When should a researcher consider transforming the explanatory variable in the simple linear regression model?
A)to estimate a coefficient on the dependent variable that matches economic theory
B)to allow non-constant marginal effects
C)to reduce variance in the dependent variable
D)to reduce (Note: the "hat" should be over the whole expression,but I can't accomplish that in my software right now)

Question

When should a researcher consider transforming the explanatory variable in the simple linear regression model?
A)to estimate a coefficient on the dependent variable that matches economic theory 
B)to allow non-constant marginal effects 
C)to reduce variance in the dependent variable 
D)to reduce (Note: the "hat" should be over the whole expression,but I can't accomplish that in my software right now)

Quizplus · Accepted Answer

Transforming the explanatory variable can be useful to allow for non-constant marginal effects. It is not typically done to estimate a coefficient that matches economic theory or to reduce variance in the dependent variable. Reducing high leverage observations, which is denoted by the expression with the "hat," is a separate issue and can sometimes be addressed by transforming variables, but this is not mentioned in the question.

When Should a Researcher Consider Transforming the Explanatory Variable in the Simple