best-fit lines, correlations: state+local ed spending VS evangelical

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 state+local ed spending vs the evangelical '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*

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?

other comments
i) the t-test value of 0.10 for EdSpending is the largest t-test value of the eight Response metrics – indicating that the data has a 10% probability that the sample means represent the same Population
ii) the Ed Spending metric shows 'typical' Response to evangelical Predictor: increasing evangelical population correlates with decreased ed spending
iii) however, the impact values are small – indicating that the Evangelical-EdSpending relationship is not-important
iv) that curious Rep state with the smallest evangelical population? Utah