Question 23Is the assumption of constant error variance (homoskedasticity) valid for Model 2? (Yes / No)

To determine whether the assumption of constant error variance (homoskedasticity) is valid for Model 2, we typically need to examine the residuals of the model. Homoskedasticity means that the variance of the errors is constant across all levels of the independent variable(s).

1. Examine Residuals: Generate residual plots by plotting the residuals against the predicted values or independent variables.

2. Check for Patterns: In a homoskedastic model, the residuals should show no systematic pattern; they should be randomly distributed around zero.

3. Statistical Tests: Conduct statistical tests, such as the Breusch-Pagan test or White test, which formally test the null hypothesis of homoskedasticity against the alternative hypothesis of heteroskedasticity.

4. Conclusion: If the residuals exhibit a constant spread and the statistical tests indicate no evidence of heteroskedasticity, then the assumption is valid. If patterns appear or deviations in variance are noted, then the assumption is violated.

Final Answer

Without specific residual data or results from residual diagnostic tests for Model 2, it is impossible to definitively answer whether the assumption of homoskedasticity is valid. Thus, consult the residual analysis results to conclude accurately (Yes / No).