Computational Statistics

Zusatztext ? 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 2011For 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 Informationen zum Autor Gunther Sawitzki (Author) Klappentext Suitable for a compact course or self-study! this text 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. Full of examples and with a color insert! it helps readers become familiar with R. Zusammenfassung 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. Inhaltsverzeichnis IntroductionBasic Data Analysis R Programming Conventions Generation of Random Numbers and PatternsCase Study: Distribution DiagnosticsMoments and Quantiles Regression General Regression Model Linear ModelVariance Decomposition and Analysis of Variance Simultaneous InferenceBeyond Linear RegressionComparisonsShift/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 DistributionsDimensions 1! 2! 3! ?!infinity Dimensions Selections Projections Sections! Conditional Distributions! and Coplots Transformations and Dimension Reduction Higher Dimensions High DimensionsAppendix: 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 FunctionsElementary Statistical Functions Distributions! Random Numbers! Densities ?Computing on the LanguageReferences Functions and Variables by Topic Function and Variable Index Subject IndexR Complements! a Statistical Summary! and Literature and Additional References are included with most chapters. ...

Autorentext
Gunther Sawitzki (Author)

Klappentext
Suitable for a compact course or self-study, this text 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. Full of examples and with a color insert, it helps readers become familiar with R.


Zusammenfassung
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.

Inhalt
Introduction. Basic Data Analysis. Regression. Comparisons. Dimensions 1, 2, 3, , infinity. R as a Programming Language and Environment. References. Functions and Variables by Topic. Function and Variable Index. Subject Index.

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