Preceptor-lifespan

Author

Hunter Stephens

\[\begin{equation} death\_age_i = \beta_0 + \beta_1 treatment_i + \beta_2 party_i + \beta_3 win\_margin_i + \epsilon_i \end{equation}\]

Warning in tidy.brmsfit(x, ..., effects = "fixed"): some parameter names
contain underscores: term naming may be unreliable!

Characteristic

Beta

95% CI

1
treatment

    treatmentwin 8.4 3.1, 14
party

    partyRepublican 4.0 1.2, 6.7
    partyThirdparty -9.5 -25, 6.5
win_margin -1.4 -2.4, -0.45
1

CI = Credible Interval

preceptor table:

outcomes: lifespan after election treatment: won election covariates: election_age, state, sex, year,

Winning the mayor postion is in the Preceptor table, but not in the dataset, which just has governor elections - so we can’t stack the preceptor table and the data. Validity: Winning the mayor postion is in the Preceptor table, but not in the dataset, which just has governor elections - so we can’t stack the preceptor table and the data. the lifespan has increased over time, governors may expect an impacted lifespan from office.