Introduction to Linear Regression Analysis
| AUTHOR | Vining, G. Geoffrey; Montgomery, Douglas C.; Vining, G. Geoffrey et al. |
| PUBLISHER | Wiley (03/16/2021) |
| PRODUCT TYPE | Hardcover (Hardcover) |
This book presents both the conventional and less common uses of linear regression in today's cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences. The authors focused on four areas of improvement for this new edition: new exercises and data sets, new material on generalized regression techniques, the inclusion of JMP software in key places, and finally, the authors focused on carefully condensing the text where possible. This book begins with an introduction of regression and model building. This is followed by chapters on simple linear regression, multiple linear regression, model adequacy checking, transformations and weighting to correct model inadequacies, and diagnostics for leverage and influence. The book also covers polynomial and nonparametric regression models, indicator variables, and multicollinearity.
A comprehensive and current introduction to the fundamentals of regression analysis
Introduction to Linear Regression Analysis, 6th Edition is the most comprehensive, fulsome, and current examination of the foundations of linear regression analysis. Fully updated in this new sixth edition, the distinguished authors have included new material on generalized regression techniques and new examples to help the reader understand retain the concepts taught in the book.
The new edition focuses on four key areas of improvement over the fifth edition:
Introduction to Linear Regression Analysis skillfully blends theory and application in both the conventional and less common uses of regression analysis in today's cutting-edge scientific research. The text equips readers to understand the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences.
This book presents both the conventional and less common uses of linear regression in today's cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences. The authors focused on four areas of improvement for this new edition: new exercises and data sets, new material on generalized regression techniques, the inclusion of JMP software in key places, and finally, the authors focused on carefully condensing the text where possible. This book begins with an introduction of regression and model building. This is followed by chapters on simple linear regression, multiple linear regression, model adequacy checking, transformations and weighting to correct model inadequacies, and diagnostics for leverage and influence. The book also covers polynomial and nonparametric regression models, indicator variables, and multicollinearity.