distributions, and multivariate analysis.                 �Read more...