1. Suppose you run the following regression: $y_i - \bar{y} = \hat{\beta}_1 + \hat{\beta}_2(x_i - \bar{x}) + \hat{u}_i$ What will be the value of $\hat{\beta}_1$ and $\hat{\beta}_2$.
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$\hat{\beta}_1$ is the estimated intercept and $\hat{\beta}_2$ is the estimated slope. $\hat{u}_i$ is the residual term. Step 2: Properties of Ordinary Least Squares (OLS) residuals. One of the key properties of OLS regression is that the sum of the residuals is Show more…
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