Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illustrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. While maintaining the accessible appeal of each previous edition, Applied Linear Regression, Fourth Edition features:
Graphical methods stressed in the initial exploratory phase, analysis phase, and summarization phase of an analysisIn-depth coverage of parameter estimates in both simple and complex models, transformations, and regression diagnosticsNewly added material on topics including testing, ANOVA, and variance assumptionsUpdated methodology, such as bootstrapping, cross-validation binomial and Poisson regression, and modern model selection methods
Applied Linear Regression, Fourth Edition is an excellent textbook for upper-undergraduate and graduate-level students, as well as an appropriate reference guide for practitioners and applied statisticians in engineering, business administration, economics, and the social sciences.

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