Introduced on day 11
Heteroscedasticity
Error variance that changes with the fitted value or an input, so the residual plot fans out instead of forming an even band.
In detail
Least-squares coefficients stay unbiased, but classical standard errors and constant-width prediction intervals become wrong: too narrow where the spread is large. Typical for counts and money, where errors scale with size. Remedies are heteroscedasticity-consistent (robust) standard errors, a log or ratio target, weighted fitting, or intervals built per segment.