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

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

• 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?

• 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 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 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

• Our data does not support Phillips’ hypothesis

• Possible limitations– Our people are not famous enough

Overall distribution by month

Percent of Deaths per Month

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

January FebruaryMarch April May June July

AugustSeptember

OctoberNovember December

Month

Percent of Deaths

Is this distribution uniform?

Death Month # died % died zstatJanuary 49 12.5% 3.004February 35 9.0% 0.442March 45 11.5% 2.272April 30 7.7% -0.473May 27 6.9% -1.021June 32 8.2% -0.107July 36 9.2% 0.625August 19 4.9% -2.485September 30 7.7% -0.473October 26 6.6% -1.205November 33 8.4% 0.076December 29 7.4% -0.656

Distribution by month and volume

Percent Dead Per Month

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

January FebruaryMarch April May June July

AugustSeptember

OctoberNovemberDecember

Month

Percent

Vol 0Vol 1

Is this distribution uniform?

• Unpaired test for two sample proportions

Overall distribution by season

Percent of Deaths Per Season

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

Winter:Dec, Jan, Feb Spring: Mar, Apr, May Summer: Jun, Jul, Aug Fall: Sep, Oct, Nov

Season

Percent of Deaths

Deaths per season by volumePercent of Deaths per Season

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

Winter:Dec, Jan, Feb Spring: Mar, Apr, May Summer: Jun, Jul, Aug Fall: Sep, Oct, Nov

Season

Percent

Vol 0Vol 1

Is this distribution uniform?

• Test for difference by volume:

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

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?

• 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

Deaths in month before/after each date

Date #deaths inmonth before

#deaths inmonth after

Birthday 35 38

Christmas 34 48

New Yea r’s 29 49

July 4th 30 33

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’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

• 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

Conclusion