Applied Linear Regression
3rd Edition
Published on 11. February 2005
Book
Hardback
336 pages
978-0-471-66379-9 (ISBN)
Description
Applied Linear Regression, Third Edition is thoroughly updated to help students master the theory and applications of linear regression modeling. Focusing on model building, assessing fit and reliability, and drawing conclusions, the text demonstrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. To facilitate quick learning, this Third Edition stresses using graphical methods to find appropriate models and to better understand them. In that spirit, most analyses and homework problems use graphs for the discovery of structure as well as for the summarization of results. This text is an excellent tool for learning how to use linear regression analysis techniques to solve and gain insight into real-life problems.
Reviews / Votes
"...this is an excellent book which could easily be used as a course text..." (International Statistical Institute, January 2006) "Twenty years after the release of the excellent previous edition, the author has succeeded in putting together a superb and inviting third edition..." (Technometrics, August 2005)More details
Series
Edition
3., Auflage
Language
English
Place of publication
New York
United States
Publishing group
John Wiley and Sons Ltd
Target group
Professional and scholarly
Edition type
Revised edition
Illustrations
Illustrations
Dimensions
Height: 24 cm
Width: 16.1 cm
Thickness: 20 mm
Weight
567 gr
ISBN-13
978-0-471-66379-9 (9780471663799)
Schweitzer Classification
Other editions
New editions
Sanford Weisberg
Applied Linear Regression
Book
02/2014
4th Edition
Wiley
€145.50
Shipment within 15-20 days
Person
SANFORD WEISBERG, PhD, is Professor of Statistics and Director of the Statistical Consulting Service at the University of Minnesota. He has authored or coauthored three popular texts for John Wiley & Sons, Inc. and is a Fellow of the American Statistical Association.
Content
Preface.
1. Scatterplots and Regression.
2. Simple Linear Regression.
3. Multiple Regression.
4. Drawing Conclusions.
5. Weights, Lack of Fit, and More.
6. Polynomials and Factors.
7. Transformations.
8. Regression Diagnostics: Residuals.
9. Outliers and Influence.
10. Variable Selection.
11. Nonlinear Regression.
12. Logistic Regression.
Appendix A.1. Web Site.
Appendix A.2. Means and Variances of Random Variables.
Appendix A.3. Least Squares for Simple Regression.
Appendix A.4. Means and Variances of Least Squares Estimates.
Appendix A.5. Estimating E(Y/X) Using a Smoother.
Appendix A.6. A Brief Introduction to Matrices and Vectors.
Appendix A.7. Random Vectors.
Appendix A.8. Least Squares Using Matrices.
Appendix A.9. The QR Factorization.
Appendix A.10. Maximum Likelihood Estimates.
Appendix A.11. The Box-Cox Method for Transformations.
Appendix A.12. Case Deletion in Linear Regression.
References.
Author Index.
Subject Index.
1. Scatterplots and Regression.
2. Simple Linear Regression.
3. Multiple Regression.
4. Drawing Conclusions.
5. Weights, Lack of Fit, and More.
6. Polynomials and Factors.
7. Transformations.
8. Regression Diagnostics: Residuals.
9. Outliers and Influence.
10. Variable Selection.
11. Nonlinear Regression.
12. Logistic Regression.
Appendix A.1. Web Site.
Appendix A.2. Means and Variances of Random Variables.
Appendix A.3. Least Squares for Simple Regression.
Appendix A.4. Means and Variances of Least Squares Estimates.
Appendix A.5. Estimating E(Y/X) Using a Smoother.
Appendix A.6. A Brief Introduction to Matrices and Vectors.
Appendix A.7. Random Vectors.
Appendix A.8. Least Squares Using Matrices.
Appendix A.9. The QR Factorization.
Appendix A.10. Maximum Likelihood Estimates.
Appendix A.11. The Box-Cox Method for Transformations.
Appendix A.12. Case Deletion in Linear Regression.
References.
Author Index.
Subject Index.