If variable x1 and x2 have a high correlation, adding x2 to a model where x1 is already included would likely:

When two variables, x1 and x2, exhibit a high correlation, they tend to provide similar information about the relationship with the dependent variable in a regression model. In such cases, including both variables can lead to redundancy. Because x2 shares a significant amount of its explanatory power with x1, adding x2 to a model that already includes x1 often results in only a small increase in the model's overall explanatory power.

This phenomenon is often referred to as multicollinearity, which occurs when independent variables are highly correlated. While it may not render the model unusable—hence the choices related to confusion or inapplicability are less relevant—it does imply that the additional variable does not contribute significantly beyond what x1 already accounts for. Therefore, the model gains minimal benefit from including x2, leading to only a slight enhancement, if any, of its predictive capabilities.

The emphasis here is on the minimal addition of explanatory power because the primary information provided by x2 is likely already captured by x1, making its contribution to the model relatively insignificant.