In Linear Regression Residual Analysis heteroscedastic results mean that the variance in errors is not consistent (see: Graph 1 and 2), which is what a good linear regression model should show — a good random scattering, showing no particular pattern. This is called, homoscedasticity (see: Graph 3).
If your residual analysis results look like this then the model is not a good fit. To fix this, one could perform a data transform, or add a variable to the model to help account for what is the cause between the relationship of errors and input values.
In the example above for Graph 1 and 2, this could be the number of people at a table or the time of day — since larger groups sometimes tip less because they assume everyone else will tip, or people are more generous later in day after some vino in the evening!
Now that’s what I call statistical bombasticity!