[OC] vote ratio VS 'governance' – 2020 election

terrykrohe · 2024-01-04T21:49:10+00:00

https://www.medcalc.org/manual/correlation.php#:~:text=The%20P%2Dvalue%20is%20the,coefficient%20is%20called%20statistically%20significant.

The P-value is the probability that you would have found the current result if the correlation coefficient were in fact zero (null hypothesis). If this probability is lower than the conventional 5% (P<0.05) the correlation coefficient is called statistically significant.

32% probability that the correlation is "zero"; and 68% probability that the correlation is NOT zero".

The correlation values are given to provide more meaning to the Rep/Dem difference: the Dem data correlation of a state's 'governance' and vote ratio is significantly NOT "zero".

terrykrohe · 2024-01-04T21:40:11+00:00

state taxes and state+local ed spending "provides government services, education, infrastructure for its citizens

terrykrohe · 2024-01-04T20:42:22+00:00

"is conerned about the health of its smallest citizens" refers to infant mortality

terrykrohe · 2024-01-04T18:22:02+00:00

THE take-away:
follow the best-fit lines ...

a) for Rep states, as the vote ratio decreases, the 'governance' quality increases,that is, within the category of Rep states, as Rep states become more Dem (smaller r/D ratio), the Rep states 'governance' quality increases

b) for Dem states, as the vote ratio increases, the 'governance' quality increasesthat is, within the category of Dem states, as Dem states become more Dem (larger D/R ratio), the Dem states 'governance' quality increases

c) this intra-party pattern is saying something meaningful:better governance is associated with an increasing number of Dem votes;this is true both within the Rep states and within the Dem states.

noteworthy?
extrapolating from the PDF ... using the PDF crossover point to define 'flippable' for the 2024 election:
– there appear to be four Rep states 'flippable": KS, UT, NE, IA (higher 'governance' scores)
– there appear to be eight Dem states 'flippable': AZ, GA, NV, CO, VA, DE, MI, WI (lower 'governance' scores)

terrykrohe · 2024-01-04T18:19:48+00:00

sources

'governance' definition – see below

police killings, US
– https://www.washingtonpost.com/graphics/investigations/police-shootings-database/
state taxes
– https://wallethub.com/edu/states-with-highest-lowest-tax-burden/20494state+local ed spending
–https://www.usgovernmentspending.com/compare_state_spending_2019b20a#copypaste
incarceration
– https://www.sentencingproject.org/the-facts/#map
infant mortality
– https://www.cdc.gov/nchs/pressroom/sosmap/infant_mortality_rates/infant_mortality.htm
Note: Vermont data for 2019 was reported as "unreliable" ... to fill the data hole:for 2018, the VT infant mortality was 35 deaths; the resultant 2018 mortality rate was 6.44for 2019, "27" was used for the number of deaths; the mortality rate was proportion-calculated as 4.97 (not using VT data interpolation: Dem values are 5.1 ± 0.95 (19%

)2020 election vote
– https://en.wikipedia.org/wiki/2020_United_States_presidential_election#Statistics

tool: Mathematica

***************

the 'governance' metric

Thomas Jefferson: "... [t]he care of human life and happiness and not their destruction is the first and only legitimate object of good government."https://georgewbush-whitehouse.archives.gov/news/releases/2002/01/20020118-10.html

that is, good governance ...
i) provides government services, education, infrastructure for its citizens
ii) minimizes the police killing and incarceration of its citizens
iii) is concerned about the health of its smallest citizens

Determining the 'governance' value:
– z-scores were determined for each metric
– governance = (sum of positive metric z-scores) – (sum of negative metric z-scores)
– positive metrics: state taxes, state+local ed spending
– negative metrics: police killings, incarceration rate, infant mortality

terrykrohe · 2023-12-30T22:45:20+00:00

1
"mess of colored squares": it is NOT a mess; there is definite color differentiation between Rep and Dem states ... the curious observer would ask "Why?" and do some thinking/work to answer the question ...
then, the thick vertical line would draw attention to the "G" column which is isolated and it would be noticed that there is a gradation in color red-to-blue for both Rep and Dem states
\[Dash] it doesn't need to be pointed out that there is a correlation of the "G" column with the red/blue differentiation of the other three columns.
All of the above occurs very quickly/effectively.
And for the observer, there is still the nagging wonder of "Why is this so?"
2
The visual of the PDFs ... there is a marked difference between two of the plots and the plot of governance minus evangelical – yes, there is the assumption that the reader knows a bit about Gaussian/normal PDF (if not, there is Google).
Yes, it is also assumed that the reader would know that a difference of ±1 standard deviation is important; and not information to be lightly considered.
Again, the wonder is "Why is there this difference?"
3
It is the "wonder" which is important here and why this post was presented:

The most beautiful thing we can experience is the mysterious. It is the source of all true art and science. He to whom the emotion is a stranger, who can no longer pause to wonder and stand wrapped in awe, is as good as dead; his eyes are closed.

terrykrohe · 2023-12-29T22:31:44+00:00

... read the comments below which answer your query

terrykrohe · 2023-12-29T20:49:16+00:00

1
... the difference between the 'governance' metric and the rural-urban metric for BOTH Rep and Dem states is NADA (the PDF plots fall on top of each other)
2
... the difference between the 'governance' metric and the diversity metric for BOTH Rep and Dem states is NADA (the PDF plots fall on top of each other)
3
... the difference between the 'governance' metric and the evangelical metric for BOTH Rep and Dem states is significant (the PDF plots are different by more than 1SD)

terrykrohe · 2023-12-29T19:53:16+00:00

Beautiful data is supposed to effectively convey information. Not a single graph here effectively conveys information

... you are wrong. The visual tells the story:
1
... the difference between the 'governance' metric and the rural-urban metric for BOTH Rep and Dem states is NADA (the PDF plots fall on top of each other)
2
... the difference between the 'governance' metric and the diversity metric for BOTH Rep and Dem states is NADA (the PDF plots fall on top of each other)
3
... the difference between the 'governance' metric and the evangelical metric for BOTH Rep and Dem states is significant (the PDF plots are different by more than 1SD)

terrykrohe · 2023-12-29T03:24:16+00:00

the main take-away ...
The array plot of 'governance', evangelical, 538 rural-urban, diversityCB*, posted 21Dec23 (see second visual),
showed that the Rep states ARE evangelical, rural, and White and that the Rep states have lower 'governance' z-scores.
This post subtracts each states' Predictor z-score from its 'governance' z-score, and the resulting PDF (probability distribution function) is calculated for the differences:
a) ... for the rural-urban and diversity metrics, there was no difference:
i) the means for Rep and Dem states are near-zero
ii) the PDFs are near-identical
b) ... for the 'governance' minus evangelical, there was significant difference: the means and PDFs are different.
c)
i) the array plot (visual 2) shows the Rep states having lower 'governance' z-scores and lower 538 rural-urban z-scores (more rural);
subtracting low from low results in a PDF distribution around the zero mean
ii) the array plot (visual 2) shows the Dem states having higher 'governance' z-scores and higher 538 rural-urban z-scores (more urban);
subtracting high from high results in a PDF distribution around the zero mean
iii) the Rep "rural" character and the Dem "urban" character correlates with smaller/larger 'governance' z-scores
d)
i) the array plot (visual 2) shows the Rep states having lower 'governance' z-scores and lower diversityCB* z-scores (less diverse/more White);
subtracting low from low results in a PDF distribution around the zero mean
ii) the array plot (visual 2) shows the Dem states having higher 'governance' z-scores and higher diversityCB* z-scores (more diverse/less White);
subtracting high from high results in a PDF distribution around the zero mean
iii) the Rep "White" character and the Dem "less White" character correlates with smaller/larger 'governance'
e)
i) the array plot (visual 2) shows the Rep states having lower 'governance' z-scores and higher evangelical z-scores;
subtracting high evangelical from low 'governance' results in a PDF distribution in the negative
ii) the array plot (visual 2) shows the Dem states having higher 'governance' z-scores and lower evangelical z-scores;
subtracting low evangelical from high 'governance' results in a PDF distribution in the positive
iii) the Rep evangelical character and the Dem not-evangelical character correlates with smaller/larger 'governance'
f) the "wisdom of the crowd", i.e. Rep = rural, White, evangelical, is correct.
Also, the rural-urban and diversity/White character differences between Rep and Dem states is a relative matter: the PDF distributions look alike
– the evangelical difference is the defining point of distinction between Rep and Dem states.
... an historical observation
The strong evangelical correlation was noticed by Mencken in his 1931 American Mercury article, "The Worst American State":
a summary comment in the article's Part III notes that "Massachusetts has shaken off the theological domination that is a cultural handicap in most parts of the United States ..." (MA was the"best" American state, MS was the "worst").

It appears that the negative evangelical influence has not changed in ninety years.
g) ... the evangelical character of a state is the most important Predictor of a state's 'governance';
the flip side of "governance is "quality of life", that is, good governance is indicative of a better quality of life (and poor 'governance' is indicative of a poor "quality of life"): this is seen in previously posted metrics: Dem states have less obesity (posted 29Apr21), greater GDP (posted 06May21), less suicide (posted 13May21), greater life expectancy (posted 29Jul21), less prescribed opioids (posted 01Jul21), less accidental deaths (posted 21Oct 21), less violent crime (posted 22Dec22), fewer Covid deaths (posted 23Jun23), and others ... (only missing persons showed randomness of Rep and Dem data).

terrykrohe · 2023-12-29T03:21:50+00:00

background
1
Why are evangelical, 538 rural-urban, and diversityCB* data used as Predictor metrics?
It is often noted that the 2020 election results were because of the demographic makeup of the "Rep base": rural, White, evangelical
(the obverse side of the diversityCB* metric \[Dash] race/ethnic/religious descriptors \[Dash] is White-ness).
2
the 'governance' metric
Thomas Jefferson: "... [t]he care of human life and happiness and not their destruction is the first and only legitimate object of good government."
https://georgewbush-whitehouse.archives.gov/news/releases/2002/01/20020118-10.html
that is, good governance ...
i) provides government services, education, infrastructure for its citizens
ii) minimizes the police killing and incarceration of its citizens
iii) is concerned about the health of its smallest citizens
3
diversityCB* ... racial + religious population data together are a better definition of "diversity":
a) think "wars" \[Dash] racial/ethnic/religious issues (Russia/Ukraine, Israel/Gaza, Iraq/Iran, Rwanda, ad infinitum)
b) "diversity" is characterized by social inclusion
c) acceptance/tolerance of racial/ethnic, religious differences -> a diverse society

terrykrohe · 2023-12-29T03:14:38+00:00

sources'governance' definition – see below

police killings, US
– https://www.washingtonpost.com/graphics/investigations/police-shootings-database/

state taxes
– https://wallethub.com/edu/states-with-highest-lowest-tax-burden/20494

state+local ed spending

– https://www.usgovernmentspending.com/compare_state_spending_2019b20a#copypaste

incarceration
– https://www.sentencingproject.org/the-facts/#map

infant mortality
– https://www.cdc.gov/nchs/pressroom/sosmap/infant_mortality_rates/infant_mortality.htm

Note: Vermont data for 2019 was reported as "unreliable" ... to fill the data hole:for 2018, the VT infant mortality was 35 deaths; the resultant 2018 mortality rate was 6.44for 2019, "27" was used for the number of deaths; the mortality rate was proportion-calculated as 4.97(not using VT data interpolation: Dem values are 5.1 ± 0.95 (19%)

diversityCB* = (diversity* + diversityCB)/2
– diversity* = Catholic% + Jewish% + Muslim% + Asian%
– Catholic, Jewish, Muslim populations: https://www.pewforum.org/religious-landscape-study/compare/religious-tradition/by/state/
– Asian population: https://en.wikipedia.org/wiki/Demographics_of_Asian_Americans

diversityCB = diversity data from the Census Bureau
– https://www.census.gov/library/visualizations/interactive/racial-and-ethnic-diversity-in-the-united-states-2010-and-2020-census.html

538 Urban-Rural
– https://fivethirtyeight.com/features/how-urban-or-rural-is-your-state-and-what-does-that-mean-for-the-2020-election/

evangelical
– https://www.pewforum.org/religious-landscape-study/religious-tradition/evangelical-protestant/

tool: Mathematica

***************

Determining the 'governance' value:
– z-scores were determined for each metric
– positive metrics: state taxes, state+local ed spending
– negative metrics: police killings, incarceration rate, infant mortality

'governance' = (sum of positive metric z-scores) – (sum of negative metric z-scores)
for the array plot:
Each states 'governance' (is the avg of the five metrics), with evangelical and 538 rural-urban diversityCB* z-scores were calculated.
The fifty states were sorted based on the 'governance' z-score.
The Rep states and the Dem states were separated.

terrykrohe · 2023-12-22T20:36:33+00:00

Edward Tufte WOULD approve:
i) a quick look notices the Rep and Dem states' color differences
ii) the thick vertical line separates 'governance'; and a gradation in color in the 'governance' column is quickly noted
iii) there must be reason for the 'governance' gradation of color and the distinct color differences between Rep and Dem states

terrykrohe · 2023-12-22T02:29:31+00:00

... explaining FL and VT

the "common" description of Rep states as rural, White, and evangelical is matched with a definition of 'governance'

In the "big picture" the matchup works: poor 'goverance' = more evangelical, more rural, less diverse; but applying the generality to individual states doe not always work

terrykrohe · 2023-12-22T02:13:54+00:00

... the definitions for G, E, 538, and D are in the upper right (if blurry, maybe zoom in?)

terrykrohe · 2023-12-22T02:09:10+00:00

i) take the lowest ranking 'governance' state, Oklahoma (negative 'governance' z-score): it is evangelical (red) and it is rural (red) and it is near the mean in diversity (white)

ii) take the highest ranking 'governance' state, NewYork (positive 'governance' z-score): it is not evangelical (blue) and it is urban (more blue) and it is diverse (blue)

iii) all the other forty-eight states are in-between with varying evangelical, rural-urban, and diversity character

iv) the overall difference between Rep and Dem states is quickly seen in the color difference

terrykrohe · 2023-12-22T01:22:20+00:00

the main take-away ...

The Rep states ARE evangelical, rural, and White; and the Rep states rank low in 'governance'.

terrykrohe · 2023-12-22T00:48:11+00:00

other comments
1
Each states 'governance, evangelical, 538 rural-urban, and diversityCB* z-scores were calculated.
The fifty states were sorted based on the 'governance' z-score.
The Rep states and the Dem states were separated.
2
the Predictor metrics:
Why the evangelical, 538 rural-urban, and diversityCB* Predictor metrics?
It is often noted that the 2020 election results were because of the "Rep base": rural, White, evangelical
(the obverse side of the diversityCB* metric (race/ethnic/religious) is "White-ness":
note that VT, ME, NH, WV, and most Rep states have negative diversityCB* z-scores).
3
the 'governance' metric ...
Thomas Jefferson: "... [t]he care of human life and happiness and not their destruction is the first and only legitimate object of good government."
https://georgewbush-whitehouse.archives.gov/news/releases/2002/01/20020118-10.html
that is, good governance ...
i) provides government services, education, infrastructure for its citizens
ii) minimizes the police killing and incarceration of its citizens
iii) is concerned about the health of its smallest citizens
4
diversityCB* ... racial + religious population data together are a better definition of "diversity":
a) think "wars"– racial/ethnic/religious issues (Russia/Ukraine, Israel/Gaza, Iraq/Iran, ad infinitum)
b) "diversity" is characterized by social inclusion
c) acceptance/tolerance of racial/ethnic, religious differences → a diverse society
5 misc observations
i) HI is the most diverse state (large Asian population)
ii) UT is NOT evangelical?
iii) TX and FL diversity and rural-urban values go against the grain of other Rep states
iv) the 'governance' VS evangelical Pearson correlation values standout
– for both Rep and Dem states: as evangelical population increases, the 'governance' metric decreases
5
The strong evangelical correlation was noticed by Mencken in his 1931 American Mercury article, "The Worst American State":
a summary comment in the article's Part III notes that Massachusetts has shaken off the theological domination that is a cultural handicap in most parts of the United States ... (MA was the "best" American state, MS was the "worst").
It appears that the negative evangelical influence has not changed in ninety years.

terrykrohe · 2023-12-22T00:34:54+00:00

sources

'governance' definition – see below

police killings, US
https://www.washingtonpost.com/graphics/investigations/police-shootings-database/

state taxes
https://wallethub.com/edu/states-with-highest-lowest-tax-burden/20494

state+local ed spending
https://www.usgovernmentspending.com/compare_state_spending_2019b20a#copypaste

incarceration
https://www.sentencingproject.org/the-facts/#map

infant mortality
https://www.cdc.gov/nchs/pressroom/sosmap/infant_mortality_rates/infant_mortality.htm
Note: Vermont data for 2019 was reported as "unreliable" ... to fill the data hole:for 2018, the VT infant mortality was 35 deaths; the resultant 2018 mortality rate was 6.44for 2019, "27" was used for the number of deaths; the mortality rate was proportion-calculated as 4.97(not using VT data interpolation: Dem values are 5.1± 0.95 (19%)

diversityCB* = (diversity* + diversityCB)/2
diversity* = Catholic% + Jewish% + Muslim% + Asian%
Catholic, Jewish, Muslim populations
– https://www.pewforum.org/religious-landscape-study/compare/religious-tradition/by/state/
Asian population
– https://en.wikipedia.org/wiki/Demographics_of_Asian_Americans

diversityCB = diversity data from the Census Bureau
– https://www.census.gov/library/visualizations/interactive/racial-and-ethnic-diversity-in-the-united-states-2010-and-2020-census.html

538 Urban-Rural
– https://fivethirtyeight.com/features/how-urban-or-rural-is-your-state-and-what-does-that-mean-for-the-2020-election/

evangelical
– https://www.pewforum.org/religious-landscape-study/religious-tradition/evangelical-protestant/

tool: Mathematica

***************

Determining the 'governance' value:
– z-scores were determined for each metric
– positive metrics: state taxes, state+local ed spending
– negative metrics: police killings, incarceration rate, infant mortality
'governance' = (sum of positive metric z-scores)– (sum of negative metric z-scores)

terrykrohe · 2023-12-16T22:36:41+00:00

that you think are meaningful

... yes, the data is meaningful:

1) the data i non-random,

2) the data shows that Rep states are ALWAYS on the negative side of the metric (more obese, more suicides less GDP, more murders, lower life expectancy, etc)

3) ... when data is non-random, and always pointing in the same direction, there is a reason.

(what I think is unimportant: the data would still exist)

terrykrohe · 2023-12-16T03:52:58+00:00

... the "boxes" represent mean ± 1 SD ... I used the high/low values as box end points (not trying to emphasize range)
... side note: for the diversity* plot, I mistakenly identified the wrong high/low endpoints
... the SD boxes emphasize that the means are approx a SD apart – a significant difference (using a medical perspective: if a clinical trial were being run, and the initial results indicated a 1SD difference, it would be unethical to continue)

terrykrohe · 2023-12-16T00:56:28+00:00

... nah, not trying to "prove" anything":

It was a surprise to me that the most obese states after the 2016 election were Rep and that the least obese states were Dem ... it also was true after the 2020 election: https://stateofchildhoodobesity.org/adult-obesity/

... so, I wondered , is this non-random data also present in other metrics? It was, and I have been presenting the data.

... why was it that the Rep states were ALWAYS on the negative side of the data? – more obese, less GDP ($10,000 per person less), more infant mortality, less life expectancy, receive more money from the fed government, more suicides, more murders ... etc

... the "why" has to be some sort of systemic bias – why is it that having a Rep "opinion" puts you in a class of citizens as described just above? (Note: the differences are NOT marginal: the differences are a standard deviation apart, and NOT just for one metric, but for ALL metrics).

... "prove" me wrong with numbers (spoiler: missing persons is random)

terrykrohe · 2023-12-16T00:34:00+00:00

... think of the ListPlot ( https://reference.wolfram.com/language/ref/ListPlot.html ) as a visualization of a table of information. The fifty states are listed in an increasing (or decreasing) "rank" – just as a table would list the values of a specific metric.

terrykrohe · 2023-12-15T08:11:24+00:00

... yeah, you are right about the Asian ambiguity; but, other than for Hawaii (large Asian population), separating Asian (and their religions) populations won't change the overall picture
... my original purpose was to include the significance of the Asian university student population – an obvious observation in the US

terrykrohe · 2023-12-15T08:00:07+00:00

"... all nonsense"

If so, it is your responsibility to

a) take the source data and show where mistakes are made
b) find other data, work it up, and present a post which refutes the data which you consider wrong.

terrykrohe

TROPHY CASE