Respuesta :
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 [tex]Var (\beta j )[/tex] = σ^2 / [tex][TSS j (1 - R^2j)][/tex] ; 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
The statement that asserts a true claim regarding "Multicollinearity" would be as follows:
d). It is best measured using the statistic variance inflation factor (VIF).
"Multicollinearity"
"Multicollinearity" is described as the process in which a "greater correlation exists among two or more independent variables in a compound model of regression."
The last statement most effectively asserts that it can most adequately be estimated by employing the statistic VIF.
This will show how stronger correlations exist between the variables.
Thus, option d is the correct answer.
Learn more about "Inflation" here:
brainly.com/question/2506739
