What does a linear regression model being heteroscedastic imply?The variance in the data is constantThe variance in the data is not constantThe variance in the data is zero

The assumption of regression which requires that the variance of the errors is constant for all values of the independent variable(s) is:A.Equal variance or homoscedasticityB.LinearityC.NormalityD.Independence of errors

If a linear regression model indicated heteroscedasticity, which of the following actions could be considered to address this issue?Removing outliers from the dataset to reduce the impact of extreme values on the variance of residuals.All of the above.Applying transformations to the independent variables to better fit the linear relationship.Implementing weighted least squares regression to give less emphasis to observations with higher variance in residuals.

Let's evaluate our model's results. Generate a scatter plot of the residuals against the fitted values allowing us to visually inspect whether the residuals have constant variance and are distributed randomly around the zero residual line.What does the scatter plot tell us?OptionsThe model has perfect predictive accuracy.The plot indicates homoscedasticity as residuals have a constant variance and are distributed randomly around the zero residual line.There is no linear relationship between the dependent and independent variables.The model suffers from multicollinearity.

What does the assumption of homoscedasticity in linear regression refer to?

Which assumption is NOT made by Linear Regression?Answer areaLinearity of relationshipsIndependence of errorsHomoscedasticityData follows a normal distribution