An economist is interested to see how consumption for an economy (in $ billions) is influenced by gross domestic product ($ billions) and aggregate price (consumer price index). The Microsoft Excel output of this regression is partially reproduced below.

1. When the economist used a simple linear regression model with consumption as the dependent variable and GDP as the independent variable, he obtained an r2 value of .971. What additional percentage of the total variation of consumption has been explained by including aggregate prices in the multiple regression?

1. 98.2

2. 1.1

3. 2.8

4. 11.1

B. The p-value for GDP is

1. 0.05

2. 0.01

3. 0.001

4. None of the above

C. What is the predicted consumption level for an economy with a GDP equal to $4 billion and an aggregate price index of 150?

1. $4.75 billion

2. $9.45 billion

3. $2.89 billion

4. $1.39 billion

D. One economy in the sample had an aggregate consumption level of $4 billion, a GDP of $6 billion, and an aggregate price level of 200, What is the residual for this data point?

1. $-0.39 billion

2. $0.39 billion

3. -$1.33 billion

4. $4.39 billion

E. To test for the significance of the coefficient on aggregate price index, the value of the relevant T statistic is ____.

1. 2.365

2. -1.960

3. 0.143

4. -0.219

F. To test for the significance of the coefficient on the aggregate price index, the p-value is _____.

5. .9999

6. 0.0001

7. 0.8330

8. 0.8837
    

Transcribed Image Text:

SUMMARY OUTPUT Regression Statistics 0.991 0.982 0.976 0.299 10 Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept GDP Price df 7 9 Coeff - 0.0861 0.7654 - 0.0006 SS 33.4163 0.6277 34.0440 StdError 0.5674 0.0574 0.0028 MS 16.7082 0.0897 t Stat - 0.152 13.340 - 0.219 F 186.325 P-value 0.8837 0.0001 0.8330 Signif F 0.0001 SUMMARY OUTPUT Regression Statistics 0.991 0.982 0.976 0.299 10 Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept GDP Price df 7 9 Coeff - 0.0861 0.7654 - 0.0006 SS 33.4163 0.6277 34.0440 StdError 0.5674 0.0574 0.0028 MS 16.7082 0.0897 t Stat - 0.152 13.340 - 0.219 F 186.325 P-value 0.8837 0.0001 0.8330 Signif F 0.0001

Answer rating: 100% (QA)

SolutionA Given that R2 value for model is 0982 While if we use one variable as independent v... View the full answer