 Homoscedasticity and heteroscedasticity

These describe constancy or variation of conditional error variance across observations. A homoscedastic regression model has $\mathrm{Var}({\epsilon }_i\mid X)={\sigma }^2$; a heteroscedastic model allows this variance to depend on the observation or its covariates.

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.