000943977 000__ 02914cam\a2200481Mi\4500 000943977 001__ 943977 000943977 005__ 20230306152349.0 000943977 006__ m\\\\\o\\d\\\\\\\\ 000943977 007__ cr\nn\nnnunnun 000943977 008__ 200820s2020\\\\gw\\\\\\o\\\\\|||\0\eng\d 000943977 019__ $$a1191057231$$a1193124726$$a1195460544$$a1195711732$$a1196167217$$a1197809959$$a1198251814 000943977 020__ $$a3662620278 000943977 020__ $$a9783662620274 000943977 020__ $$z9783662620267 000943977 0247_ $$a10.1007/978-3-662-62027-4$$2doi 000943977 0247_ $$a10.1007/978-3-662-62 000943977 035__ $$aSP(OCoLC)on1197540039 000943977 035__ $$aSP(OCoLC)1197540039$$z(OCoLC)1191057231$$z(OCoLC)1193124726$$z(OCoLC)1195460544$$z(OCoLC)1195711732$$z(OCoLC)1196167217$$z(OCoLC)1197809959$$z(OCoLC)1198251814 000943977 040__ $$aSFB$$beng$$cSFB$$dFIE$$dLQU$$dOCLCO$$dUKMGB$$dEBLCP$$dGW5XE$$dOCLCF 000943977 049__ $$aISEA 000943977 050_4 $$aQA276-280 000943977 08204 $$a519.5$$223 000943977 1001_ $$aWehrens, Ron.$$eauthor. 000943977 24510 $$aChemometrics with R :$$bMultivariate Data Analysis in the Natural and Life Sciences /$$cby Ron Wehrens. 000943977 250__ $$a2nd ed. 2020. 000943977 264_1 $$aBerlin, Heidelberg :$$bSpringer Berlin Heidelberg :$$bImprint: Springer,$$c2020. 000943977 300__ $$a1 online resource (xvi, 308 pages) :$$billustrations. 000943977 336__ $$atext$$btxt$$2rdacontent 000943977 337__ $$acomputer$$bc$$2rdamedia 000943977 338__ $$aonline resource$$bcr$$2rdacarrier 000943977 4901_ $$aUse R!,$$x2197-5736 000943977 504__ $$aIncludes bibliographical references and index. 000943977 5050_ $$aIntroduction. -- Data -- Preprocessing -- Principal Component Analysis -- Self-Organizing Maps. -- Clustering -- Classification -- Multivariate Regression. -- Validation -- Variable Selection -- Chemometric Applications. 000943977 506__ $$aAccess limited to authorized users. 000943977 520__ $$aThis book offers readers an accessible introduction to the world of multivariate statistics in the life sciences, providing a comprehensive description of the general data analysis paradigm, from exploratory analysis (principal component analysis, self-organizing maps and clustering) to modeling (classification, regression) and validation (including variable selection). It also includes a special section discussing several more specific topics in the area of chemometrics, such as outlier detection, and biomarker identification. The corresponding R code is provided for all the examples in the book; and scripts, functions and data are available in a separate R package. This second revised edition features not only updates on many of the topics covered, but also several sections of new material (e.g., on handling missing values in PCA, multivariate process monitoring and batch correction). . 000943977 650_0 $$aStatistics. 000943977 650_0 $$aCheminformatics. 000943977 650_0 $$aBioinformatics. 000943977 830_0 $$aUse R!,$$x2197-5736 000943977 852__ $$bebk 000943977 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=https://dx.doi.org/10.1007/978-3-662-62027-4$$zOnline Access 000943977 909CO $$ooai:library.usi.edu:943977$$pGLOBAL_SET 000943977 980__ $$aEBOOK 000943977 980__ $$aBIB 000943977 982__ $$aEbook 000943977 983__ $$aOnline 000943977 994__ $$a92$$bISE