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020 _a9780387790541
_99780387790541
024 7 _a10.1007/9780387790541
_2doi
035 _avtls000332990
039 9 _a201509030222
_bVLOAD
_c201404122323
_dVLOAD
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_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
100 1 _aDalgaard, Peter.
_eautor
_9304131
245 1 0 _aIntroductory Statistics with R /
_cby Peter Dalgaard.
264 1 _aNew York, NY :
_bSpringer New York,
_c2008.
300 _axvI, 364 páginas
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aStatistics and Computing,
_x1431-8784
500 _aSpringer eBooks
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.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9780387790534
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-79054-1
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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