Answer:
d. It is best measured using the statistic variance inflation factor (VIF).
Explanation:
Multicollinearity is an important issue in multiple regression model, having many independent/ explanatory variables. Multicollinearity is the situation in which two or more independent variables are highly correlated. It is problematic because it increases the standard error of independent variable coefficient & undermines its statistical significance
Variance Inflation Factor [VIF] is a check & corrective measure of multicollinearity.
- VIF as a multicollinearity check : It quantifies the correlation between one explanatory variable with other explanatory variables.VIF = 1 implies there is no multicollinearity (correlation between independent variables); VIF upto 5 implies there is moderate multicollinearity (correlation between independent variables). VIF > 5 implies high multicollinearity (correlation between independent variables)
- VIF as a multicollinearity correction : Calculating
= σ^2 /
; where TSS = total sum of square of variable j , σ^2 = j variance, R^2 j = R^2 from regressing all other independent variable on variable j