#### Priyanka_Chandak23

##### New Member

1)For a sample of 400 firms, the relationship between corporate revenue (Y

You wish to test the joint null hypothesis that

Correct answer: c

GARP 2013

Answer: d.

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. Answers a, b and c are not likely causes and are therefore incorrect.

Thanks a lot.

Priyanka.

*i*) and the average years of experience per employee (X*i*) is modeled as follows:*Yi = β1 + β2 Xi + εi, i*= 1, 2,...,400You wish to test the joint null hypothesis that

*β1*= 0 and*β2*= 0 at the 95% confidence level. The p-value for the t-statistic for*β1*is 0.07, and the p-value for the t-statistic for*β2*is 0.06. The p-value for the F-statistic for the regression is 0.045. Which of the following statements is correct?**a.**You can reject the null hypothesis because each*β*is different from 0 at the 95% confidence level.**b.**You cannot reject the null hypothesis because neither*β*is different from 0 at the 95% confidence level.**c.**You can reject the null hypothesis because the F-statistic is significant at the 95% confidence level.**d.**You cannot reject the null hypothesis because the F-statistic is not significant at the 95% confidence level.Correct answer: c

**Explanation:**The T-test would not be sufficient to test the joint hypothesis. In order to test the joint null hypothesis, examine the F-statistic, which in this case is statistically significant at the 95% confidence level. Thus the null can be rejected.**Could someone please explain this, how do we solve such questions without a table if it appears this way in the exam?**GARP 2013

**2)**You built a linear regression model to analyze annual salaries for a developed country. You incorporated two independent variables, age and experience, into your model. 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.**MulticollinearityAnswer: 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. Answers a, b and c are not likely causes and are therefore incorrect.

**What do we mean by low t statistics here with reference to multicollinearity?**Thanks a lot.

Priyanka.

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