Five Parameters
Using data from the paper “Longevity Returns to Political Office” including data from 1945 to 2012, we try to predict candidate longevity after the elections. A problem with the data is average lifespan is constantly changing as medical technology advances. To calculate this, we are using a linear model with a normal error term, where our intercept are females, and we are calculating the longevity of the candidates. It reveals that the interaction between sex and the age after an election has a positive direction, and that as election age increases, the life expectancy difference between male and female governors increases. The quantity of interest is the difference in the number of years that the male candidate lives versus the female candidate. We don’t know of the approximation is accuraet, because the data tends to have fewer female candidates.
\[ lived\_after_i = \beta_0 + \beta_1 male_i + \beta_2 c\_election\_age_i + \\ \beta_3 male_i * c\_election\_age_i + \epsilon_i \]
| Characteristic | Beta | 95% CI1 |
|---|---|---|
| (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 |
| 1 CI = Credible Interval | ||
# A tibble: 2 × 2
sex election_age
<chr> <dbl>
1 Male 50
2 Female 50