Elections

Author

Gitanjali Sheth

Preceptor table: Will contain rows of candidates who ran for office that can be identified with their names. The outcome we are looking for is how long they live for post election. Covariates that might effect their age post election is their gender, whether they won or lost, their age at election time and their affiliated party. There is no treatment as it is a predictive model.

\[death\_age = \beta_{0} + \beta_{1} treatment_i + \beta_{2}win\_margin_i + \\ \beta_{3}repub_i + \beta_{4}thirdparty_i + \epsilon_{i}\]

                 Estimate Est.Error       Q2.5      Q97.5
Intercept       73.542859 1.8008034  69.941591 77.0647733
treatmentwin     8.340945 2.7931453   2.967633 14.0196176
win_margin      -1.406902 0.5116411  -2.402440 -0.4211054
partyRepublican  3.976145 1.3926685   1.292565  6.7439850
partyThirdparty -9.315387 8.0616311 -25.004359  6.1882960

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

Characteristic	Beta	95% CI ¹
(Intercept)	74	70, 77
treatment
treatmentwin	8.3	3.0, 14
win_margin	-1.4	-2.4, -0.42
party
partyRepublican	4.0	1.3, 6.7
partyThirdparty	-9.3	-25, 6.2
¹ CI = Credible Interval