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Regression analysis for social sciences [electronic resource] / Alexander von Eye, Christof Schuster.

By: Eye, Alexander von.
Contributor(s): Schuster, Christof.
Material type: TextTextPublisher: San Diego, Calif. : Academic Press, c1998Description: 1 online resource (xv, 386 p.) : ill.ISBN: 9780080550824 (electronic bk.); 0080550827 (electronic bk.); 1281057126; 9781281057129.Subject(s): Social sciences -- Statistical methods | Regression analysis | Sciences sociales -- M�ethodes statistiques | Analyse de r�egression | SOCIAL SCIENCE -- Essays | Regressieanalyse | Sociaal-wetenschappelijk onderzoekGenre/Form: Electronic books.Additional physical formats: Print version:: Regression analysis for social sciences.DDC classification: 300/.01/519536 Online resources: EBSCOhost
Contents:
Preface; CHAPTER 1. INTRODUCTION; CHAPTER 2. SIMPLE LINEAR REGRESSION; CHAPTER 3. MULTIPLE LINEAR REGRESSION; CHAPTER 4. CATEGORICAL PREDICTORS; CHAPTER 5. OUTLIER ANALYSIS; CHAPTER 6. RESIDUAL ANALYSIS; CHAPTER 7. POLYNOMIAL REGRESSION; CHAPTER 8. MULTICOLLINEARITY; CHAPTER 9. MULTIPLE CURVILINEAR REGRESSION; CHAPTER 10. INTERACTION TERMS IN REGRESSION; CHAPTER 11. ROBUST REGRESSION; CHAPTER 12. SYMMETRIC REGRESSION; CHAPTER 13. VARIABLE SELECTION TECHNIQUES; CHAPTER 14. REGRESSION FOR LONGITUDINAL DATA; CHAPTER 15. PIECEWISE REGRESSION; CHAPTER 16. DICHOTOMOUS CRITERION VARIABLES
Summary: Regression Analysis for Social Sciences presents methods of regression analysis in an accessible way, with each method having illustrations and examples. A broad spectrum of methods are included: multiple categorical predictors, methods for curvilinear regression, and methods for symmetric regression. This book can be used for courses in regression analysis at the advanced undergraduate and beginning graduate level in the social and behavioral sciences. Most of the techniques are explained step-by-step enabling students and researchers to analyze their own data. Examples include data from the.
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Includes bibliographical references (p. 373-380) and index.

Description based on print version record.

Preface; CHAPTER 1. INTRODUCTION; CHAPTER 2. SIMPLE LINEAR REGRESSION; CHAPTER 3. MULTIPLE LINEAR REGRESSION; CHAPTER 4. CATEGORICAL PREDICTORS; CHAPTER 5. OUTLIER ANALYSIS; CHAPTER 6. RESIDUAL ANALYSIS; CHAPTER 7. POLYNOMIAL REGRESSION; CHAPTER 8. MULTICOLLINEARITY; CHAPTER 9. MULTIPLE CURVILINEAR REGRESSION; CHAPTER 10. INTERACTION TERMS IN REGRESSION; CHAPTER 11. ROBUST REGRESSION; CHAPTER 12. SYMMETRIC REGRESSION; CHAPTER 13. VARIABLE SELECTION TECHNIQUES; CHAPTER 14. REGRESSION FOR LONGITUDINAL DATA; CHAPTER 15. PIECEWISE REGRESSION; CHAPTER 16. DICHOTOMOUS CRITERION VARIABLES

Regression Analysis for Social Sciences presents methods of regression analysis in an accessible way, with each method having illustrations and examples. A broad spectrum of methods are included: multiple categorical predictors, methods for curvilinear regression, and methods for symmetric regression. This book can be used for courses in regression analysis at the advanced undergraduate and beginning graduate level in the social and behavioral sciences. Most of the techniques are explained step-by-step enabling students and researchers to analyze their own data. Examples include data from the.

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