Question

Regression and Multicollinearity

When multiple independent variables are used to predict a dependent variable in multiple regression, multicollinearity among the independent variables is often a concern. What is the main problem caused by high multicollinearity among the independent variables in a multiple regression equation? Can you still achieve a high r for your regression equation if multicollinearity is present in your data?

Regression and Multicollinearity

When multiple independent variables are used to predict a dependent variable in multiple regression, multicollinearity among the independent variables is often a concern. What is the main problem caused by high multicollinearity among the independent variables in a multiple regression equation? Can you still achieve a high r for your regression equation if multicollinearity is present in your data?

Answer 1

1

Multicollinearity makes it extreme to evaluate autonomous or separate relapse coefficients for corresponded factors. This is on the grounds that the autonomous factors important to the examination group are very related with one another. In this regard the short response to the inquiry "Can a relapse condition produce a high r2 measurement when multicollinearity is available in the information which has been caught?" is "No". The real effect of multicollinearity is felt most intensely in the factual essentialness of the relapse coefficients present in the yield when the relapse condition is connected. In this way, it would be improbable (if not illogical) for an informational collection described by multicollinearity to create a high 12 measurement in relapse investigation. Nonetheless, on the grounds that generally couple of connections between factors can be known with conviction (no mistake) our reaction to this talk question leaves the window open for irregularities of "practice" that "hypothesis" seems to close down.