#Rank transformation xlstat how to#

If e is the vector of the errors of the model, the null hypothesis H0 of said error terms being of constant variance can write: This heteroscedastiicity test has been developed by Breusch and Pagan (1979), and later improved by Koenker (1981) - which is why this test is sometimes named the Breusch- Pagan and Koenker test - to allow identifying cases of heteroscedasticity, which make the classical estimators of the parameters of the linear regression unreliable. Several tests have been developed, with the following null and alternative hypotheses:

If it is suspected that the variances are not homogeneous (a representation of the residuals against the explanatory variables may reveal heteroscedasticity), it is therefore necessary to perform a test for heteroscedasticity.

#Rank transformation xlstat how to#

How to check for Heteroscedasticity in XLSTAT The estimation of their variance is not reliable. Why is heteroscedasticity a problem? In linear regression analysis, the fact that the errors of the model (also named residuals) are not homoskedastic has the consequence that the model coefficients estimated using ordinary least squares (OLS) are neither unbiased nor those with minimum variance. The concept of heteroscedasticity - the opposite being homoscedasticity - is used in statistics, especially in the context of linear regression or for time series analysis, to describe the case where the variance of errors of the model is not the same for all observations, while often one of the basic assumption in modeling is that the variances are homogeneous and that the errors of the model are identically distributed.