4) Consider the following three regression results. (I did not print the intercepts) (10 points) : variable name variable label 1wage natural log of wage tenure years with current employer KWW knowledge of world work test score (i) Regression of log of monthly wage earnings against years of job tenure and KWW test score 1wage Coef. Std. Err. t P>It| [95% Conf. Interval] - - - - - tenure 0120699 . 0025837 4.67 0.000 0069994 0171404 KWW . 0157515 . 0017166 9.18 0.000 0123827 . 0191204 (ii) Regression of log of monthly wage earnings against years of jobe tenure only 1wage Coef. Std. Err. t P>It| [95% Conf. Interval] tenure . 0154222 . 0026693 5.78 0.000 . 0101836 . 0206608 (iii) Regression of KWW score against years of job tenure KWW | Coef. Std. Err. t P> It| [95% Conf. Interval] tenure 2128208 . 0487803 4.36 0.000 . 117089 . 3085526a) Suppose regression (i) represents the TRUE POPULATION model. What is the true effect of an extra year of job tenure on the wage? b) Explain carefully why the effect of job tenure appears to be larger in regression (ii) than it is in regression (i) . Which basic assumption of the OLS model is violated in regression (ii) and why? (Tell me what that assumption says, not just its number!) Which equation do you think is more likely to be correct? c) Write the formula that relates the job tenure parameter estimate in regression (ii) to the job tenure parameter in regression (i), and do the calculation to confirm that the formula holds, up to rounding error