Regression Modeling
Methods, Theory, and Computation with SAS
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Requiring only basic knowledge of statistics and calculus, this comprehensive, accessible text provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs. He explores the well-known methods of OLS and maximum likelihood regression before developing many alternative regression techniques, such as nonparametric, logistic, Bayesian, robust, fuzzy, random coefficients, spatial, polynomial, ridge, semiparametric, and more. T...
Requiring only basic knowledge of statistics and calculus, this comprehensive, accessible text provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs. He explores the well-known methods of OLS and maximum likelihood regression before developing many alternative regression techniques, such as nonparametric, logistic, Bayesian, robust, fuzzy, random coefficients, spatial, polynomial, ridge, semiparametric, and more. The book also covers nonlinear and time series modeling.
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