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Applied Regression de Colin Lewis-Beck

Applied Regression

Autor Colin Lewis-Beck, Michael Lewis-Beck
en Limba Engleză Paperback – 13 iul 2015
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.
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Specificații

ISBN-13: 9781483381473
ISBN-10: 1483381471
Pagini: 120
Ilustrații: illustrations
Dimensiuni: 140 x 216 x 7 mm
Greutate: 0.16 kg
Ediția:2. Auflage
Editura: Sage Publications, Inc
Locul publicării:Thousand Oaks, United States

Recenzii

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!
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.  

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.
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.

Cuprins

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

Notă biografică

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.

Descriere

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.