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

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Linear Modelinghard

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