
Multiple Regression
A Primer
Paul D. Allison(Author)
SAGE Publications Inc (Publisher)
1st Edition
Published on 10. February 1999
Book
Paperback/Softback
224 pages
978-0-7619-8533-4 (ISBN)
Description
Presenting topics in the form of questions and answers, this popular supplemental text offers a brief introduction on multiple regression on a conceptual level. Author Paul D. Allison answers the most essential questions (such as how to read and interpret multiple regression tables and how to critique multiple regression results) in the early chapters, and then tackles the less important ones (for instance, those arising from multicollinearity) in the later chapters. With this organization, readers can stop at the end of any chapter and still feel like they've already gotten the meat of the subject.
Reviews / Votes
"I find the concept for this approach to teaching undergraduate research methods and statistics innovative and exciting. The idea of 'core' texts supplemented by satellite volumes covering specific, important topics makes absolute sense." -- Barbara HeylMore details
Series
Language
English
Place of publication
Thousand Oaks
United States
Target group
College/higher education
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 13 mm
Weight
354 gr
ISBN-13
978-0-7619-8533-4 (9780761985334)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
Paul D. Allison, Ph.D., is Professor of Sociology at the University of Pennsylvania where he teaches graduate courses in methods and statistics. He is also the founder and president of Statistical Horizons LLC which offers short courses on a wide variety of statistical topics.
After completing his doctorate in sociology at the University of Wisconsin, he did postdoctoral study in statistics at the University of Chicago and the University of Pennsylvania. He has published eight books and more than 60 articles on topics that include linear regression, log-linear analysis, logistic regression, structural equation models, inequality measures, missing data, and survival analysis.
Much of his early research focused on career patterns of academic scientists. At present, his principal research is on methods for analyzing longitudinal data, especially those for determining the causes and consequences of events, and on methods for handling missing data.
A former Guggenheim Fellow, Allison received the 2001 Lazarsfeld Award for distinguished contributions to sociological methodology. In 2010 he was named a Fellow of the American Statistical Association. He is also a two-time winner of the American Statistical Association's award for "Excellence in Continuing Education."
After completing his doctorate in sociology at the University of Wisconsin, he did postdoctoral study in statistics at the University of Chicago and the University of Pennsylvania. He has published eight books and more than 60 articles on topics that include linear regression, log-linear analysis, logistic regression, structural equation models, inequality measures, missing data, and survival analysis.
Much of his early research focused on career patterns of academic scientists. At present, his principal research is on methods for analyzing longitudinal data, especially those for determining the causes and consequences of events, and on methods for handling missing data.
A former Guggenheim Fellow, Allison received the 2001 Lazarsfeld Award for distinguished contributions to sociological methodology. In 2010 he was named a Fellow of the American Statistical Association. He is also a two-time winner of the American Statistical Association's award for "Excellence in Continuing Education."
Content
What Is Multiple Regression?
How Do I Interpret Multiple Regression Results?
What Can Go Wrong with Multiple Regression?
How Do I Run a Multiple Regression?
How Does Bivariate Regression Work?
What Are the Assumptions of Multiple Regression?
What Can Be Done about Multicollinearity?
How Can Multiple Regression Handle Nonlinear Relationships?
How Is Multiple Regression Related to Other Statistical Techniques?
How Do I Interpret Multiple Regression Results?
What Can Go Wrong with Multiple Regression?
How Do I Run a Multiple Regression?
How Does Bivariate Regression Work?
What Are the Assumptions of Multiple Regression?
What Can Be Done about Multicollinearity?
How Can Multiple Regression Handle Nonlinear Relationships?
How Is Multiple Regression Related to Other Statistical Techniques?