The meaning or impact of the regression data residual not being normally distributed is that the assumptions of the regression model are not met. This can result in biased or misleading estimates of the regression coefficients and the overall fit of the model.
When the residuals are not normally distributed, it can indicate that there are other factors influencing the relationship between the independent and dependent variables that are not accounted for in the model. This can lead to incorrect predictions and unreliable conclusions about the relationship between the variables.
To address this issue, it may be necessary to transform the data or use a different type of regression model that does not assume normality of the residuals. It is also important to check for outliers and influential points, as these can also affect the distribution of the residuals.
In conclusion, the normality of the residuals is an important assumption of the regression model, and if it is not met, it can have a significant impact on the reliability and validity of the results.