Collinearity is concerned with linear relationships among the X variables. linea
ID: 3220228 • Letter: C
Question
Collinearity is concerned with
linear relationships among the X variables.
linear relationships in the error term.
the correlation between Y and b1.
transformations made to Y.
Question 2
Which of the following can occur if collinearity is a problem?
Question 3
The variable X1=Household Income was one of 5 X variables being used in a regression analysis. When Household Income was used as the dependent variable and the other 4 X variables were used as the independent variables the resulting model had an R2 of 0.75.
What is the Variance Inflation Factor for Household Income?
Question 4
Principal Component Analysis produces linear combinations of the X variables that are uncorrelated.
linear relationships among the X variables.
Explanation / Answer
1)Linear relationships among the X variables.
Correlation between independent variables causes Collinearity
2)The standard error of the estimates of the coefficients of the X variables will be large. A significant relationship can be hidden.
MULTI-co-linearity increases SE, which makes some coefficients statistically insignificant when infact they are significant.
3) VIF = 1/(1-R^2) = 1/(1-.75) = 1/.25 = 4
4)
TRUE. Principal Component Analysis uses correlation structure of original variables and derives linear combinations which are uncorrelated.