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　　Exercise：
　　You built a linear regression model to analyze annual salaries for a developed country. You incorporated two independent variables, age and experience, into your mode. Upon reading the regression results, you noticed that the coefficient of “experience” is negative which appears to be counter-intuitive. In addition you have discovered that the coefficients have low t-statistics but the regression model has a high R2. What is the most likely cause of these results?
　　A.     Incorrect standard errors
　　B.     Heteroskedasticity
　　C.     Serial correlation
　　D.     Multicollinearity
　　Answer: D
　　Explanation: Age and experience are highly correlated and would lead to multicollinearity. In fact, low t-statistics but a high R2 do suggest this problem also. Answer A, B and C are not likely causes and are therefore incorrect.
　　相關(guān)知識(shí)點(diǎn)：
　　Detecting Multicollinearity
　　l  The most common way to detect multicollinearity is the situation where t-tests indicate that none of the individual coefficients is significantly different than zero, while the R2 is high.
　　l  High correlation among independent variables is sometimes suggests as a sign of multicollinearity. In fact, as a general rule of thumb: if the absolute value of the sample correlation between any two independent variables in the regression is greater than 0.7, multicollinearity is a potential problem.