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Suppose That in Order to Estimate of the Average Effect

Question 7

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Suppose that in order to estimate of the average effect of participation in a job training program (train) on wage, you included controls for education (educ) , years of experience (exper) in your model: log(wage) = Suppose that in order to estimate of the average effect of participation in a job training program (train) on wage, you included controls for education (educ) , years of experience (exper) in your model: log(wage) = + + + + u . Which of the following is a reason why the coefficient, , might be biased? A) Exper is uncorrelated with train. B) Educ is likely correlated with both wage and train . C) Educ and exper are highly correlated with each other. D) There might be some other variable that is also correlated with both wage and train. + Suppose that in order to estimate of the average effect of participation in a job training program (train) on wage, you included controls for education (educ) , years of experience (exper) in your model: log(wage) = + + + + u . Which of the following is a reason why the coefficient, , might be biased? A) Exper is uncorrelated with train. B) Educ is likely correlated with both wage and train . C) Educ and exper are highly correlated with each other. D) There might be some other variable that is also correlated with both wage and train. Suppose that in order to estimate of the average effect of participation in a job training program (train) on wage, you included controls for education (educ) , years of experience (exper) in your model: log(wage) = + + + + u . Which of the following is a reason why the coefficient, , might be biased? A) Exper is uncorrelated with train. B) Educ is likely correlated with both wage and train . C) Educ and exper are highly correlated with each other. D) There might be some other variable that is also correlated with both wage and train. + Suppose that in order to estimate of the average effect of participation in a job training program (train) on wage, you included controls for education (educ) , years of experience (exper) in your model: log(wage) = + + + + u . Which of the following is a reason why the coefficient, , might be biased? A) Exper is uncorrelated with train. B) Educ is likely correlated with both wage and train . C) Educ and exper are highly correlated with each other. D) There might be some other variable that is also correlated with both wage and train. + Suppose that in order to estimate of the average effect of participation in a job training program (train) on wage, you included controls for education (educ) , years of experience (exper) in your model: log(wage) = + + + + u . Which of the following is a reason why the coefficient, , might be biased? A) Exper is uncorrelated with train. B) Educ is likely correlated with both wage and train . C) Educ and exper are highly correlated with each other. D) There might be some other variable that is also correlated with both wage and train. + u . Which of the following is a reason why the coefficient, Suppose that in order to estimate of the average effect of participation in a job training program (train) on wage, you included controls for education (educ) , years of experience (exper) in your model: log(wage) = + + + + u . Which of the following is a reason why the coefficient, , might be biased? A) Exper is uncorrelated with train. B) Educ is likely correlated with both wage and train . C) Educ and exper are highly correlated with each other. D) There might be some other variable that is also correlated with both wage and train. , might be biased?


A) Exper is uncorrelated with train.
B) Educ is likely correlated with both wage and train .
C) Educ and exper are highly correlated with each other.
D) There might be some other variable that is also correlated with both wage and train.

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