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Why is homogeneity of variance (homoscedasticity) important in statistical analysis?
Why is homogeneity of variance (homoscedasticity) important in statistical analysis?
- It ensures that the errors in prediction are consistent across different values of the independent variable (correct)
- It allows errors in prediction to vary widely across different values of the independent variable
- It has no impact on the accuracy of statistical analysis
- It makes statistical analysis more complex and difficult to interpret
What does homogeneity of variance (homoscedasticity) refer to?
What does homogeneity of variance (homoscedasticity) refer to?
- The changing size of the error in prediction across different values of the independent variable
- The size of the error in prediction being dependent on the dependent variable
- The complete absence of errors in prediction across different values of the independent variable
- The consistent size of the error in prediction across different values of the independent variable (correct)
What happens when homoscedasticity is violated in statistical analysis?
What happens when homoscedasticity is violated in statistical analysis?
- The assumptions underlying many statistical tests are violated (correct)
- The accuracy of predictions becomes more reliable
- The analysis becomes less prone to errors
- The impact on statistical analysis is negligible
How does heteroscedasticity differ from homoscedasticity?
How does heteroscedasticity differ from homoscedasticity?
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