Overview

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 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.

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9781420091977

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In his book, Michael Panik takes up many aspects of modeling with a pedagogical approach, helping the reader to understand the process of the problem and proposed methods. The appendices enrich his process to [readers] who want to increase their knowledge. ! this book is a very good tool for students and teachers in statistics, but also for researchers wishing to improve their knowledge in statistical modeling to apply it in their expertise domain. --Christian Derquenne, Journal of Statistical Software, February 2010

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Panik, Michael

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