Known for its readability and clarity, this Second Edition provides an accessible introduction to regression analysis for social scientists and other professionals who want to model quantitative data. After covering the basic idea of fitting a straight line to a scatter of data points, the text uses clear language to explain both the mathematics and assumptions behind the simple linear regression model. The authors then cover more specialized subjects of regression analysis, such as multiple regression, measures of model fit, analysis of residuals, interaction effects, multicollinearity, and prediction. Throughout the text, graphical and applied examples help explain and demonstrate the power and broad applicability of regression analysis for answering scientific questions.
Series Editor's Introduction
Preface
Acknowledgments
About the Authors
1. Bivariate Regression: Fitting a Straight Line
2. Bivariate Regression: Assumptions and Inferences
3. Multiple Regression: The Basics
4. Multiple Regression: Special Topics
Appendix
References
Index
Known for its readability and clarity, this Second Edition provides an accessible introduction to regression analysis for social scientists and other professionals who want to model quantitative data. After covering the basic idea of fitting a straight line to a scatter of data points, the text uses clear language to explain both the mathematics and assumptions behind the simple linear regression model. The authors then cover more specialized subjects of regression analysis, such as multiple regression, measures of model fit, analysis of residuals, interaction effects, multicollinearity, and prediction. Throughout the text, graphical and applied examples help explain and demonstrate the power and broad applicability of regression analysis for answering scientific questions.
Series Editor's Introduction
Preface
Acknowledgments
About the Authors
1. Bivariate Regression: Fitting a Straight Line
2. Bivariate Regression: Assumptions and Inferences
3. Multiple Regression: The Basics
4. Multiple Regression: Special Topics
Appendix
References
Index
Series Editor′s Introduction
Preface
Acknowledgments
About the Authors
1. Bivariate Regression: Fitting a Straight Line
2. Bivariate Regression: Assumptions and Inferences
3. Multiple Regression: The Basics
4. Multiple Regression: Special Topics
Appendix
References
Index
Colin Lewis-Beck is a PhD candidate in Statistics at Iowa State
University. He holds a BA from Middlebury College and a dual
MPP/MA in Public Policy and Applied Statistics from the University
of Michigan. While at Michigan, he received an Outstanding
Teaching Award from the Department of Statistics. Also, he
has worked as a Teaching Assistant and a Computer Consultant,
during multiple summers at the Inter-University Consortium for
Political and Social Research (ICPSR) Summer Program, University of
Michigan. His research experiences in statistics are varied,
and including serving as a Statistician in the Economic Analysis
and Statistics Division of the OECD (Paris), and at STATinMED, a
health outcomes research firm in Ann Arbor, MI. His interests
are applied statistics related to social science research, causal
inference, and spatial statistics. Mr. Lewis-Beck has
co-authored papers on the quality of life and work productivity,
modeling health care costs, and technology use in educational
performance.
Michael S. Lewis-Beck is F. Wendell Miller Distinguished Professor
of Political Science at the University of Iowa, and holds a Ph.D.
from the University of Michigan. His interests are
comparative elections, election forecasting, political economy, and
quantitative methodology. He has been designated the 4th most
cited political scientist since 1940, in the field of methodology.
Professor Lewis-Beck has authored or co-authored over 240 articles
and books, including Applied Regression: An Introduction, Data
Analysis: An Introduction, Economics and Elections: The Major
Western Democracies, Forecasting Elections, The American Voter
Revisited and French Presidential Elections. He has served as
an Editor of the American Journal of Political Science, the Sage
QASS series (the green monographs) in quantitative methods and The
Sage Encyclopedia of Social Science Research Methods.
Currently he is Associate Editor of International Journal of
Forecasting and Associate Editor of French Politics. In
spring 2012, he held the position of Paul Lazersfeld University
Professor at the University of Vienna. During the fall of 2012, he
was Visiting Professor at Center for Citizenship and Democracy,
University of Leuven (KU Leuven), Belgium. In spring 2013,
Professor Lewis-Beck was Visiting Scholar, Centennial Center,
American Political Science Association, Washington, D.C.
During fall 2013, he served as Visiting Professor, Faculty of
Law and Political Science, Universidad Autónoma de Madrid, Spain.
In spring, 2014, he was Visiting Scholar, Department of Political
Science, University of Göteborg, Sweden. For fall, 2014, he
served as a Visiting Professor at LUISS University, Rome. At
present, he is co-authoring a book on how Latin Americans vote.
This is a great book to acquaint students with the world of linear
models. It is perfect to use in combination with other texts,
or as a stand-along book in introductory courses. The Lewis-Beck’s
have updated the presentation, provided additional examples, and
included more discussion of regression diagnostics. I am sure that
it will, once again, be a best seller!
*Saundra K. Schneider*
This is an excellent update and extension of a wonderfully clear
exposition of bivariate and multiple regression analysis for
beginning practitioners and students. I was a fan of the
first edition, and I am even more pleased with the revision.
*Walter J. Stone*
This is one of the best resources on basic regression techniques
available on the market today and it remains my go-to guide for my
own research. Applied Regression is the quintessential text for
graduate students pursuing degrees in the quantitative social
sciences; it has helped train several generations of social science
researchers over the course of the last four decades. The second
edition will remain instrumental in training social scientists for
years to come.
*Matt Vogel*
The new edition of Applied Regression maintains the excellence of
the original edition while modernizing and extending it. Its
highpoint is how the Lewis-Becks state everything with complete
precision. From the assumptions of OLS to the ways of coping
with outliers and to the methods of detecting multicollinearity,
the authors tell readers exactly what they need to know to perform
regression analysis.
*Herbert Weisberg*
![]() |
Ask a Question About this Product More... |
![]() |