Heteroskedastic
Candlefocus EditorHeteroskedasticity can cause a problem in a regression analysis, because it means that the estimated standard errors will not be reliable. The variance of the error terms will be over- or underestimated, creating an inaccurate measure of the regression parameters. This can lead to the conclusion of false relationships between the independent and dependent variables, such as a false-positive or a false-negative.
Fortunately, there are a few techniques in statistics used to detect and diagnose heteroscedasticity. The most common methods are the use of graphical techniques, such as boxplots and scatterplots, as well as tests of independence, autocorrelation, and other similar tests. Once the presence of heteroskedasticity is determined, several ways of fixing it exist, depending on the degree of violation of the homoscedasticity assumption.
One of the most commonly used methods to deal with heteroskedasticity is to use weighted least squares regression. This method weights each value in the dataset according to the size of the variance, allowing unequal variances of error terms to be taken into consideration when producing the final estimates of regression parameters. In addition to weighted least squares, other methods such as Generalized Least Squares, Ridit Analysis, and White's test are often used to test for heteroskedasticity.
Heteroskedasticity is an important concept to understand in order to ensure the accuracy of an analysis and its conclusions. Proper detection and fixing of heteroskedasticity is critical in order to obtain reliable results from regression analysis. By using the methods mentioned above, researchers can better mitigate its effects and protect the accuracy of their analyses.