Apply the logic developed in this chapter to the model (Y_{i}=beta_{0}+beta_{1} X_{i}+) (epsilon_{mathrm{i}}). (There was no (beta_{0})

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Apply the logic developed in this chapter to the model \(Y_{i}=\beta_{0}+\beta_{1} X_{i}+\) \(\epsilon_{\mathrm{i}}\). (There was no \(\beta_{0}\) in the simplified model.) Derive the OLS estimate for \(\hat{\beta} 0\) and \(\hat{\beta}_{1}\).

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