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25 Jan 2019

*** 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?

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Lelia Lubowitz
Lelia LubowitzLv2
28 Jan 2019

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