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Confirm the partialling out interpretation of the OLS estimates by explicitly doing the partialling outfor Example $3.2 .$ This first requires regressing educ on exper and tenure and saving the residuals, $\hat{r}_{1}$ .Then, regress log(wage) on $\hat{r}_{1}$ . Compare the coefficient on $\hat{r}_{1}$ with the coefficient on educ in the regression of log(wage) on educ, exper, and tenure.

They are identical. See video for detailed answer.

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Chapter 3

Multiple Regression Analysis: Estimation

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we might expect the coefficients to be identical, and these estimation results show confirmed what we expected. The first equation is the result for the regression of education on expert and tenure. The second equation is the estimated results for the regression of a lot of ways. On the residual are one of Equation one, and for your reference, the third equation is equation 3.19 We have long of wage is regressed on education, expert and tenure. And, as you can see, the coefficient of R one hat in equation too. 0.92 is identical to the coefficient of education. In Equation 3.19 The regression of lock of wish on our one explains the variation in lock of which, using only the part of education that is an correlated with expert and tenure. Separate effects of expert and tenure are not included.

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