Models in which the error terms do not all have the same variance are said to exhibit heteroscedasticity.

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Heteroscedasticity refers to a situation where the variance of errors is not constant across all levels of the independent variables. This violates one of the assumptions of linear regression, which assumes that the variance of errors is constant (homoscedastic) across all levels of the independent variables. Hence, models in which the error terms do not all have the same variance are said to exhibit heteroscedasticity.

Models in Which the Error Terms Do Not All Have