Five Parameters
We have a dataset on governor election candidates from 1945 to 2012 that lists information on the candidates, including their birth and death dates. We want to predict form this how long candidates live after the election. After building a population table and addressing the concerns of stability, representativeness and unconfoundedness, arises the concern that the data is not stable as average lifespans change overtime in hand with medical advancement. We are using a Bayesian regression model with formula lived_after ~sex*election_age with lived_after as the dependent variable. It shows that the sex and election age have a positive relationship. From this, males live longer than females after election, however we cannot know the entire truth, this is just a model prediction.
\[ lived\_after_i = \beta_0 + \beta_1 male_i + \beta_2 c\_election\_age_i + \\ \beta_3 male_i * c\_election\_age_i + \epsilon_i \]
Warning in tidy.brmsfit(x, ..., effects = "fixed"): some parameter names
contain underscores: term naming may be unreliable!Characteristic |
Beta |
95% CI 1 |
|---|---|---|
| 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 |
||