Description: Heteroscedastic regression is a regression analysis where the variance of errors varies across observations. Unlike homoscedastic regression, which assumes that the variance of errors is constant, heteroscedasticity implies that this variance changes depending on the values of the independent variables. This phenomenon can arise in various situations, such as in economic models or social science studies, where data may exhibit variations in dispersion. Heteroscedasticity can affect the validity of regression model results, as it can lead to inefficient estimates and erroneous inferences about model parameters. To address this issue, techniques such as weighted least squares regression or transformations of variables can be used. Identifying heteroscedasticity is crucial, and statistical tests like the Breusch-Pagan test or White test can be employed to detect it. In summary, heteroscedastic regression is a fundamental concept in data analysis that allows for understanding and correcting variability in prediction errors, thereby ensuring the robustness of statistical models.
