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For two or more dependent variables, use multivariate-regression.

Linear regression models a variable (the "dependent variable") as varying randomly with respect to a linear combination of other variables (the "independent variables"). Multiple regression includes two or more non-constant independent variables (whence, three or more variables in toto). This adds complications not present with only one independent variable, including complex forms of correlation and interaction effects.

Use this multiple-regression tag instead of the more generic regression tag when your question focuses on an issue specifically related to including two or more independent variables in a regression model.

Multiple regression concerns the so-called "general linear model," not to be confused with the generalized linear model despite the close similarity of their names.

For two or more dependent variables, use multivariate-regression.

Linear regression models a variable (the "dependent variable") as varying randomly with respect to a linear combination of other variables (the "independent variables"). Multiple regression includes two or more non-constant independent variables (three or more variables in total). This adds complications not present with only one independent variable, including complex forms of correlation and interaction effects.

Use this multiple-regression tag instead of the more generic regression tag when your question focuses on an issue specifically related to including two or more independent variables in a regression model.

Multiple regression concerns the so-called "general linear model," not to be confused with the generalized linear model despite the close similarity of their names.

Linear regression models a variable (the "dependent variable") as varying randomly with respect to a linear combination of other variables (the "independent variables"). Multiple regression includes two or more non-constant independent variables (whence, three or more variables in toto). This adds complications not present with only one independent variable, including complex forms of correlation and interaction effects.

Use this multiple-regression tag instead of the more generic regression tag when your question focuses on an issue specifically related to including two or more independent variables in a regression model.

Multiple regression concerns the so-called "general linear model," not to be confused with the generalized linear model despite the close similarity of their names.

For two or more dependent variables, use multivariate-regression.

Linear regression models a variable (the "dependent variable") as varying randomly with respect to a linear combination of other variables (the "independent variables"). Multiple regression includes two or more non-constant independent variables (whence, three or more variables in toto). This adds complications not present with only one independent variable, including complex forms of correlation and interaction effects.

Use this multiple-regression tag instead of the more generic regression tag when your question focuses on an issue specifically related to including two or more independent variables in a regression model.

Multiple regression concerns the so-called "general linear model," not to be confused with the generalized linear model despite the close similarity of their names.

Linear regression models a variable (the "dependent variable") as varying randomly with respect to a linear combination of other variables (the "independent variables"). Multiple regression includes two or more non-constant independent variables (whence, three or more variables in toto). This adds complications not present with only one independent variable, including complex forms of correlation and interaction effects.

Use this multiple-regression tag instead of the more generic regression tag when your question focuses on an issue specifically related to including two or more independent variables in a regression model.

Linear regression models a variable (the "dependent variable") as varying randomly with respect to a linear combination of other variables (the "independent variables"). Multiple regression includes two or more non-constant independent variables (whence, three or more variables in toto). This adds complications not present with only one independent variable, including complex forms of correlation and interaction effects.

Use this multiple-regression tag instead of the more generic regression tag when your question focuses on an issue specifically related to including two or more independent variables in a regression model.

Multiple regression concerns the so-called "general linear model," not to be confused with the generalized linear model despite the close similarity of their names.