Regression Modeling Methods, Theory, and Computation with SAS

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.

Preface. Review of Fundamentals of Statistics. Bivariate Linear Regression and Correlation. Misspecified Disturbance Terms. Nonparametric Regression. Logistic Regression. Bayesian Regression. Robust Regression. Fuzzy Regression. Random Coefficients Regression. L1 and q-Quantile Regression. Regression in a Spatial Domain. Multiple Regression. Normal Correlation Models. Ridge Regression. Indicator Variables. Polynomial Model Estimation. Semiparametric Regression. Nonlinear Regression. Issues in Time Series Modeling and Estimation. Appendix. References. Index.