What Is a Biased Estimator In Linear Regression? In linear regression, estimator biasness refers to the systematic deviation of the estimated coefficients from their true population values. A biased estimator consistently produces estimates that, on average, differ from the true values in a predictable manner. In the context of linear regression, the biases of an estimator can occur for different reasons: Omitted Variable Bias: If relevant variables are excluded from the regression model, the estimated coefficients may be biased. Omitted variable bias arises when the omitted variables are correlated with both the independent variables included in the model and the

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