The book investigates the impact of deviations from underlying assumptions on the results of linear regression models. It addresses the critical need for understanding how model sensitivity affects the reliability of statistical inferences, particularly when assumptions such as normality, homoscedasticity, and independence of errors are violated. The work provides methods for assessing the robustness of regression coefficients and predictions to changes in data or model specification.

This work is recognized as a specialized text for statisticians and researchers dealing with the practical application of linear regression. Its focus on sensitivity analysis makes it a valuable resource for understanding the robustness of statistical findings. The book is likely to be appreciated by those who need to rigorously validate their models and ensure the reliability of their conclusions in the face of potential data or assumption issues.