
Regression Analysis by Example
Wiley (Publisher)
3rd Edition
Published on 29. November 1999
Book
Hardback
XVI, 368 pages
978-0-471-31946-7 (ISBN)
Description
While regression analysis methods may appear simple in theory, applying them successfully requires a careful balance of theoretical results, empirical rules, and judgement. This volume bridges the gap between theory and practice, offering guidance for practitioners who need to use sophisticated statistical methodology but who may not be well-versed in mathematics or mathematical statistics.
More details
Series
Edition
3., Aufl.
Language
English
Place of publication
New York
United States
Publishing group
John Wiley and Sons Ltd
Target group
College/higher education
Professional and scholarly
Edition type
Revised edition
Illustrations
illustrations
Dimensions
Height: 24.5 cm
Width: 16.2 cm
Weight
652 gr
ISBN-13
978-0-471-31946-7 (9780471319467)
Schweitzer Classification
Other editions
Previous edition
Samprit Chatterjee | Bertram Price
Regression Analysis by Example
Book
08/1991
2nd Edition
Wiley
€129.00
Article exhausted; check for reprint
Persons
SAMPRIT CHATTERJEE, PhD, is Professor of Statistics at New York University. A well-known research scientist and Fulbright scholar, Dr. Chatterjee has coauthored Sensitivity Analysis in Linear Regression (with Dr. Hadi) and A Casebook for a First Course in Statistics and Data Analysis, both available from Wiley. ALI S. HADI, PhD, is the Stephen H. Weiss Presidential Fellow and Professor of Statistical and Computing Sciences at Cornell University. The author/coauthor of three other books, Dr. Hadi is a Fellow of the American Statistical Association and an elected member of the International Statistical Institute. BERTRAM PRICE, PhD, is President of Price Associates, a consulting firm in Washington D.C. Dr. Price's experience spans both academia and business, including work as a business scientist at IBM, and he has served as an associate editor of The American Statistician.
Content
Simple Linear Regression; Multiple Linear Regression; Regression Diagnostics: Detection of Model Violations; Qualitative Variables as Predictors; Transformation of Variables; Weighted Least Squares; The Problem of Correlated Errors; Analysis of Collinear Data; Biased Estimation of Regression Coefficients; Variable Selection Procedures; Logistic Regression; Appendix; References; Index.