2.5% 97.5%
b_Intercept -22.534127 59.2678846
b_sexMale 13.619354 94.6285742
b_election_age -0.717572 0.6505509
b_sexMale:election_age -1.497934 -0.1287349
sigma 10.605877 11.5476120
Intercept 27.555715 28.9067251
lprior -7.083778 -7.0291533
lp__ -4182.103994 -4176.3917694
Estimate Est.Error Q2.5 Q97.5
Intercept 18.96654427 20.7604969 -22.534127 59.2678846
sexMale 53.63451338 20.8167183 13.619354 94.6285742
election_age -0.05008554 0.3496353 -0.717572 0.6505509
sexMale:election_age -0.80544177 0.3509873 -1.497934 -0.1287349
Using 10 posterior draws for ppc type 'dens_overlay' by default.
We seek to use the governors dataset and its variables including sex to determine what influences lifespan of candidates after an election. Due to the dataset being a timeline of over 50 years during which many changes took place, we will have to adjust for those changes to average lifespan or shorten the timeline. We have used a Bayesian model to assess the relationship between sex and lifespan after, coming to the conclusion that the average male candidate will likely live longer after an election than the average female candidate but the average male candidate’s further lifespan is more greatly shortened for each year of additional age they have at the time of the election. The QoI is ~19 years different between male and female candidates with ~a 22% mOE.
\[ lived\_after_i = \beta_0 + \beta_1 male_i + \beta_2 c\_election\_age_i +
\\ \beta_3 male_i * c\_election\_age_i + \epsilon_i \]
# A tibble: 2 × 2
sex election_age
<chr> <dbl>
1 Male 50
2 Female 50
