I don't know how to write the conclusion of these findings when looking at the graphs and data shown below.

America is at the top of the scale for the greatest number of people in jail and has the 6^th highest incarceration compared to the rest of the world at 531/100,000. While the majority of these encounters are not fatal, we do see fatal police shootings in the United States have been trending up over the last few years. This topic has been a very hot button issues in our society lately as many of these high-profile fatalities have been accused on being related to race and other discriminatory factors, while the law enforcement organization states that they are reacting to high risk factors. We believe that the data can show us a clearer picture and remove the emotion and the politics from these charged events especially when they are still fresh. We propose that there are contributing factors to the increase in police fatalities that can be determined with a regression analysis of the perpetrators' City / State, Age / gender, and their armed / feeling status. We will also explore if there are any commonalities or multiple factors that seem to be combined that are contributing at a higher rate.

The initial analysis we are investigating would be comparing the highest state fatalities compared to those states' overall arrests. The basic hypothesis would be that the states with the most arrests would also have the highest fatality rate. This basic assumption did prove mostly true states marked in green had the highest fatalities and arrests, with the exception of GA which overall lower total arrest (in 2019).

State	<1>		2015	2016	2017	2018	2019	2020	2021	2022	2023	Grand Total	Total Arrests	Fatal %
AZ			42	49	44	61	35	45	37	52	31	396	235594	0.00297%
CA			190	139	160	116	135	147	140	128	77	1232	1014785	0.00069%
CT			2	3	5	1	4	5	2	1	1	24	92925	0.00753%
DE			3	1	6		2	2	2	2	1	19	30038	0.02330%
FL			61	60	57	64	63	93	50	62	48	558	679072	0.00103%
GA			29	27	29	44	37	40	51	49	25	331	59704	0.01172%
ND			1	1	3	4		3	3	3	1	19	32647	0.02144%
RI				2	1	1			1	1	1	7	25403	0.02756%
TX			101	81	70	83	108	82	95	118	63	801	624840	0.00112%
VT			1	2	1	2	3		2	2	1	14	15719	0.04453%

Based on our initial hypothesis we would also expect the inverse to be true that the states with the lowest total arrests would also have the lowest number of fatalities. That was incorrect based on the data we can see many other states with lower total arrests but higher number of fatalities. While VT was in the lowest of state arrests and fatalities places like CT had high arrest and fatalities. Another very interesting outlier piece of data is the state of Alabama, they had one of the lowest total arrests only 1831 in 2019 and they also had a lower fatal count of 11 but because of the low total arrest they had the highest percentage of arrest compared to the rest of the US at 0.38%. One of the takes aways from this analysis is how to properly view this data, should this data be broken down by state or are there other factors that are more important. We took the highest and lowest states and performed another analysis comparing based on fatalities to total state population. The result of this type of analysis should help reduce the impact of state size and put everyone on a more level playing field and have less biased perception. This data is fairly straightforward as the states with the highest fatalities have the highest rate per million people. The only exception here likely due to the low population is Vermont which had fatality rate that is more in line with the higher states rather than the lower states.

State	Largest	2019	2019 total	Percentage	Population	Fatalities per million of population
CA	1232	135	1,014,785	0.0133%	39440000	3.422920892
TX	801	108	624,840	0.0173%	28990000	3.725422560
FL	558	63	679,072	0.0093%	21490000	2.931596091
AZ	396	35	235,594	0.0149%	7291843	4.799883925
GA	331	37	59,704	0.0620%	10630000	3.480714958
	Smallest
RI	7	0	25,403	0.0000%	1056440	0.000000000
VT	14	3	15,719	0.0191%	643085	4.665013179
ND	19	0	32,647	0.0000%	763724	0.000000000
DE	19	2	30,038	0.0067%	1018396	1.963872600
CT	24	4	92,925	0.0043%	3626205	1.103081596

There could be many different reasons for this that we will work to break down in the following analysis.