Five Parameters

Using the data from the governors dataset, we will determine whether election status had an impact on candidate longevity post-election. The data may not be representative as major party candidates are not systematically different from all candidates. We are using a Bayesian regression model to analyze the data, and the model reveals that the interaction between sex and election age has a positive direction, suggesting that as election age increases, the life expectancy difference between male/female governors tends to increase. One quantity of interest is the value of lived_after, as it helps display the impact that election status has on age.

\[ lived\_after_i = \beta_0 + \beta_1 male_i + \beta_2 c\_election\_age_i + \\ \beta_3 male_i * c\_election\_age_i + \epsilon_i \]

# A tibble: 2 × 2
  sex    election_age
  <chr>         <dbl>
1 Male             50
2 Female           50