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- Regression analysis is performed on data that may have a high variance across distinct independent variable values.
- Heteroscedasticity, which denotes variable variances around the fitted values, is associated with this type of data.
- The data points are spread around the fitted line when we do regression.
- There should be as little scattering as feasible in a good regression model.
- The model is referred to as homoscedastic when the dispersion is uniform. The model is heteroscedastic if it is not.
- The form of the typical heteroscedastic distribution resembles a cone.
- Most frequently, the source of this type of cone-shaped distribution is the data itself.
- Sometimes it is quite normal for the dependent variable’s variance to change and to not remain constant across the whole dataset.
- In other words, the newest data points may show more variety than the prior data did.
- When I performed the linear regression model, heteroskedasticity was noticed in the similar way.
- Later performed Cross Validation to avoid this problem.
