It is a situation in which the variance of a variable is unequal across the range of values of the predictor variable.
It should be avoided in regression as it introduces unnecessary variance.
Heteroscedasticity is a term used in statistics and econometrics to describe a situation where the variability of the errors (residuals) in a regression model is not constant across all levels of the independent variable(s). In simpler terms, it means that the spread or dispersion of the residuals is not consistent throughout the range of the predictor variables.
In a well-behaved regression model, the residuals should have a constant variance, known as homoscedasticity. Heteroscedasticity can lead to inefficient estimates of the regression coefficients and can affect the validity of statistical inferences, such as hypothesis tests and confidence intervals.
Addressing heteroscedasticity may involve transforming the data or using weighted least squares regression techniques. Identifying and correcting heteroscedasticity is crucial for obtaining reliable and accurate results from regression analysis.