Final answer:
The best approach when dealing with multicolinearity and a high R2 in an estimated model is regression diagnostics.
Step-by-step explanation:
When confronted with multicollinearity, the best approach may be to do regression diagnostics if the estimated model yields a high R2. Regression diagnostics help identify potential outliers and influential points, as well as assess the appropriateness and reliability of the regression model.
By examining the scatter plot, calculating new regression lines, and comparing correlation coefficients, you can determine the impact of outliers on the model's fit and predictive power.