In deriving the OLS estimator, you minimize the sum of squared residuals with respect to the two parameters $\hat { \beta }$ ₀ and $\hat { \beta }$ ₁. The resulting two equations imply two restrictions that OLS places on the data, namely that $\sum _ { i = 1 } ^ { n } \hat { u } _ { i } = 0$ and $\sum _ { i = 1 } ^ { n } \hat { u } _ { i } X _ { i } = 0$ Show that you get the same formula for the regression slope and the intercept if you impose these two conditions on the sample regression function.

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The Answer of In deriving the OLS estimator, you minimize the sum of squared residuals with respect to the two parameters $\hat { \beta }$ ₀ and $\hat { \beta }$ ₁. The resulting two equations imply two restrictions that OLS places on the data, namely that $\sum _ { i = 1 } ^ { n } \hat { u } _ { i } = 0$ and $\sum _ { i = 1 } ^ { n } \hat { u } _ { i } X _ { i } = 0$ Show that you get the same formula for the regression slope and the intercept if you impose these two conditions on the sample regression function.

In Deriving the OLS Estimator, You Minimize the Sum of Squared