Info

5,036.4

10. Health care

80,552.8

6,620.1

13,869.9

11. Aerospace

95,294.0

3,918.6

4,487.8

12. Consumer products

101,314.1

1,595.3

10,278.9

13. Electrical and electronics

116,141.3

6,107.5

8,787.3

14. Chemicals

122,315.7

4,454.1

16,438.8

15. Conglomerates

141,649.9

3,163.8

9,761.4

16. Office equipment and computers

175,025.8

13,210.7

19,774.5

17. Fuel

230,614.5

1,703.8

22,626.6

18. Automotive

293,543.0

9,528.2

18,415.4

Source: Business Week, Special 1989 Bonus Issue, R&D Scorecard, pp. 180-224. Note: The industries are listed in increasing order of sales volume.

Source: Business Week, Special 1989 Bonus Issue, R&D Scorecard, pp. 180-224. Note: The industries are listed in increasing order of sales volume.

pattern between the residuals and squared residuals and sales, perhaps suggesting that there is heteroscedasticity. To test this formally, we used the Park, Glejser, and White tests, which gave the following results:

Park Test

IU2| = -974,469.1 + 86.2321 Sales, se = (4,802,343) (40.3625) r2 = 0.2219 (11.7.4)

The Park test suggests that there is a statistically significant positive relationship between squared residuals and sales.

Glejser Test

UI = 578.5710 + 0.0119 Sales, se = (678.6950) (0.0057) r2 = 0.214 (11.7.5)

The Glejser test also suggests that there is a systematic relationship between the absolute values of the residuals and sales, raising the possibility that the regression (11.7.3) suffers from heteroscedasticity.

CHAPTER ELEVEN: HETEROSCEDASTICITY 425

EXAMPLE 11.10 (Continued)

10,000 r

5,000

-10,000

TT"

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