These describe constancy or variation of conditional error variance across observations. A homoscedastic regression model has ; a heteroscedastic model allows this variance to depend on the observation or its covariates.
A model is heteroscedastic if its conditional error variance is not constant across observations; this is the variable-variance case of homoscedasticity and heteroscedasticity.
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Homoscedasticity and heteroscedasticity are terms used in statistics and regression analysis to describe the variability of the error terms (or residuals) in a model. Understanding these concepts is important for validating the assumptions of linear regression and ensuring the reliability of the model's results.