Explanation

This is a classic case of multicollinearity in multiple regression analysis. Let's analyze why option D is correct:

Evidence of Multicollinearity:

1. High correlations between independent variables:

   • Russell 1000 vs Russell 3000: 0.998 (almost perfect correlation)
   • Russell 1000 vs Russell 2000: 0.813
   • Russell 2000 vs Russell 3000: 0.845

2. Symptoms in regression output:

   • High R² (0.905) but insignificant individual coefficients (all p-values > 0.05)
   • Large standard errors for the coefficients
   • Low t-statistics (all < 1)

Why other options are incorrect:

Option A: The coefficient magnitude (0.3533) doesn't indicate statistical significance. All coefficients have high p-values (> 0.05), meaning none are statistically significant.

Option B: High R² only indicates the model explains variance well overall, but doesn't guarantee individual coefficients are significant. In fact, multicollinearity can cause high R² with insignificant coefficients.

Option C: High p-values (like 0.9452) indicate low statistical significance, not high significance.

Multicollinearity Impact:

• Makes coefficient estimates unstable and unreliable
• Increases standard errors
• Can cause incorrect signs on coefficients
• Makes it difficult to determine the individual effect of each variable

This is an example of imperfect multicollinearity since the variables are not perfectly correlated but highly correlated, which still causes estimation problems.