Applied Linear Regression

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John Wiley & Sons, Nov 25, 2013 - Mathematics - 368 pages

Praise for the Third Edition

"...this is an excellent book which could easily be used as a course text..."
—International Statistical Institute

The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples.

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 analysis
  • In-depth coverage of parameter estimates in both simple and complex models, transformations, and regression diagnostics
  • Newly added material on topics including testing, ANOVA, and variance assumptions
  • Updated 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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Copyright page
Multiple Regression
Interpretation of MainEffects
Complex Regressors
Variable Selection
Nonlinear Regression
Binomial and Poisson Regression
A Brief Introduction to Matrices and Vectors
Author Index

Regression Diagnostics

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About the author (2013)

SANFORD WEISBERG, PhD, is Professor of Statistics and Director of the Statistical Consulting Service in the School of Statistics at the University of Minnesota. He is also a coauthor of Applied Regression Including Computing and Graphics and An Introduction to Regression Graphics, both published by Wiley.