Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In robust statistics, robust regression is a form of regression analysis designed to circumvent some limitations of traditional parametric and non-parametric methods. In particular, least squares estimates for regression models are highly non-robust to outliers. One instance in which robust estimation should be considered is when there is a strong suspicion of heteroskedasticity. In the homoskedastic model, it is assumed that the variance of the error term is constant for all values of x. Heteroskedasticity allows the variance to be dependent on x, which is more accurate for many real scenarios. For example, the variance of expenditure is often larger for individuals with higher income than for individuals with lower incomes. Software packages usually default to a homoskedastic model, even though such a model may be less accurate than a heteroskedastic model.