Regression Analysis, Linear
Sonny Rosenthal
Abstract
Sonny Rosenthal
Abstract
Linear regression is a versatile analysis. Simple linear regression shows the relationship between a dependent variable and an independent variable. Multiple regression controls for and models the effects of additional independent variables, and can include interaction effects. Hierarchical regression separates independent variables into theoretically meaningful blocks. A useful function of linear regression is to predict values of the dependent variable, although such prediction does not imply causation. Linear regression is available for a number of quantitative methods, including cross‐sectional surveys, longitudinal surveys, and experimental designs.
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Linear regression is a versatile analysis. Simple linear regression shows the relationship between a dependent variable and an independent variable. Multiple regression controls for and models the effects of additional independent variables, and can include interaction effects. Hierarchical regression separates independent variables into theoretically meaningful blocks. A useful function of linear regression is to predict values of the dependent variable, although such prediction does not imply causation. Linear regression is available for a number of quantitative methods, including cross‐sectional surveys, longitudinal surveys, and experimental designs.
Key concepts: Linear predictor function, Proper linear model, Linear regression, Segmented regression, Regression diagnostic, Regression analysis, Variables, Statistics