Contents
What do Standardised residuals show?
The standardized residual is a measure of the strength of the difference between observed and expected values. It’s a measure of how significant your cells are to the chi-square value. If your sample is large enough, the standardized residual can be roughly compared to a z-score.
What is heteroscedasticity of residuals?
Heteroskedasticity refers to situations where the variance of the residuals is unequal over a range of measured values. When running a regression analysis, heteroskedasticity results in an unequal scatter of the residuals (also known as the error term).
What happens if heteroskedasticity is present?
When heteroscedasticity is present in a regression analysis, the results of the analysis become hard to trust. Specifically, heteroscedasticity increases the variance of the regression coefficient estimates, but the regression model doesn’t pick up on this.
Why is heteroscedasticity a problem in OLS regression?
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). To satisfy the regression assumptions and be able to trust the results, the residuals should have a constant variance.
Which is the best example of heteroscedasticity?
What Causes Heteroscedasticity? 1 Heteroscedasticity in cross-sectional studies. Cross-sectional studies often have very small and large values and, thus, are more likely to have heteroscedasticity. 2 Heteroscedasticity in time-series models. 3 Example of heteroscedasticity. 4 Pure versus impure heteroscedasticity.
Why does heteroscedasticity result in smaller p-values?
Heteroscedasticity tends to produce p-values that are smaller than they should be. This effect occurs because heteroscedasticity increases the variance of the coefficient estimates but the OLS procedure does not detect this increase.
What’s the difference between pure and impure heteroscedasticity?
Pure heteroscedasticity refers to cases where you specify the correct model and yet you observe non-constant variance in the residual plots. Impure heteroscedasticity refers to cases where you incorrectly specify the model, and that causes the non-constant variance.