Applied Regression: An Introduction: Quantitative Applications in the Social Sciences, cartea 22
Autor Colin Lewis-Beck, Michael S. Lewis-Becken Limba Engleză Paperback – 7 sep 2015
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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:Second Edition
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Quantitative Applications in the Social Sciences
Locul publicării:Thousand Oaks, United States
ISBN-10: 1483381471
Pagini: 120
Ilustrații: illustrations
Dimensiuni: 140 x 216 x 7 mm
Greutate: 0.16 kg
Ediția:Second Edition
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Quantitative Applications in the Social Sciences
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.
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
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
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.