Applied Regression - Lewis-Beck, Colin
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Présentation Applied Regression Format Broché
- Livre Science humaines et sociales, Lettres
Résumé :
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.
Biographie: 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.
Sommaire:
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