Descripción del título
Introductory statistics wit...
R 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
Monografía
monografia Rebiun31585248 https://catalogo.rebiun.org/rebiun/record/Rebiun31585248 m o d cr cn||||||||| 020313s2002 nyua ob 001 0 eng d 2002020947 53482727 55050761 228042440 228372728 456042932 474416102 551393196 647290567 722206656 793541107 814404404 888521337 961487168 962697991 988415363 1035702665 1037428480 1045505143 1078826278 1086683107 1097344027 1193134199 1200086236 1202255654 1259123927 1288250298 038722632X electronic bk.) 9780387226323 electronic bk.) 0585477957 electronic bk.) 9780585477954 electronic bk.) 1280009748 9781280009747 9780387954752 0387954759 9780387790541 electronic bk.) 0387790543 electronic bk.) 9786610009749 6610009740 10.1007/b97671 doi AU@ 000051742212 AU@ 000053231209 NLGGC 382469690 NZ1 11922950 NZ1 14969564 YDXCP 2296333 AU@ 000066532570 AU@ 000067496626 MERUC eng pn MERUC CCO E7B OCLCQ N$T YDXCP EBLCP UV0 UAB OKU OCLCQ QE2 IDEBK OCLCO OCLCQ GW5XE OCLCF OCLCQ DKDLA OCLCQ COO OCLCQ NLGGC OCLCQ EZ9 AZK MOR OCLCQ KIJ STF BRL OCLCQ CEF NRAMU U3W OCLCQ TKN CANPU LEAUB NZHMA CNTRU OCLCQ UKEHC OCLCO N$T OCLCA OCLCQ OCLCO QGK S2H MAT 029000 bisacsh UPLH bicssc HA lcco 512.764 519.5 21 31.73 bcl 54.81 bcl 70.03 bcl Dalgaard, Peter Introductory statistics with R Peter Dalgaard New York Springer 2002 New York New York Springer 1 online resource (xv, 267 pages) illustrations 1 online resource (xv, 267 pages) Text txt rdacontent computer c rdamedia online resource cr rdacarrier data file rda Statistics and computing Includes bibliographical references (pages 259-260) and index Cover -- Preface -- Contents -- 1. Basics -- 2. Probability and Distributions -- 3. Descriptive Statistics and Graphics -- 4. One- and Two-Sample Tests -- 5. Regression and Correlation -- 6. Analysis of Variance and the Kruskal-Wallis test -- 7. Tubular Data -- 8. Power and the Computaion of Sample Size -- 9. Multiple Regression -- 10. Linear Models -- 11. Logistic Regression -- 12. Survival Analysis -- A-Obtaining and Installing R -- B-Data Sets In The ISWR Package -- C-Compendium -- Bibliography R 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 Statistics- Data processing R (Computer program language) Mathematical Computing Statistique- Informatique R (Langage de programmation) MATHEMATICS- Probability & Statistics- General R (Computer program language) Statistics- Data processing R (computerprogramma) Statistiek Estatística (processamento de dados) Linguagem de programação Programação matemática Statistics COMPUTING Data processing R (Computer program language) COMPUTER LANGUAGES Electronic books Electronic books Leermiddelen (vorm) Print version Dalgaard, Peter. Introductory statistics with R. New York : Springer, 2002 (DLC) 2002020947 Statistics and computing