Heteroscedasticity

Summary

A collection of random variables is heteroscedastic when their variances are not all equal; it is homoscedastic when they are. In regression the term refers to errors whose variance changes with the regressors, for example noise that grows with the size of the response. Under heteroscedasticity ordinary least squares remains unbiased but is no longer the best linear unbiased estimator. Also, the usual formula for its standard errors is wrong. Remedies include weighted least squares and heteroscedasticity-consistent standard errors, and tests such as the Breusch–Pagan and White tests detect it. The spelling heteroskedasticity is also common.