How can one adjust for multicollinearity in a multiple linear regression model?
- By adding interaction terms
- By increasing the sample size
- By removing one of the correlated variables or combining the correlated variables
- By transforming the dependent variable
To adjust for multicollinearity in a multiple linear regression model, one of the common strategies is to remove one of the highly correlated independent variables or to combine the correlated variables.
Loading...
Related Quiz
- How does the choice of significance level affect the probability of making a Type I error?
- In the context of simple linear regression, the difference between the observed value and the predicted value is referred to as the ________.
- A __________ is the difference between the observed value and the predicted value of the response variable in regression analysis.
- What is the purpose of multiple linear regression analysis?
- What is the interpretation of a 95% confidence interval that contains zero?