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What happens if homoscedasticity is violated?
The impact of violating the assumption of homoscedasticity is a matter of degree, increasing as heteroscedasticity increases. By definition, OLS regression gives equal weight to all observations, but when heteroscedasticity is present, the cases with larger disturbances have more “pull” than other observations.
Why is homoscedasticity violated?
Typically, homoscedasticity violations occur when one or more of the variables under investigation are not normally distributed. Sometimes heteroscedasticity might occur from a few discrepant values (atypical data points) that might reflect actual extreme observations or recording or measurement error.
What to do if assumptions of linear models are violated?
Another model might be better to explain your data (for example, non-linear regression, etc). You would still have to check that the assumptions of this “new model” are not violated. Your data may not contain enough covariates (dependent variables) to explain the response (outcome). In this case, you cannot do anything else.
Is there an assumption about the normality of residuals?
However, there is an assumption about the normality of the residuals. Check different kind of models. Another model might be better to explain your data (for example, non-linear regression, etc). You would still have to check that the assumptions of this “new model” are not violated.
Is the normality assumption necessary for nonlinear regression?
Normality assumption is not necessary for nonlinear regression. It is often used because it’s convenient. However, if it’s clearly violated then I wouldn’t use it. The same with heteroscedasticity. In your example the dependent variable seems to be confined between 0 and 100%.
Why are the results of linear regression unreliable?
Normality: The residuals of the model are normally distributed. If one or more of these assumptions are violated, then the results of our linear regression may be unreliable or even misleading.