Yearly Archives: 2023

Multicollinearity in regression analysis occurs when two or more predictor variables are highly correlated to each other, such that they do not provide unique or independent information in the regression model. Multicollinearity exists whenever an independent variable is highly correlated with one or more of the other independent variables in a multiple regression equation. Multicollinearity […]

Multicollinearity in regression analysis occurs when two or more predictor variables are highly correlated to each other, such that they do not provide unique or independent information in the regression model. Multicollinearity exists whenever an independent variable is highly correlated with one or more of the other independent variables in a multiple regression equation. Multicollinearity […]

Multicollinearity prevents predictive models from producing accurate predictions by increasing model complexity and overfitting. To reduce the amount of multicollinearity found in a statistical model, one can remove the specific variables identified as the most collinear. You can also try to combine or transform the offending variables to lower their correlation. If that does not […]

Multicollinearity prevents predictive models from producing accurate predictions by increasing model complexity and overfitting. To reduce the amount of multicollinearity found in a statistical model, one can remove the specific variables identified as the most collinear. You can also try to combine or transform the offending variables to lower their correlation. If that does not […]