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Using data from all deceased candidates in the United States between 1945 and 2012, we aim to predict their longevity after election. Using data from all deceased gubernatorial candidates in the United States from elections held between 1945 and 2012, we aim to forecast candidate longevity in state-wide US races post-election. We are concerned that longevity for gubernatorial candidates may differ significantly from that of candidates in Senate and other state-wide elections. Using data from deceased U.S. gubernatorial candidates between 1945 and 2012, we forecast post-election longevity. We are using a Bayesian regression model with lived_after ~ sex * election_age, where lived_after represents life expectancy. The results suggest that as election age increases, the longevity gap between male and female governors widens.
\[ lived\_after_i = \beta_0 + \beta_1 male_i + \beta_2 c\_election\_age_i +
\\ \beta_3 male_i * c\_election\_age_i + \epsilon_i \]
Warning in tidy.brmsfit(x, ..., effects = "fixed"): some parameter names
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| (Intercept) |
20 |
-24, 63 |
| sex |
|
|
| sexMale |
53 |
9.9, 97 |
| election_age |
-0.06 |
-0.79, 0.66 |
| sex * election_age |
|
|
| sexMale * election_age |
-0.79 |
-1.5, -0.07 |
