Description: Heteroscedasticity test is a fundamental statistical tool in regression analysis used to assess whether the variance of errors in a regression model is constant across all observations. In an ideal regression model, it is expected that the errors, or residuals, are normally distributed, implying that the variance of these errors should not change with the level of the independent variable. Heteroscedasticity, on the other hand, refers to the situation where this variance is not constant, which can lead to inefficient and biased estimates of the model parameters. Detecting heteroscedasticity is crucial because it can affect the validity of statistical inferences, such as confidence intervals and hypothesis tests. There are various tests to identify heteroscedasticity, such as the Breusch-Pagan test and the White test, which analyze the relationship between residuals and independent variables. Identifying heteroscedasticity allows analysts to adjust their models using techniques such as robust regression or data transformations to improve the accuracy and reliability of their results.
