Ononot — In a simple linear regression Yi = \beta0 + \beta1 Xi + \eps

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Linear Modelinghard

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In a simple linear regression $Y_i = \beta_0 + \beta_1 X_i + \epsilon_i$ , suppose the error term $\epsilon_i$ has variance $Var(\epsilon_i) = \sigma^2 X_i^2$ . Which statement correctly describes the implication for the Ordinary Least Squares (OLS) estimator $\hat{\beta}_1$ ?