Homoskedastic

If variance of a residual (think: the error of regression models) is constant, then the variance is considered homoskedastic, which is fantastic if it’s your regression model.

Homoskedasticity implies that the data fits the regression model well, while its opposite...heteroskedasticity...implies that there’s some explanatory factor missing from the model (probably).

Homoskedasticity is an assumption that’s foundational to regression models. Many economists are regression model fans, since they have a way to try to measure cause-and-effect, along with the strength of each causing factor.