1. A Matter of Life and Death Can the Famous Really Postpone Death? The distribution of death dates across the year Alisa Beck, Marcella Gift, Katie Miller

2. Basis for Project • Case Study 6.3.2 • David Phillips’ study on postponing death until after one’s birthday • Theory of death dip/death rise

3. Questions to answer • Do people postpone their death until after a birthday? • Is the distribution of death dates uniform throughout the year? • Is there a difference in distribution for people who died in the 1920s vs 1990s? • Can people postpone their death past another special date? What date?

4. Sample • 391 entries from two volumes of Who Was Who in America • Selected every other entry for a given number of entries for each letter of the alphabet • 39.1% from Volume I (1920s), 60.9% from Volume XIII (1990s) • 89.3% male, 10.7% female

5. Do people postpone death past their birthday? • Test of proportions to compare the number of people dying in the month after their birthday against the expected proportion • Expected number of deaths in a given month is 391/12=32.6 • Number of people dying in one month after birthday is 38

6. Do people postpone death past their birthday? • Z=x-np/sqrt(np(1-p)) • Z=.99<1.64 • Therefore we cannot reject the null hypothesis that the proportion of deaths in the month after one’s birthday is 1/12. • Phillips’ hypothesis does not hold for our data.

7. Do people postpone death past their birthday? • Confidence interval for the mean difference in the number of days between birth date and death date • Mean difference=6.84 days after birthday • Range of -180 to 180 • 95% CI: (-3.57, 17.27) • Therefore, the mean is not significantly different from 0, so people are not more likely to die after their birthday

8. Conclusion • Our data does not support Phillips’ hypothesis • Possible limitations • Our people are not famous enough

9. Overall distribution by month

10. Is this distribution uniform?

11. Distribution by month and volume

12. Is this distribution uniform? • Unpaired test for two sample proportions

13. Overall distribution by season

14. Deaths per season by volume

15. Is this distribution uniform? • Test for difference by volume: • ANOVA for difference in seasons is not significant (p=.07)

16. Implications • People who died in the 1920s are more likely to have died in the spring, while people who died in the 1990s were more likely to die in the winter. • More people tend to die in winter...is this because of postponement or other factors?

17. Can people postpone their death dates? • Dates we considered that would be important to people • Birthday • Christmas • 4th of July • New Year’s • Expected number of deaths in any given month is 391/12=32.6

18. Deaths in month before/after each date

19. New Year’s • The date with the greatest evidence of death rise/death dip is New Year’s Day • Test significance of date with z-test for proportions • H0: p=1/12=.083 • H1: p>.083, phat=49/391=.125 • Z=2.99>1.64 • There is a significant increase in deaths after the New Year

20. New Year’s • Test significance of date with z-test for proportions • H0: p=1/12=.083 • H1: p<.083, phat=29/391=.074 • Z=-.66>-1.64 • There is not a significant decrease in deaths before the New Year

21. Regression • Age of death= ß0 + ß1*(Days after birthday died) + ß2*(birth month) + ß3*(sex) + ß4*(volume) • Hypothesis testing using regression: Do people live longer now than in the last century? • Compare models with and without volume

22. Conclusion