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Applied Regression: An Introduction (Quantitative Applications in the Social Sciences) - Softcover

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9781483381473: Applied Regression: An Introduction (Quantitative Applications in the Social Sciences)

Synopsis

Known for its readability and clarity, this Second Edition of the best-selling Applied Regression 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. Authors Colin Lewis-Beck and Michael Lewis-Beck 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.

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About the Authors

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.

 

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Paperback. Condition: new. Paperback. 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. Updates to this new edition include: more coverage of regression assumptions and model fit; additional material on residual analysis; more examples of transformations; and the inclusion of the measures of tolerance and VIF within the discussion about collinearity. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781483381473

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