Regression Diagnostics: Identifying Influential Data and Sources of Collinearity: 546 (Wiley Series in Probability and Statistics) - Tapa blanda

Acerca del autor

David A. Belsley, PhD, is Professor in the Department of Economics at Boston College in Newtonville, Massachusetts.

Edwin Kuh, PhD, is Professor in the Department of Economics at Boston College in Newtonville, Massachusetts.

Roy E. Welsch, PhD, is Professor of Statistics and Management at the Sloan School of Management at the Massachusetts Institute of Technology.

De la contraportada

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

"The title of the book more or less sums up the contents. It appears to me to represent a real breakthrough in the art of dealing in ‘unconventional’ data. . . . I found the whole book both readable and enjoyable. It is suitable for data analysts, academic statisticians, and professional software writers."
—Journal of the Royal Statistical Society

"The book assumes a working knowledge of all of the principal results and techniques used in least squares multiple regression, as expressed in vector and matrix notation. Given this background, the book is clear and easy to use. . . . The techniques are illustrated in great detail with practical data sets from econometrics."
—Short Book Reviews, International Statistical Institute

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity provides practicing statisticians and econometricians with new tools for assessing quality and reliability of regression estimates. Diagnostic techniques are developed that aid in the systematic location of data points that are unusual or inordinately influential; measure the presence and intensity of collinear relations among the regression data; and help to identify variables involved in each and pinpoint estimated coefficients potentially most adversely affected. The book emphasizes diagnostics and includes suggestions for remedial action.

De la solapa interior

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

The title of the book more or less sums up the contents. It appears to me to represent a real breakthrough in the art of dealing in 'unconventional' data. . . . I found the whole book both readable and enjoyable. It is suitable for data analysts, academic statisticians, and professional software writers.
--Journal of the Royal Statistical Society

The book assumes a working knowledge of all of the principal results and techniques used in least squares multiple regression, as expressed in vector and matrix notation. Given this background, the book is clear and easy to use. . . . The techniques are illustrated in great detail with practical data sets from econometrics.
--Short Book Reviews, International Statistical Institute

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity provides practicing statisticians and econometricians with new tools for assessing quality and reliability of regression estimates. Diagnostic techniques are developed that aid in the systematic location of data points that are unusual or inordinately influential; measure the presence and intensity of collinear relations among the regression data; and help to identify variables involved in each and pinpoint estimated coefficients potentially most adversely affected. The book emphasizes diagnostics and includes suggestions for remedial action.