Family: gaussian
Links: mu = identity; sigma = identity
Formula: lived_after ~ sex * election_age
Data: df1 (Number of observations: 1092)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Regression Coefficients:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
Intercept 19.77 22.29 -24.03 63.02 1.00 1158
sexMale 52.83 22.42 9.91 97.33 1.00 1163
election_age -0.06 0.38 -0.79 0.66 1.00 1162
sexMale:election_age -0.79 0.38 -1.53 -0.07 1.00 1166
Tail_ESS
Intercept 1606
sexMale 1544
election_age 1534
sexMale:election_age 1554
Further Distributional Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 11.07 0.24 10.60 11.54 1.00 2405 1933
Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).Five Parameters
My question is: How long do political candidates live after the election? I have examined the preceptor table and validity. One problem is that the birth and death dates of losing candidates aren’t well documented. I have also examined the population table, stability, representativeness and unconfoundedness. One problem is that only two candidates are documented each year. I then made a model with the equation lived_after = sex * election_age. Finally, I added draws on both male and female candidates at the age of 50. I plotted them and saw that males lived longer and the distribution also had lower standard deviation.
\[ lived\_after_i = \beta_0 + \beta_1 male_i + \beta_2 c\_election\_age_i + \\ \beta_3 male_i * c\_election\_age_i + \epsilon_i \]
