![]() The residuals appear to be randomly scatted around zero and don’t exhibit any noticeable patterns, so this assumption is met. As long as the residuals appear to be randomly and evenly distributed throughout the chart around the value zero, we can assume that homoscedasticity is not violated: #define residuals The x-axis displays the fitted values and the y-axis displays the residuals. To verify that this assumption is met, we can create a residuals vs. The assumption of homoscedasticity is that the residuals of a regression model have roughly equal variance at each level of a predictor variable. Lastly, we need to create residual plots to check the assumptions of homoscedasticity and normality. 05, our model is statistically significant and hours is deemed to be useful for explaining the variation in score. Since the p-value in this example is less than. whether predictor variables in the model are useful for explaining the variation in the response variable.
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