| 410495 | 4 | 410500 | 5 | 410501 | |-| 6 | 410509 |

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Unformatted text preview: | 410495 . . | 4. | 410500 . . | 5. | 410501 . . | |---------------------------------------| 6. | 410509 . . | 7. | 410513 . . | 8. | 410517 . . | 9. | 410518 . . | 10. | 410521 . . | |---------------------------------------| 11. | 410523-.1823215-.01 | 12. | 410529 . . | 13. | 410531 . . | 14. | 410533 . . | 15. | 410535 . . | |---------------------------------------| 16. | 410536 . . | 17. | 410538 .037179 .0999999 | 18. | 410540 . . | 19. | 410544 . . | 20. | 410546 . . | --------------------------------------- 84 . tab grant 1 if | received | grant | Freq. Percent Cum.------------ ----------------------------------- 0 | 121 77.07 77.07 1 | 36 22.93 100.00------------ ----------------------------------- Total | 157 100.00 . ttest cscrap 0 if grant One-sample t test------------------------------------------------------------------------------ Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]--------- -------------------------------------------------------------------- cscrap | 19-1.303158 .5437056 2.369958-2.445441-.1608747------------------------------------------------------------------------------ mean mean(cscrap) t -2.3968 Ho: mean degrees of freedom 18 Ha: mean Ha: mean ! Ha: mean Pr(T t) 0.0138 Pr(|T| |t|) 0.0276 Pr(T t) 0.9862 85 . ttest clscrap 0 if grant One-sample t test------------------------------------------------------------------------------ Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]--------- -------------------------------------------------------------------- clscrap | 19-.3744936 .1460807 .6367511-.6813978-.0675894------------------------------------------------------------------------------ mean mean(clscrap) t -2.5636 Ho: mean degrees of freedom 18 Ha: mean Ha: mean ! Ha: mean Pr(T t) 0.0098 Pr(|T| |t|) 0.0195 Pr(T t) 0.9902 . log close 86 ∙ The preceding example falls under what is sometimes viewed as a paired test of the equality of means from two populations. But one can always set up a paired test as standard test of the mean from a single population by defining X i as the difference between pairs. ∙ For example, suppose that the outcomes for pair i are Y i 1 , Y i 2 with 1 E Y 1 and 2 E Y 2 . We are interested in testing hypotheses about 2 − 1 , but this is E X i with X i Y i 2 − Y i 1 So just define E X i 2 − 1 and test hypotheses about . 87 ∙ In the job training example, we can take X i Y i ,88 − Y i ,87 where Y i ,88 and Y i ,87 are the (log) scrap rates for firm i in the two years 1988 and 1987....
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