Question

What is a good solution when confronted with multicollinearity?

Select all that apply
Multiple select question.
A. Add another variable
B. Obtain more data because the sample correlation may get weaker
C. Drop one of the collinear variables
D. Obtain more data because a bigger sample is always better

Answer 1

Good solutions to multicollinearity include dropping one of the collinear variables and potentially obtaining more data to provide a better estimate of the true relationship between variables, rather than indiscriminately adding more variables or data.

Step-by-step explanation:

A good solution when confronted with multicollinearity involves several steps that do not necessarily include adding another variable or always obtaining more data. Appropriate solutions include:

• Drop one of the collinear variables: If two variables are highly correlated, one of them can be removed from the model to reduce multicollinearity.
• Obtain more data: Sometimes, a small sample size might make variables appear more correlated than they are. Increasing the sample size can provide a better estimate of the true relationship between variables, but it is not a guaranteed solution and should be done with consideration of the data context.

It's important to note that while obtaining more data can sometimes help, adding another variable might actually increase multicollinearity if the new variable is also correlated with the existing ones. A bigger sample size is not always better if it does not address the underlying issue of multicollinearity.