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001 978-0-387-79054-1
003 DE-He213
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008 100301s2008 xxu| s |||| 0|eng d
020 _a9780387790541
_9978-0-387-79054-1
024 7 _a10.1007/978-0-387-79054-1
_2doi
050 4 _aQA273.A1-274.9
072 7 _aPBT
_2bicssc
072 7 _aPBWL
_2bicssc
072 7 _aMAT029000
_2bisacsh
072 7 _aPBT
_2thema
072 7 _aPBWL
_2thema
082 0 4 _a519.2
_223
100 1 _aDalgaard, Peter.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aIntroductory Statistics with R
_h[electronic resource] /
_cby Peter Dalgaard.
250 _a2nd ed. 2008.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2008.
300 _aXVI, 364 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStatistics and Computing,
_x2197-1706
505 0 _aBasics -- The R environment -- Probability and distributions -- Descriptive statistics and graphics -- One- and two-sample tests -- Regression and correlation -- Analysis of variance and the Kruskal-Wallis test -- Tabular data -- Power and the computation of sample size -- Advanced data handling -- Multiple regression -- Linear models -- Logistic regression -- Survival analysis -- Rates and Poisson regression -- Nonlinear curve fitting.
520 _aR is an Open Source implementation of the S language. It works on multiple computing platforms and can be freely downloaded. R is now in widespread use for teaching at many levels as well as for practical data analysis and methodological development. This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. A supplementary R package can be downloaded and contains the data sets. The statistical methodology includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one- and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last six chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, survival analysis, Poisson regression, and nonlinear regression. In the second edition, the text and code have been updated to R version 2.6.2. The last two methodological chapters are new, as is a chapter on advanced data handling. The introductory chapter has been extended and reorganized as two chapters. Exercises have been revised and answers are now provided in an Appendix. Peter Dalgaard is associate professor at the Department of Biostatistics at the University of Copenhagen and has extensive experience in teaching within the PhD curriculum at the Faculty of Health Sciences. He has been a member of the R Core Team since 1997.
650 0 _aProbabilities.
650 0 _aMathematical statistics
_xData processing.
650 0 _aBioinformatics.
650 1 4 _aProbability Theory.
650 2 4 _aStatistics and Computing.
650 2 4 _aBioinformatics.
650 2 4 _aComputational and Systems Biology.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9780387790534
776 0 8 _iPrinted edition:
_z9780387570655
830 0 _aStatistics and Computing,
_x2197-1706
856 4 0 _uhttps://doi.org/10.1007/978-0-387-79054-1
_yDisponible en Springer
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
942 _cE-BOOK
950 _aMathematics and Statistics (SpringerNature-11649)
950 _aMathematics and Statistics (R0) (SpringerNature-43713)
999 _c346648
_d346648