Regression analysis for social sciences / Alexander von Eye, Christof Schuster.

By: Eye, Alexander von
Contributor(s): Schuster, Christof
Material type: TextTextPublisher: San Diego, Calif. : Academic Press, ©1998Description: 1 online resource (xv, 386 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9780080550824; 0080550827; 1281057126; 9781281057129Subject(s): Social sciences -- Statistical methods | Regression analysis | Sciences sociales -- Méthodes statistiques | Analyse de régression | SOCIAL SCIENCE -- Essays | Regression analysis | Social sciences -- Statistical methods | Regressieanalyse | Sociaal-wetenschappelijk onderzoek | Regressionsanalyse | SozialwissenschaftenGenre/Form: Electronic books. | Electronic books. Additional physical formats: Print version:: Regression analysis for social sciences.DDC classification: 300/.01/519536 LOC classification: HA31.3 | .E94 1998ebOnline resources: Click here to access online
Contents:
Simple linear regression -- Multiple linear -- Categorical predictors -- Outlier analysis -- Residual analysis -- Polynomial regression -- Multicollinearity -- Multiple curvilinear regression -- Interaction terms in regression -- Robust regression -- Symmetric regression -- Variable selection techniques -- Regression for longitudinal data -- Piecewise regression -- Dichotomous criterion variables -- Computational issues.
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 (pages 373-380) and index.

Print version record.

Simple linear regression -- Multiple linear -- Categorical predictors -- Outlier analysis -- Residual analysis -- Polynomial regression -- Multicollinearity -- Multiple curvilinear regression -- Interaction terms in regression -- Robust regression -- Symmetric regression -- Variable selection techniques -- Regression for longitudinal data -- Piecewise regression -- Dichotomous criterion variables -- Computational issues.

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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