05231nam 22005415 450 991025407920332120250410121911.03-319-33946-X10.1007/978-3-319-33946-7(CKB)3710000000866389(DE-He213)978-3-319-33946-7(MiAaPQ)EBC4694135(PPN)195511670(EXLCZ)99371000000086638920160920d2016 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierAdaptive Regression for Modeling Nonlinear Relationships /by George J. Knafl, Kai Ding1st ed. 2016.Cham :Springer International Publishing :Imprint: Springer,2016.1 online resource (XXV, 372 p. 57 illus., 13 illus. in color.)Statistics for Biology and Health,2197-56713-319-33944-3 Includes bibliographical references and index.Introduction -- Adaptive Regression Modeling of Univariate Continuous Outcomes -- Adaptive Regression Modeling of Univariate Continuous Outcomes in SAS -- Adaptive Regression Modeling of Multivariate Continuous Outcomes -- Adaptive Regression Modeling of Multivariate Continuous Outcomes in SAS -- Adaptive Transformation of Positive Valued Continuous Outcomes -- Adaptive Logistic Regression Modeling of Univariate Dichotomous and Polytomous Outcomes -- Adaptive Logistic Regression Modeling of Univariate Dichotomous and Polytomous Outcomes in SAS -- Adaptive Logistic Regression Modeling of Multivariate Dichotomous and Polytomous Outcomes -- Adaptive Logistic Regression Modeling of Multivariate Dichotomous and Polytomous Outcomes in SAS -- Adaptive Poisson Regression Modeling of Univariate Count Outcomes -- Adaptive Poisson Regression Modeling of Univariate Count Outcomes in SAS -- Adaptive Poisson Regression Modeling of Multivariate Count Outcomes -- Adaptive Poisson Regression Modeling of Multivariate Count Outcomes in SAS -- Generalized Additive Modeling -- Generalized Additive Modeling in SAS -- Multivariate Adaptive Regression Spline Modeling -- Multivariate Adaptive Regression Spline Modeling in SAS -- Adaptive Regression Modeling Formulation. .This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the standard, logistic, and Poisson regression contexts with continuous, discrete, and counts outcomes, respectively, either univariate or multivariate. The book also provides a comparison of adaptive modeling to generalized additive modeling (GAM) and multiple adaptive regression splines (MARS) for univariate outcomes. The authors have created customized SAS macros for use in conducting adaptive regression modeling. These macros and code for conducting the analyses discussed in the book are available through the first author's website and online via the book’s Springer website. Detailed descriptions of how to use these macros and interpret their output appear throughout the book. These methods can be implemented using other programs. Provides insight into modeling of nonlinear relationships and also justifications for when to use them, thereby providing novel insights about relationships Addresses not only adaptive generation of additive models but also of models based on nonlinear interactions Discusses adaptive modeling of variances/dispersions as well as of means Highlights both univariate and multivariate outcomes, rather than solely univariate outcomes.Statistics for Biology and Health,2197-5671BiometryStatisticsBiostatisticsStatistical Theory and MethodsBiometry.Statistics.Biostatistics.Statistical Theory and Methods.519.536Knafl George Jauthttp://id.loc.gov/vocabulary/relators/aut755792Ding Kaiauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910254079203321Adaptive Regression for Modeling Nonlinear Relationships2047106UNINA03427oam 2200661 c 450 991056300710332120250513224748.010.3726/b13578(CKB)5450000000173898(oapen)https://directory.doabooks.org/handle/20.500.12854/38655(PH02)9783631750209(oapen)doab38655(EXLCZ)99545000000017389820240525h20181996 uy 0gerurnnunnnannuutxtrdacontentcrdamediacrrdacarrierBetriebstypenprofilierung in vertraglichen VertriebssystemenEine Analyse von Einflußfaktoren und Erfolgswirkungen auf der Grundlage eines Vertragshändlersystems im AutomobilhandelH. Meffert, Stefan Wöllenstein, Universität Münster1st, New ed.Frankfurt a.MPH0220182018, c19961 online resource (418 p.), EPDFSchriften zu Marketing und Management28Peter Lang GmbH, Internationaler Verlag der Wissenschaften3-631-75020-X Aus dem Inhalt: Konzeptionelle und empirische Analyse der Betriebstypenprofilierung in vertraglichen Vertriebssystemen - Umfassende Untersuchung am Beispiel eines Vertragshändlersystems der deutschen Automobilbranche - Ermittlung von sechs realen Betriebstypen und Analyse ihrer zentralen Differenzierungsdimension, Erfolgsfaktoren und Erfolgswirkungen im jeweiligen situativen Kontext.Im Handel gewinnt die langfristige, strategisch fundierte Profilierung im Wettbewerbsumfeld zunehmend an Bedeutung. Dies gilt in besonderem Maße für Handelsunternehmen in vertraglichen Vertriebssystemen, die sowohl mit systemfremden wie auch systeminternen Betrieben konkurrieren. Hohe Aktualität hat diese Problematik für die Vertragshändlersysteme der Automobilbranche. Vor diesem Hintergrund wird in dieser Studie das Verhalten von 345 Automobilhändlern unter Einbeziehung ihrer Unternehmens-, Markt- und Erfolgssituation analysiert. Auf der Grundlage eines konzeptionellen Ansatzes zur Betriebstypenprofilierung werden sechs reale Betriebstypen im Automobilhandel identifiziert und ihre zentralen Profilierungsdimensionen, Einflußfaktoren sowie die daraus resultierenden Erfolgswirkungen untersucht.Sales & marketing managementbicsscDistribution & warehousing managementbicsscTransport technology & tradesbicsscAnalyseAutomobilhandelBetriebstypenprofilierungEineeinesEinflußfaktorenErfolgswirkungenGrundlageVERTRAGLICHvertraglichenVertragshändlersystemsVertriebssystemenWöllensteinSales & marketing managementDistribution & warehousing managementTransport technology & tradesWöllenstein Stefanauth1331134Meffert HedtWöllenstein StefanautUniversität MünsterautPH02PH02BOOK9910563007103321Betriebstypenprofilierung in vertraglichen Vertriebssystemen3040248UNINA