Regression Modeling: Methods, Theory, and Computation with SAS

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Product Description

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, L 1 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 Modeling Requiring 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.

Regression Modeling: Methods, Theory, and Computation with SAS Review

As a doctoral student in public health, I am always looking for more texts to add to my professional library in statistics. This book not only covers some of the basic regression methods, but also many of the much more advanced methods that I have in classes. I really like how this text explains simply the ideas behind the different methods and when to use, as well as the SAS code and the explanation of the SAS code and output. The only downside is that I think the index could be a little longer - sometimes it is difficult to find a particular model though it actually is in the text.

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