Correlation values with the value to predict

Correlation values with the value to predict

This is the list between our value to predict (the number of violent crimes in USA).

Variable	Correlation	Variable	Correlation	Variable	Correlation
PctIlleg	0.738	PctRecImmig10	0.2643	PersPerOwnOccHous	-0.1244
racepctblack	0.6313	PctRecImmig8	0.2532	PctWorkMom	-0.1506
pctWPubAsst	0.5747	PersPerRentOccHous	0.2483	pctWFarmSelf	-0.1531
FemalePctDiv	0.556	PctImmigRec8	0.2481	PctSameHouse85	-0.1554
TotalPctDiv	0.5528	PctRecImmig5	0.248	AsianPerCap	-0.1556
MalePctDivorce	0.5254	PctRecentImmig	0.2308	OwnOccHiQuart	-0.1721
PctPopUnderPov	0.5219	PctImmigRec5	0.216	OwnOccMedVal	-0.1907
PctUnemployed	0.5042	LandArea	0.1968	whitePerCap	-0.2093
PctHousNoPhone	0.4882	PctForeignBorn	0.1944	OwnOccLowQuart	-0.2105
PctNotHSGrad	0.4834	PctImmigRecent	0.1719	RentHighQ	-0.2323
PctVacantBoarded	0.4828	PctUsePubTrans	0.1538	MedRent	-0.2399
PctHousLess3BR	0.4745	agePct12t29	0.1534	RentMedian	-0.2405
NumIlleg	0.471	PersPerFam	0.1407	PctSpeakEnglOnly	-0.2415
PctPersDenseHous	0.4529	pctWSocSec	0.18	HispPerCap	-0.2446
NumUnderPov	0.4476	agePct16t24	0.0993	RentLowQ	-0.2518
HousVacant	0.4214	pctUrban	0.082	blackPerCap	-0.2754
PctLess9thGrade	0.4111	PctSameCity85	0.0756	pctWWage	-0.3055
PctLargHouseFam	0.3835	agePct65up	0.0672	PctBSorMore	-0.3147
NumInShelters	0.3758	MedOwnCostPctInc	0.0638	PctHousOccup	-0.319
population	0.3672	agePct12t21	0.0605	PctEmploy	-0.3316
PctWOFullPlumb	0.3645	MedOwnCostPctIncNoMtg	0.0538	PctOccupMgmtProf	-0.3391
numbUrban	0.3629	racePctAsian	0.0376	perCapInc	-0.3521
LemasPctOfficDrugUn	0.3486	PctVacMore6Mos	0.0213	MedNumBR	-0.3574
NumStreet	0.3403	PctSameState85	-0.0195	medIncome	-0.4242
MedRentPctHousInc	0.325	PctWorkMomYoungKids	-0.0225	medFamInc	-0.4391
MalePctNevMarr	0.3046	householdsize	-0.0349	PctHousOwnOcc	-0.4707
PctNotSpeakEnglWell	0.3	PersPerOccupHous	-0.0397	PctPersOwnOccup	-0.5255
PctOccupManu	0.2956	PctEmplManu	-0.0449	pctWInvInc	-0.5763
PctLargHouseOccup	0.2948	PctEmplProfServ	-0.0715	PctTeen2Par	-0.6616
NumImmig	0.2942	PctBornSameState	-0.0772	PctYoungKids2Par	-0.6661
racePctHisp	0.2931	indianPerCap	-0.0909	racePctWhite	-0.6848
PctImmigRec10	0.2915	pctWRetire	-0.0984	PctFam2Par	-0.7067
PopDens	0.2814	MedYrHousBuilt	-0.11	PctKids2Par	-0.7384

So, we can see the most correlated values on this page. PctIlleg (percentage of kids born to never married) and racepctblack ( percentage of population that is african american) are very correlated with our value to estimate. I'm french and in my point of view the race has no interest. For me, it's not the race but the fact that people can live in poor area with more public assistance as the next correlated value named pctWPubAsst.

At the inverse , we cans see the racePctWhite (percentage of population that is caucasian), PctFam2Par (ercentage of families (with kids) that are headed by two parents) and PctKids2Par (percentage of kids in family housing with two parents) completing the fact that a stable family (strangely white by the race) is a decisive information to not have violent crimes on the area.

pctUrban is poorly correlated with our value. The fact of be in an urban area or not is not decisive to estimate our value.

PctSameState85 (percent of people living in the same state as in 1985 (5 years before)) and PctVacMore6Mos  (percent of vacant housing that has been vacant more than 6 months) have not an important effect in our future estimation.

I think that you can continue to verify by yourself this important list of comparison by yourself.

I hope to give the most important facts or summary of these values.

The most important in my first feeling is to say a stable and an unstable family and social area should be an important cause of the number of violent crimes. This is really important, because it proves that a politic based on security purchase or based on non management of the unemployment or social priorities could not have a direct effect on the violent crimes existence. At the inverse , in stabilizing the social family an context with a complex social politic should permit to decrease considerably the violent crimes.

Correlation Matrix - On Individuals

Correlation Matrix

On Individuals

Our values are extremely correlated, evolution of each community and county is really similar. It is interesting because we can dig up a data range to create our model and dig up other values to verify our model easily. 

Correlation Matrix - On variables

Correlation Matrix

On variables

The correlation matrix permit a clear corresponding view between variables. We can see lots of variables extremely correlated or not or neutral .The last column and last line correspond to the variable to determine (the violent crimes). For this value,we discover a large data set with different connections. the correlation force is light and give a complex model to research.

On line/column 60 we see a value uncorrelated with the value between 50 and 70. The value 60 corresponding to the PctSpeakEnglOnly. and values extremely correlated between 50 and 59 correspond to the immigration information. It appear clearly that immigrants don't talk only English.
Between 61 and 70 , the variables describe the housing status. I don't understand why housing status can be an important information to speak only English. I suppose an important correlation with the immigrants statistics that create this link.

Variables between 79 and 87 correspond to the housing business that is extremely correlated.

A grouping area between 12 and 58 whose corresponding to information relative to the social environment.This variable list appear similar in back analysis.The list is:

• medIncome: median household income (numeric - decimal) 
• pctWWage: percentage of households with wage or salary income in 1989 (numeric - decimal) 
• pctWFarmSelf: percentage of households with farm or self employment income in 1989 (numeric - decimal) 
• pctWInvInc: percentage of households with investment / rent income in 1989 (numeric - decimal) 
• pctWSocSec: percentage of households with social security income in 1989 (numeric - decimal) 
• pctWPubAsst: percentage of households with public assistance income in 1989 (numeric - decimal) 
• pctWRetire: percentage of households with retirement income in 1989 (numeric - decimal) 
• medFamInc: median family income (differs from household income for non-family households) (numeric - decimal) 
• perCapInc: per capita income (numeric - decimal) 
• whitePerCap: per capita income for caucasians (numeric - decimal) 
• blackPerCap: per capita income for african americans (numeric - decimal) 
• indianPerCap: per capita income for native americans (numeric - decimal) 
• AsianPerCap: per capita income for people with asian heritage (numeric - decimal) 
• HispPerCap: per capita income for people with hispanic heritage (numeric - decimal) 
• NumUnderPov: number of people under the poverty level (numeric - decimal) 
• PctPopUnderPov: percentage of people under the poverty level (numeric - decimal) 
• PctLess9thGrade: percentage of people 25 and over with less than a 9th grade education (numeric - decimal) 
• PctNotHSGrad: percentage of people 25 and over that are not high school graduates (numeric - decimal) 
• PctBSorMore: percentage of people 25 and over with a bachelors degree or higher education (numeric - decimal) 
• PctUnemployed: percentage of people 16 and over, in the labor force, and unemployed (numeric - decimal) 
• PctEmploy: percentage of people 16 and over who are employed (numeric - decimal) 
• PctEmplManu: percentage of people 16 and over who are employed in manufacturing (numeric - decimal) 
• PctEmplProfServ: percentage of people 16 and over who are employed in professional services (numeric - decimal) 
• PctOccupManu: percentage of people 16 and over who are employed in manufacturing (numeric - decimal) ######## 
• PctOccupMgmtProf: percentage of people 16 and over who are employed in management or professional occupations (numeric - decimal) 
• MalePctDivorce: percentage of males who are divorced (numeric - decimal) 
• MalePctNevMarr: percentage of males who have never married (numeric - decimal) 
• FemalePctDiv: percentage of females who are divorced (numeric - decimal) 
• TotalPctDiv: percentage of population who are divorced (numeric - decimal) 
• PersPerFam: mean number of people per family (numeric - decimal) 
• PctFam2Par: percentage of families (with kids) that are headed by two parents (numeric - decimal) 
• PctKids2Par: percentage of kids in family housing with two parents (numeric - decimal) 
• PctYoungKids2Par: percent of kids 4 and under in two parent households (numeric - decimal) 
• PctTeen2Par: percent of kids age 12-17 in two parent households (numeric - decimal) 
• PctWorkMomYoungKids: percentage of moms of kids 6 and under in labor force (numeric - decimal) 
• PctWorkMom: percentage of moms of kids under 18 in labor force (numeric - decimal)