Explanation

Correct Answer: B

Understanding the Pattern:

• High R²: Indicates the model explains a large portion of the variance in the dependent variable
• Few significant t-ratios: Means individual independent variables are not statistically significant

Why Multicollinearity is the Answer:

1. Multicollinearity Definition: Occurs when independent variables are highly correlated with each other

2. Effects of Multicollinearity:

   • Increases standard errors of coefficient estimates
   • Makes t-statistics smaller (less significant)
   • Coefficient estimates become unstable and sensitive to small changes in data
   • R² remains high because the variables collectively explain the dependent variable well

3. Why Other Options are Incorrect:

   • A. Homoscedasticity: Violation would affect standard errors but typically doesn't cause the specific pattern of high R² with insignificant t-ratios
   • C. Error term normality: Violation affects hypothesis testing but doesn't specifically cause high R² with insignificant coefficients
   • D. No autocorrelation: Affects efficiency of estimates but not the fundamental relationship between R² and t-statistics

Real-world Example: In the previous question, we saw:

• R² = 0.9 (very high)
• All p-values > 0.05 (insignificant coefficients)
• High correlations between independent variables (multicollinearity)

This is the classic symptom of multicollinearity - the model appears to fit well overall, but individual predictors lack statistical significance due to their intercorrelations.