Regression Modeling Methods, Theory, and Computation with SAS

• Main Author: Panik, Michael J. (Author)
• Format: Book
• Language: English
• Published: Boca Raton CRC Press 2009
• Subjects: Regression analysis > Data processing; SAS (Computer program language) > Data processing
• Online Access: Click Here to View Status and Holdings.

 Regression Modeling: Methods, Theory, and Computation with SAS 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. The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression, L1 and q-quantile regression, regression in a spatial domain, ridge regression, semiparametric regression, nonlinear least squares, and time-series regression issues. For most of the regression methods, the author includes SAS procedure code, enabling readers to promptly perform their own regression runs.