Five Parameters
Using data from deceased United States gubernatorial candidates between 1945 and 2012, we seek to predict the post-election longevity of candidates in state-wide races. There is concern that there are longevity differences between major and minor party candidates. We are using a Bayesian regression model with the formula lived_after ~ sex * election_age, where lived_after represents post-election life expectancy. The model reveals that male candidates have longer longevity post-election than female candidates. Male candidates live 53 years longer than their female counterparts, with a 95% confidence interval of 9 and 97. However, we do not know the accuracy of our approximation for female candidates, as our data has fewer of them.
\[ lived\_after_i = \beta_0 + \beta_1 male_i + \beta_2 c\_election\_age_i + \\ \beta_3 male_i * c\_election\_age_i + \epsilon_i \]
Warning in tidy.brmsfit(x, ..., effects = "fixed"): some parameter names
contain underscores: term naming may be unreliable!Characteristic |
Beta |
95% CI 1 |
|---|---|---|
| (Intercept) | 20 | -22, 64 |
| sex | ||
| sexMale | 53 | 9.0, 94 |
| election_age | -0.06 | -0.83, 0.62 |
| sex * election_age | ||
| sexMale * election_age | -0.79 | -1.5, -0.03 |
| 1
CI = Credible Interval |
||