Answer-first summary for fast verification
Answer: The high correlations between each pair of index returns indicate that multicollinearity exists between the variables in this regression.
## Explanation **Option D is correct** because the correlation matrix shows extremely high correlations between the independent variables: - Russell 1000 and Russell 3000: 0.998 (almost perfect correlation) - Russell 1000 and Russell 2000: 0.813 - Russell 2000 and Russell 3000: 0.845 These high correlations indicate **multicollinearity**, which occurs when independent variables in a regression model are highly correlated with each other. This explains why we have: - High R² (0.9) indicating good model fit - But insignificant t-statistics and high p-values for all coefficients **Why other options are incorrect:** **Option A**: The coefficient of 0.3533 for Russell 3000 has a p-value of 0.8382, which is not statistically significant (p > 0.05). **Option B**: High R² doesn't guarantee that individual coefficients are statistically significant. Here, all coefficients have high p-values (> 0.05). **Option C**: A high p-value (0.9452) indicates the coefficient is NOT statistically significant, not more significant.
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Based on the regression results, which statement is correct?
A
The estimated coefficient of 0.3533 indicates that the returns of the Russell 3000 index are more statistically significant in determining the portfolio returns than the other two indexes.
B
The high adjusted R² indicates that the estimated coefficients on the Russell 1000, Russell 2000, and Russell 3000 indexes are statistically significant.
C
The high p-value of 0.9452 indicates that the regression coefficient of the returns of Russell 1000 is more statistically significant than the other two indexes.
D
The high correlations between each pair of index returns indicate that multicollinearity exists between the variables in this regression.
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