Computational statistics : an introduction to R / G÷unther Sawitzki.
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 s...
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| Online Access: |
Full Text (via Taylor & Francis) |
|---|---|
| Main Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
Boca Raton :
CRC Press,
©2009.
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| Subjects: |
MARC
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| 100 | 1 | |a Sawitzki, Gèunther. | |
| 245 | 1 | 0 | |a Computational statistics : |b an introduction to R / |c G÷unther Sawitzki. |
| 260 | |a Boca Raton : |b CRC Press, |c ©2009. | ||
| 300 | |a 1 online resource (xiv, 251 pages, 8 unnumbered pages of plates) : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent. | ||
| 337 | |a computer |b c |2 rdamedia. | ||
| 338 | |a online resource |b cr |2 rdacarrier. | ||
| 500 | |a "A Chapman & Hall book." | ||
| 504 | |a Includes bibliographical references (pages 233-235) and indexes. | ||
| 505 | 0 | |a 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. | |
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a R (Computer program language) | |
| 650 | 0 | |a Mathematical statistics |x Data processing. | |
| 650 | 7 | |a Mathematical statistics |x Data processing. |2 fast |0 (OCoLC)fst01012133. | |
| 650 | 7 | |a R (Computer program language) |2 fast |0 (OCoLC)fst01086207. | |
| 776 | 1 | |z 9781420086782. | |
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