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best-fit lines, correlations: infant mortality vs 'predictor' variables

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 opioid dispensing rate vs the three 'predictor' variables.
   ... 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 plots present means, standard deviations, the 'best-fit' lines, and r-values for Rep and Dem states.
   ii) The evangelical metric most closely correlates with infant mortality (Dem r = 0.49, Rep r = 0.42).
   iii) Both Rep and Dem states' r-values, using the diversity* predictor metric, are essentially equal.
   iv) It is curious that as Rep best-fit line becomes more 'urban', infant mortality increases; yet, the standard expectation is that as 'urbanity' increases, infant mortality would decrease as is seen in the Dem best-fit line. However, the absolute values of the r-values is small, indicating 'noisy' data.
   v) The evangelical plot for both Rep and Dem states: increasing evangelical % increases infant mortality.
   vi) The diversity* plot for both Rep and Dem states: increasing diversity* decreases infant mortality.
4. Similar plots using the three 'predictor' metrics have been posted:
   for GDP (20Jan), state taxes (17Feb), suicide rate (17Mar), opioid dispensing rate (26May), and life expectancy (18Aug).

Data is not beautiful when you post 3 scatterplots and just expect people to figure it out. Hope about some descriptions or legends or findings?

So when a child dies, somebody asks who the parents voted for, and record that data for somebody such as yourself to interpret?

Who knew!

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You really should have included in the graphic that each dot was a state.

The data is categorized by state, so the R/D distinction is determined by the election result of the state, not by talking to any parents.

Can this data be redone at the county level?

... yeah, you are right

I am sure that it can, but ... defining the 'predictor' metrics becomes more difficult and, therefore, more prone to "what about ...." arguments which devolve to denialism.

... some person with more courage than I would need to do it and it would be pleasurable to read, I am sure.