Regression analysis for social sciences
Regression analysis Social sciences e-böcker
Academic Press
1998
EISBN 9780080550824
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.
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.
