By George J. Knafl,Kai Ding
This e-book offers tools for investigating even if relationships are linear or nonlinear and for adaptively becoming acceptable types after they are nonlinear. facts analysts will how to include nonlinearity in a single or extra predictor variables into regression types for various different types of final result variables. Such nonlinear dependence is frequently now not thought of in utilized examine, but nonlinear relationships are universal and so must be addressed. a typical linear research can produce deceptive conclusions, whereas a nonlinear research gives you novel insights into information, now not another way possible.
A number of examples of some great benefits of modeling nonlinear relationships are awarded in the course of the ebook. tools are coated utilizing what are referred to as fractional polynomials in response to real-valued strength modifications of fundamental predictor variables mixed with version choice in response to chance cross-validation. The e-book covers the best way to formulate and behavior such adaptive fractional polynomial modeling within the general, logistic, and Poisson regression contexts with non-stop, discrete, and counts results, respectively, both univariate or multivariate. The booklet additionally presents a comparability of adaptive modeling to generalized additive modeling (GAM) and a number of adaptive regression splines (MARS) for univariate results.
The authors have created personalized SAS macros to be used in undertaking adaptive regression modeling. those macros and code for engaging in the analyses mentioned within the e-book can be found during the first author's site and on-line through the book’s Springer web site. designated descriptions of the way to take advantage of those macros and interpret their output seem in the course of the publication. those tools will be applied utilizing different programs.
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Adaptive Regression for Modeling Nonlinear Relationships (Statistics for Biology and Health) by George J. Knafl,Kai Ding