Sensitivity Analysis in Linear Regression

Treats linear regression diagnostics as a tool for application of linear regression models to real-life data. Presentation makes extensive use of examples to illustrate theory. Assesses the effect of measurement errors on the estimated coefficients, which is not accounted for in a standard least squares estimate but is important where regression coefficients are used to apportion effects due to different variables. Also assesses qualitatively and numerically the robustness of the regression fit. The Prediction Matrix.

Role of Variables in a Regression Equation.

Effects of an Observation on a Regression Equation.

Assessing the Influence of Multiple Observations.

Joint Impact of a Variable and an Observation.

Assessing the Effect of Errors of Measurements.

A Study of Model Sensitivity by the Generalized Linear ModelApproach.

Computational Considerations.

Appendix: Summary of Vector and Matrix Norms, Proofs of ThreeTheorems.

Refernces.

Index.

SAMPRIT CHATTERJEE, PhD, is Professor Emeritus of Statistics at New York University. A Fellow of the American Statistical Association, Dr. Chatterjee has been a Fulbright scholar in both Kazhakstan and Mongolia. He is the coauthor of Sensitivity Analysis in Linear Regression and A Casebook for a First Course in Statistics and Data Analysis, both published by Wiley.

ALI S. HADI, PhD, is a Distinguished University Professor and former vice provost at the American University in Cairo (AUC). He is the founding Director of the Actuarial Science Program at AUC. He is also a Stephen H. Weiss Presidential Fellow and Professor Emeritus at Cornell University. Dr. Hadi is the author of four other books, a Fellow of the American Statistical Association, and an elected Member of the International Statistical Institute.