Which of the following is true about multicollinearity?

a. The effect of a dependent variable on another becomes difficult to isolate.

b. Regression coefficients become clearer and are easier to interpret.

c. P-values reduce significantly leading to rejection of null hypothesis.

d. It is best measured using the statistic variance inflation factor (VIF).

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

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