Classical and Modern Regression With Applications.

Abstract

The author emphasizes applications with examples that illustrate nearly all the techniques discussed. Applications have been selected from physical sciences, engineering, biology, management science and economics. Emphasis is also placed on concepts with a blend between illustrations using real data sets and mathematical and conceptual development. Expanded coverage includes: simultaneous influence, maximum likelihood estimation of parameters, and the plotting of residuals, the use of the general linear hypothesis, indicator variables, the geometry of least squares, the relationship to ANOVA models, Box-Cox transformation with illustrations, categorical response, other nonnormal error situations, autocorrelated errors and logistic regression.