. regress lwage educ

Source |SS df MS Number of obs=935

-------------+---------------------------------- F(1, 933)=100.70

Model |16.13770421 16.1377042 Prob > F=0.0000

Residual |149.518579933 .160255712 R-squared=0.0974

-------------+---------------------------------- Adj R-squared=0.0964

Total |165.656283934 .177362188 Root MSE=.40032

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lwage |Coef. Std. Err. t P>|t|[95% Conf. Interval]

-------------+----------------------------------------------------------------

educ |.0598392.005963110.030.000.0481366.0715418

_cons |5.973063.081373773.400.0005.8133666.132759

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Using Wooldrige's"WAGE2.DTA", estimate a regression of log wages only on education.

Part a.) Report your regression results - OLS coefficients, standard errors, sample size and R^2.

Part b.) Interpret the estimated beta on education. In this case do you think your estimate of the beta on education is close to the population value? Why or why not?

Part c.) Suppose your academic advisor told you that one additional year of education increases monthly earnings by 6%, and you test whether this claim is supported by our data. Formally state the null hypothesis and consider both a two-sided alternative and a right tailed alternative.Using the critical value rejection rule and p-value rejection rule for both alternative hypotheses,do you reject the null hypothesis in both cases at the alpha=5% significance level?Explain.Hint:you will need to use display invttail(df,p) and display ttail(df,t) commands as in lecture.