Which of the following is NOT a remedy for heteroscedasticity?Log transformation of the dependent variable.Adding more predictor variables.Using weighted least squares.Box-Cox transformation.

Which of the following is a common method to address heteroscedasticity?Increase the sample size.Transform the dependent variable.Remove outliers.Add more independent variables.

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.

Which of the following is NOT a remedy for multicollinearity?Removing one of the correlated variables.Combining correlated variables.Increasing the sample size.Applying a logarithmic transformation

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

(1 mark) Why is working with the income in a log-scale a common practice in data analysis? Answer 1 Question 1A. Income is often left skewed, and through a log-transformation, the distribution of the log income is more symmetric.B. Log-transformation is useful because it always improves the accuracy of the model.C. Income is often positively skewed, and through a log-transformation, the distribution of the log income is more symmetric.D. Log-transformation makes the data look more linear.