What is the difference between autocolleration and heteroscedasticity?
Autocorrelation is the correlation between the values of a time series at different points in time. It occurs when there is a relationship between the current observed value and past observed values of a time series. Autocorrelation is a problem because it violates the assumption of independence of the residuals in regression analysis.
Heteroscedasticity, on the other hand, refers to the unequal variance of the residuals (errors) in a regression model. It occurs when the variance of the errors is not constant across the range of values of the independent variable. Heteroscedasticity is a problem because it violates the assumption of equal variance of the residuals in regression analysis.
In summary, autocorrelation relates to the relationship between the values of a time series, while heteroscedasticity relates to the variance of the errors in a regression model.