Heteroscedasticity

Heteroscedasticity may arise due to differences in variances of residuals across different levels of the independent variable.

The heteroscedasticity assumption was violated due to the presence of outliers in the dataset.

The analysis should account for heteroscedasticity to ensure that the results are valid and reliable.

The heteroscedasticity issue required a reevaluation of the model assumptions and data transformation techniques.

The use of robust standard errors is a common method to address heteroscedasticity in regression models.

The financial regulator warned about the risks associated with heteroscedasticity and its impact on the stability of the financial system.

Heteroscedasticity in the data made it difficult to draw conclusions about the effect of the treatment.

The use of a log transformation can help reduce heteroscedasticity in a regression model.

The market's heteroscedasticity was evident in the extreme fluctuations in the price of Bitcoin.

The heteroscedasticity in the mixed-effects model suggests that the variance of the random effects is not constant across groups.

The heteroscedasticity of the errors can be tested using statistical tests such as the Breusch-Pagan test.

Heteroscedasticity can be caused by measurement errors, omitted variables, or non-linear relationships between variables.

The heteroscedasticity in the data set was addressed by using a log transformation of the dependent variable.

The assumption of homoscedasticity is often violated in mixed-effects models due to heteroscedasticity.

The heteroscedasticity in the data was addressed using a mixed-effects model.

Heteroscedasticity in mixed-effects models can arise when the variance of the response variable differs across groups or conditions.

The heteroscedasticity in the mixed-effects model was detected using statistical tests.

The use of robust standard errors can help address the issue of heteroscedasticity in mixed-effects models.

Heteroscedasticity can lead to incorrect estimates of the standard errors and bias the results of the analysis.

The plot of the residuals suggested the presence of heteroscedasticity, which could affect the validity of the model.

The analysis showed evidence of heteroscedasticity, suggesting that the variance of the errors was not constant.

The ANOVA results were not reliable due to the heteroscedasticity of the data.