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A crucial assumption in a linear regression model is that the error term is not correlated with the predictor variables. In general, when does this assumption break down?-When there are too many variables in the model-When important predictor variables are excluded.-The estimated standard errors of the OLS estimators are inappropriate-When the standard errors are distorted downward
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