how homoskedasticity works
homoskedasticity is one assumption of linear regression modeling and data of this type work well with the least squares method if the variance of the errors around the regression line varies much the regression model may be poorly defined the opposite of homoskedasticity is heteroskedasticity just as the opposite of homogenous is heterogeneous heteroskedasticity also spelled heteroscedasticity refers to a condition in which the variance of the error term in a regression equation is not constant special considerationsa simple regression model or equation consists of four terms on the left side is the dependent variable it represents the phenomenon the model seeks to explain on the right side are a constant a predictor variable and a residual term also known as an error term the error term shows the amount of variability in the dependent variable that is not explained by the predictor variable example of homoskedasticsuppose you wanted to explain student test scores using the amount of time each student spent studying in this case the test scores would be the dependent variable and the time spent studying would be the predictor variable the error term would show the amount of variance in the test scores that was not explained by the amount of time studying if that variance is uniform or homoskedastic then that would suggest the model may be an adequate explanation for test performance that is that the amount of time spent studying explains the test scores but the variance may be heteroskedastic a plot of the error term data may show a large amount of study time corresponded very closely with high test scores but that low study time test scores varied widely and even included some very high scores this would indicate that the variance of scores was not well explained simply by the one predictor variable of the amount of time studying in this case some other factor is probably at work the model would likely need to be enhanced to identify it or them 