How do you treat heteroscedasticity in regression?

Heteroscedasticity means unequal scattered distribution.
In regression analysis, we generally talk about heteroscedasticity in the context of the error term.
Heteroscedasticity is the systematic change in the spread of the residuals or errors over the range of measured values.
Heteroscedasticity is the problem because Ordinary least squares (OLS) regression assumes that all residuals are drawn from a random population that has a constant variance.

Heteroscedasticity occurs more often in datasets, where we have a large range between the largest and the smallest observed values.
There are many reasons why heteroscedasticity can exist, and a generic explanation is that the error variance changes proportionally with a factor.

It refers to cases where we specify the correct model and let us observe the non-constant variance in residual plots.

It refers to cases where you incorrectly specify the model, and that causes the non-constant variance.
When you leave an important variable out of a model, the omitted effect is absorbed into the error term.
If the effect of the omitted variable varies throughout the observed range of data, it can produce telltale signs of heteroscedasticity in the residual plots.

How to Fix Heteroscedasticity?

If your model is a cross-sectional model that includes large differences between the sizes of the observations, you can find different ways to specify the model that reduces the impact of the size differential.
To do this, change the model from using the raw measure to using rates and per capita values.
Of course, this type of model answers a slightly different kind of question. You’ll need to determine whether this approach is suitable for both your data and what you need to learn.

It is a method that assigns each data point a weight based on the variance of its fitted value.
The idea is to give small weights to observations associated with higher variances to shrink their squared residuals.
Weighted regression minimizes the sum of the weighted squared residuals. When you use the correct weights, heteroscedasticity is replaced by homoscedasticity.