03500nam a22004095i 4500001001800000003000900018005001700027007001500044008004100059020001800100020001900118024003500137100003000172245008000202264004600282300003300328336002600361337002600387338003600413347002400449490004100473505044500514520168100959650001602640650002002656650003002676650002902706650001602735650005002751650002002801650003702821710003402858773002002892776003602912830004102948856010102989978-0-387-79054-1DE-He21320260521092009.0cr nn 008mamaa100301s2008    xxu|    s    |||| 0|eng d  a9780387790541  a997803877905417 a10.1007/978-0-387-79054-12doi1 aDalgaard, Peter.eauthor.10aIntroductory Statistics with Rh[electronic resource] /cby Peter Dalgaard. 1aNew York, NY :bSpringer New York,c2008.  aXVI, 364p.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aStatistics and Computing,x1431-87840 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.  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. 0aSTATISTICS. 0aBIOINFORMATICS. 0aBIOLOGYxDATA PROCESSING. 0aMATHEMATICAL STATISTICS.14aSTATISTICS.24aSTATISTICS AND COMPUTING/STATISTICS PROGRAMS.24aBIOINFORMATICS.24aCOMPUTER APPL. IN LIFE SCIENCES.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9780387790534 0aStatistics and Computing,x1431-878440uhttp://dx.doi.org/10.1007/978-0-387-79054-1zVer el texto completo en las instalaciones del CICY