Praise for the First Edition

"The attention to detail is impressive. The book is very well
written and the author is extremely careful with his descriptions .
. . the examples are wonderful." ?The American
Statistician

Fully revised to reflect the latest methodologies and emerging
applications, Applied Regression Modeling, Second Edition
continues to highlight the benefits of statistical methods,
specifically regression analysis and modeling, for understanding,
analyzing, and interpreting multivariate data in business, science,
and social science applications.

The author utilizes a bounty of real-life examples, case
studies, illustrations, and graphics to introduce readers to the
world of regression analysis using various software packages,
including R, SPSS, Minitab, SAS, JMP, and S-PLUS. In a clear and
careful writing style, the book introduces modeling extensions that
illustrate more advanced regression techniques, including logistic
regression, Poisson regression, discrete choice models, multilevel
models, and Bayesian modeling.

In addition, the Second Edition features clarification
and expansion of challenging topics, such as:

• Transformations, indicator variables, and interaction


• Testing model assumptions


• Nonconstant variance


• Autocorrelation


• Variable selection methods


• Model building and graphical interpretation

Throughout the book, datasets and examples have been updated and
additional problems are included at the end of each chapter,
allowing readers to test their comprehension of the presented
material. In addition, a related website features the book's
datasets, presentation slides, detailed statistical software
instructions, and learning resources including additional problems
and instructional videos.

With an intuitive approach that is not heavy on mathematical
detail, Applied Regression Modeling, Second Edition is an
excellent book for courses on statistical regression analysis at
the upper-undergraduate and graduate level. The book also serves as
a valuable resource for professionals and researchers who utilize
statistical methods for decision-making in their everyday work.