Contents
What is heteroscedasticity in machine learning?
Heteroscedasticity refers to data for which the variance of the dependent variable is unequal across the range of independent variables. Heteroscedasticity is the opposite of homoscedasticity. A regression model assumes a consistent variance, or homoscedasticity, across the data.
How heteroscedasticity is detected?
A formal test called Spearman’s rank correlation test is used by the researcher to detect the presence of heteroscedasticity. The researcher then fits the model to the data by obtaining the absolute value of the residual and then ranking them in ascending or descending manner to detect heteroscedasticity.
Why is heteroscedasticity important?
The existence of heteroscedasticity is a major concern in regression analysis and the analysis of variance, as it invalidates statistical tests of significance that assume that the modelling errors all have the same variance.
Why is heteroscedasticity a problem in OLS regression?
Heteroscedasticity is a problem because ordinary least squares (OLS) regression assumes that all residuals are drawn from a population that has a constant variance (homoscedasticity). To satisfy the regression assumptions and be able to trust the results, the residuals should have a constant variance.
Why does heteroscedasticity result in smaller p-values?
Heteroscedasticity tends to produce p-values that are smaller than they should be. This effect occurs because heteroscedasticity increases the variance of the coefficient estimates but the OLS procedure does not detect this increase.
When does heteroscedasticity occur in a data set?
As you can see in the above diagram, in case of homoscedasticity, the data points are equally scattered while in case of heteroscedasticity the data points are not equally scattered. Often occurs in those data sets which have a large range between the largest and the smallest observed values i.e. when there are outliers.
When does a time series model have heteroscedasticity?
Heteroscedasticity in time-series models A time-series model can have heteroscedasticity if the dependent variable changes significantly from the beginning to the end of the series. For example, if we model the sales of DVD players from their first sales in 2000 to the present, the number of units sold will be vastly different.