This textbook adopts an applied approach, demonstrating concepts by means of worked examples. Data sets are contained on the accompanying disk included. All textual material is organized in such a way that the reader is taken from relatively simple topics to more complex concepts gradually.

From the Publisher

Applied Linear Regression Models assumes the use of computers. Thus, while the basic mathematical steps are given, the text does not dwell on computational details. This allows instructors to eliminate complex formulas and focus on basic principles.
Multiple linear regression analysis discussion starts the text.
Polynomial regression in now woven into the discussion of multiple linear regression.
Qualitative predictor variables now follows discussion of multiple regression model building and diagnostics.
There is an expanded discussion of diagnostics and remedial measures.
New topics added include: robust tests for constancy of the error variance, smoothing techniques to explore the shape of the regression function, robust regression and nonparametric regression techniques, bootstrapping methods for evaluating the precision of sample estimates for complex situations, and estimation of the variance and standard derivation functions to obtain weights for weighted least squares.
Chapter 14 has been revised and expanded to include introduction to polytomous logistic regression, Poisson regression, and generalized linear models.
A disk containing data sets for all examples, problems, exercises, and projects as well as data in Appendix C is packaged with each text.
Applied Linear Regression Models contains several new case studies at strategic places to aid understanding of the methods discussed.
A check in the margin of the problems section indicates the Student Solutions Manual provides immediate and final answers for self-checking.
The expanded use of graphs includes scatter plot matrices, three-dimensional rotating plots, and conditional effects plots.
The comprehensive use of computer and graphic plots helps focus the text on analysis and models.
A chapter on binary dependent variables reflects the trend to the increasing importance of logistic regression models for binary dependent variables in many areas of application.
Model building is thoroughly examined to allow students to see how the model-building process integrates many of the elements considered in earlier chapters.
The discussion of regression diagnostics includes the DFBETAS, DFFITS, and PRESS measures.

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