MULTICOLLINEARITY implies high correlation between independent variables. It is ONE of the assumptions in linear and LOGISTIC regression. It can be identified by looking at VIF score of variables. VIF > 2.5 implies moderate collinearity issue. VIF >5 is considered as high collinearity.

It can be handled by iterative process : first step - remove VARIABLE having highest VIF and then CHECK VIF of remaining variables. If VIF of remaining variables > 2.5, then follow the same first step until VIF < =2.5

Multicollinearity implies high correlation between independent variables. It is one of the assumptions in linear and logistic regression. It can be identified by looking at VIF score of variables. VIF > 2.5 implies moderate collinearity issue. VIF >5 is considered as high collinearity.

It can be handled by iterative process : first step - remove variable having highest VIF and then check VIF of remaining variables. If VIF of remaining variables > 2.5, then follow the same first step until VIF < =2.5