Answer: b. x2
Step-by-step explanation:
Now, Multicollinearity results from a linear association between 2 independent variables in a multiple regression model. We have perfect multicollinearity if, for example, the correlation between two independent variables is equal to 1 or −1.
We know the following from the text,
Correlation (y, x1) = 0.81
Correlation (y, x2) = 0.75
To resolve the issue of Multicollinearity, a variable needs to be dropped. Usually it is the variable that has a weaker correlation with the Dependent Variable.
In this case that is x2 which has a lower correlation with Y of 0.75 compared to the 0.81 that x1 has with Y.