Five Parameters
Using a dataset that lists various information for candidates between 1945 and 2012, we seek to find whether sex affects how long candidates live after an election. One problem that might cause an issue is that the data only observes the front-running candidates in these elections. We are using a regression model that analyses a dependent variable known as lived_after to represent post-election life expectancy. The model represents that there is a positive relation between sex and election age, which suggests that life expectancy between male and female governors widen with election age. Male candidates tend to live for 30 more years post election, with around 28-32 years in the 95% certainty range.
\[ lived\_after_i = \beta_0 + \beta_1 male_i + \beta_2 c\_election\_age_i + \\ \beta_3 male_i * c\_election\_age_i + \epsilon_i \]
Warning in tidy.brmsfit(x, ..., effects = "fixed"): some parameter names
contain underscores: term naming may be unreliable!Characteristic |
Beta |
95% CI 1 |
|---|---|---|
| (Intercept) | 20 | -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 |
||