Election

Overview

Using information about candidates for governor from 1945 to 2012, we seek to find the relationship between death age of Preceptor and whether or not he wins the mayoral election in Newton, Massachusetts. Modern medicine has increased the overall lifespan of all candidates regardless if they won or lost. We modeled the age of death as a sum of multiple variables multiplied by their coefficients: election result, how much they won by, age during the election, political party, and sex. We are 95% confident that Preceptor will live an extra 3 to 14 years. Our estimate is 8.6 years.

Question

How old will Preceptor live to if he wins the election for mayer of Newton?

Wisdom

Preceptor Table

A perfect table. One row for each mayer, columns are state, lived_after. Covariates are state, sex, election_age, party.

EDA

Validity

Relationship between columns in preceptor table is the same for the data. One problem is governor is not a mayer

Justice

Population table

table with preceptor table, data, and greater population

Stability

People are living longer overtime because of better healthcare

Representativeness

Massachusetts represents more than Newton

Unconfoundedness

it’s a predictive model

not random because the people volunteered to run

Courage

 Family: gaussian 
  Links: mu = identity; sigma = identity 
Formula: death_age ~ treatment + party + win_margin 
   Data: x (Number of observations: 254) 
  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 Tail_ESS
Intercept          73.52      1.77    69.90    76.86 1.00     3193     2761
treatmentwin        8.41      2.71     3.14    13.83 1.00     2962     2625
partyRepublican     3.95      1.42     1.21     6.68 1.00     4063     2749
partyThirdparty    -9.50      8.09   -24.72     6.54 1.00     4454     2848
win_margin         -1.41      0.50    -2.42    -0.45 1.00     3024     2547

Further Distributional Parameters:
      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma    11.28      0.51    10.31    12.35 1.00     4423     2534

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).

Using 10 posterior draws for ppc type 'dens_overlay' by default.

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

Characteristic	Beta	95% CI1
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

Temprance

Variables

treatment, win_margin, party

Values

party: Republican, Democrat, Third Party treatment: win and lose win_margin: 0

Formula

\[death\_age_i = \beta_{0} + \beta_{1} treatment_i + \beta_{2}party_i + \beta_{3}win\_margin_i + \epsilon_{i}\]