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  "body": "\n## Summary\n\nA collection of random variables is heteroscedastic when their variances are not all equal; it is homoscedastic when they are.\nIn regression the term refers to errors whose variance changes with the regressors, for example noise that grows with the size of the response.\nUnder heteroscedasticity [[ordinary least squares|YM708KHV2W]] remains unbiased but is no longer the best linear unbiased estimator.\nAlso, the usual formula for its standard errors is wrong.\nRemedies include [[weighted least squares|H5CGZHBR58]] and heteroscedasticity-consistent standard errors, and tests such as the Breusch–Pagan and White tests detect it.\nThe spelling heteroskedasticity is also common.\n\n## Explanations\n\n### L1 human\n\nPlot household spending on food against income.\nPoor households spend similar amounts, but rich households differ a lot, so the cloud of points fans out as income grows.\nThat fanning out is heteroscedasticity: the scatter around the trend is not the same everywhere.\nIt matters because a method that treats all points as equally precise will be overconfident about some conclusions.\n",
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