*** Please see p. 2 for Question 2 ***
Question 2 (7 points)
The following Excel output shows the outcome of a linear regression of individuals%u2019 wage per hour (in dollars) on the number of years they attended school (in years).
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.381932619
R Square
0.145872525
Adjusted R Square
0.144267022
Standard Error
4.753758428
Observations
534
ANOVA
df
SS
MS
F
Significance F
Regression
1
2053.22554
2053.22554
90.8578469
5.45998E-20
Residual
532
12022.25261
22.59821919
Total
533
14075.47815
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Upper 95.0%
Intercept
-0.745942699
1.045403804
-0.71354504
0.475821452
-2.799566599
1.307681201
1.307681201
Years of School
0.750448943
0.078729942
9.531938255
0.000545998
0.595789385
0.9051085
0.9051085
Part (a) (1 point)
What is the value of the estimated slope %u201Cb%u201D?
Part (b) (2 points)
Interpret the estimated value of the slope (i.e., explain what the number means in this regression).
Part (c) (1 point)
Is the estimate of the slope statistically significant? Please answer %u201Cyes%u201D or %u201Cno%u201D and explain how you can tell.
Part (d) (2 points)
Explain why we want to be able to reject the null hypothesis H0: %u03B2 = 0.
Part (e) (1 point)
How much of the total variation in wages can be explained by individuals%u2019 education?
*** Please see p. 2 for Question 2 ***
Question 2 (7 points)
The following Excel output shows the outcome of a linear regression of individuals%u2019 wage per hour (in dollars) on the number of years they attended school (in years).
SUMMARY OUTPUT | ||||||||
Regression Statistics | ||||||||
Multiple R | 0.381932619 | |||||||
R Square | 0.145872525 | |||||||
Adjusted R Square | 0.144267022 | |||||||
Standard Error | 4.753758428 | |||||||
Observations | 534 | |||||||
ANOVA | ||||||||
df | SS | MS | F | Significance F | ||||
Regression | 1 | 2053.22554 | 2053.22554 | 90.8578469 | 5.45998E-20 | |||
Residual | 532 | 12022.25261 | 22.59821919 | |||||
Total | 533 | 14075.47815 | ||||||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Upper 95.0% | ||
Intercept | -0.745942699 | 1.045403804 | -0.71354504 | 0.475821452 | -2.799566599 | 1.307681201 | 1.307681201 | |
Years of School | 0.750448943 | 0.078729942 | 9.531938255 | 0.000545998 | 0.595789385 | 0.9051085 | 0.9051085 |
Part (a) (1 point)
What is the value of the estimated slope %u201Cb%u201D?
Part (b) (2 points)
Interpret the estimated value of the slope (i.e., explain what the number means in this regression).
Part (c) (1 point)
Is the estimate of the slope statistically significant? Please answer %u201Cyes%u201D or %u201Cno%u201D and explain how you can tell.
Part (d) (2 points)
Explain why we want to be able to reject the null hypothesis H0: %u03B2 = 0.
Part (e) (1 point)
How much of the total variation in wages can be explained by individuals%u2019 education?