Final answer:
To address multicollinearity, it is advisable to drop one of the collinear variables or obtain more data to see if the sample correlation weakens. Adding another variable or just obtaining more data without a specific focus on multicollinearity is not recommended.
Step-by-step explanation:
When confronted with multicollinearity in a statistical model, an effective way to handle it is to implement some specific remedies. Multicollinearity occurs when two or more independent variables in a regression model are highly correlated, which can cause issues with the interpretation of the coefficients of the variables. To address multicollinearity, you can:
- Consider dropping one of the collinear variables from your model (Option C). This simplifies the model and reduces the multicollinearity, but at the risk of losing some information provided by the variables.
- Obtain more data to see if the sample correlation weakens (Option B). Having a larger dataset may diminish the multicollinearity as the variance in the data increases.
However, adding another variable (Option A) is not a recommended solution because it may increase the complexity without resolving the multicollinearity issue. Similarly, simply obtaining more data without considering its relevance to multicollinearity (Option D) does not necessarily improve the model's performance. Therefore, the correct options to choose for handling multicollinearity are dropping one of the collinear variables and obtaining more data to weaken sample correlation.