There are various tests for heteroscedasticity. For bi-variate data the easiest is simply plotting the data as a scatter graph. If the vertical spread of the data points is broadly the same along its range then the data are homoscedastic and if not then there is evidence of heteroscedasticity.
Heteroscedasticity may be removed using data transformations. The appropriate transformation will depend on the data and there is no general transformation that will work in all instances.
Heteroscedasticity in a dataset can be detected by visually inspecting a scatter plot of the data or by conducting statistical tests such as the Breusch-Pagan test or the White test. These tests help determine if the variance of the errors in a regression model is not constant across all levels of the independent variables.
variables
The answer depends on consistent with WHAT!
In regression analysis , heteroscedasticity means a situation in which the variance of the dependent variable varies across the data. Heteroscedasticity complicates analysis because many methods in regression analysis are based on an assumption of equal variance.
releation ship between two variable one depend other is undepended
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They are still unbiased however they are inefficient since the variances are no longer constant. They are no longer the "best" estimators as they do not have minimum variance