best-fit lines, correlations: incarcerationvs 'rural-urban'

1. Purpose
   In order to 'understand' the non-random, top/bottom, Rep/Dem differentiation of metric values, eight "response" metrics are correlated with three "predictor" metrics. This post presents the 'response' variable incarceration vs the 'rural-urban' predictor metric.
   ... the eight "response" metrics: GDP, state taxes; suicide rate, opioids; life expectancy, infant mortality; incarceration, state+local ed spending
   ... the three "predictor" metrics: 'rural-urban', evangelical, diversity*

2. the "big picture"
   i) There is a non-random, top/bottom, Dem/Rep pattern. Patterns have reasons/causes and are mathematical.
   ii) Rep states are always on the negative side (less GDP, more suicides, lower life expectancy, etc).
   iii) How did 150 million voters, acting individually, separate the fifty states into two such disparate groups?iv) is there a "predictive" metric or combination of metrics which can be used to explain the characteristic Rep/Dem differences seen in the data?

3. general comments
   i) the Rep states r-value is about half of the Dem states r-value; the r-value of Rep states indicates Rep data is "noisy"
   ii) however, the best-fit lines show Rep/Dem difference: as Rep states become more "urban", there is increasing incarceration; as Dem states become more "urban", there is decreasing incarceration