Five Parameters

Summary

Using data from all deceased gubernatorial candidates in the United States from 1945-2012, we seek to predict candidate longevity post-election. However, the life expectancies from current day have vastly shifted 1945-2012, concerning the assumption of stability. Using a Bayesian regression model with the formula “lived_after ~ sex * election_age”, we analyzed the data, where “lived_after” is the dependent variable representing post-election life expectancy. The model demonstrates that sex and election age has a positive correlation, indicating that as election age increases, the life expectancy difference between male and female governors tends to widen.

\[ lived\_after_i = \beta_0 + \beta_1 male_i + \beta_2 c\_election\_age_i + \\ \beta_3 male_i * c\_election\_age_i + \epsilon_i \]Temperance uses the Data Generating Mechanism to answer the question with which we began. Humility reminds us that this answer is always a lie.