Is Heteroscedasticity a Problem?

Sarah Jenkins
Sarah Jenkins
• • 6 min read

Heteroscedasticity Is a Problem Because Ordinary Least Squares (Ols) Regression Assumes That All Residuals Are Drawn from a Population That Has a Constant...

Is Heteroscedasticity a Problem?

Heteroscedasticity is a problem because ordinary least squares (OLS) regression assumes that all residuals are drawn from a population that has a constant variance (homoscedasticity

homoscedasticity
In statistics, a sequence (or a vector) of random variables is homoscedastic/ˌhoʊmoʊskəˈdæstɪk/ if all its random variables have the same finite variance. This is also known as homogeneity of variance. The complementary notion is called heteroscedasticity.
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). To satisfy the regression assumptions and be able to trust the results, the residuals should have a constant variance.

Is Heteroscedasticity a problem in logistic regression?

1 Answer. You're right - homoscedasticity (residuals at each level of the predictor have the same variance), is not an assumption in logistic regression. However, the binary response in logistic regression is heteroscedastic (0 or 1) which is why a corresponding estimator should be consistent with it.

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What are the bad consequences of Heteroscedasticity?

The OLS estimators and regression predictions based on them remains unbiased and consistent. The OLS estimators are no longer the BLUE (Best Linear Unbiased Estimators) because they are no longer efficient, so the regression predictions will be inefficient too.

Sarah Jenkins

Sarah Jenkins

Senior Technology Editor & AI Specialist
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Sarah Jenkins is a veteran tech journalist with over 12 years of experience covering artificial intelligence, mobile innovations, and digital ethics. Her insights have appeared in leading technology publications worldwide.