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A Matter of Life and Death

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vicky
22 diapositives · 566 Views · Aug 08, 2014

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. 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.

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A Matter of Life and Death
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  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