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Regression Modeling - Methods, Theory, and Computation with SAS (Paperback)
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Regression Modeling - Methods, Theory, and Computation with SAS (Paperback)
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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. A Comprehensive, Accessible Source on
Regression Methodology and ModelingRequiring only basic knowledge
of statistics and calculus, this book discusses how to use
regression analysis for decision making and problem solving. It
shows readers the power and diversity of regression techniques
without overwhelming them with calculations.
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