A matter of life and death
Download
1 / 22

A Matter of Life and Death - PowerPoint PPT Presentation


  • 148 Views
  • Uploaded on

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.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'A Matter of Life and Death' - Faraday


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
A matter of life and death

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
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


Questions to answer
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?


Sample
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


Do people postpone death past their birthday
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


Do people postpone death past their birthday1
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.


Do people postpone death past their birthday2
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


Conclusion
Conclusion

  • Our data does not support Phillips’ hypothesis

  • Possible limitations

    • Our people are not famous enough





Is this distribution uniform1
Is this distribution uniform?

  • Unpaired test for two sample proportions




Is this distribution uniform2
Is this distribution uniform?

  • Test for difference by volume:

  • ANOVA for difference in seasons is not significant (p=.07)


Implications
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?


Can people postpone their death dates
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



New year s
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


New year s1
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


Regression
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



ad