AI engineering glossary · Statistics inference
What is heteroscedasticity?
Heteroscedasticity: A situation in which the spread of a regression model's errors changes with the predictors, often widening as values grow. It leaves coefficients unbiased but makes usual standard errors wrong.
Heteroscedasticity: an example
Errors in predicting salary are small for freshers but large for senior managers, giving a fan-shaped residual plot.
Related terms
More Statistics inference terms
- Homoscedasticity
- Instrumental variable
- Interaction term
- Jackknife
- Kolmogorov-Smirnov test
- Likelihood function
- Likelihood ratio test
- Log-likelihood
Where you learn it
Heteroscedasticity is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.