Violent Crime in America ECON 240A Group 4 Thursday 3 December 2009.

Violent Crimein America

ECON 240A

Group 4

Thursday 3 December 2009

QuickTime™ og en-dekomprimerer

kreves for å se dette bildet.

Table of Contents

IntroductionDataDescriptive StatisticsStatistical Analysis

What are the causes of crime? Our team hypothesized that there may be

five factors contributing to the prevalence of violent crime in a specific jurisdiction Public Expenditures on Law Enforcement and

Public Safety Public Firearm Ownership Education Income Ethnicity

QuickTime™ og en-dekomprimerer

kreves for å se dette bildet.

Violent crimes per 100,000 people.

Our Measures of Violence

Data From the 50 States and DC

Education: Percentage of public high school freshman going on to graduate high school.

Poverty: Per capita income.

Public Spending: per capita expenditures on state and local law enforcement and corrections.

Ethnicity: percent of population that is non-white.

Firearms percent of households that own guns.

Freshmen that Graduate HS

Cost of State and Local Law

Enforcement

Guns Per

Household

Income Per Capita

Percent Non-White (Minorities)

Freshmen that Graduate HS

Cost of State and Local Law

Enforcement

Guns Per

Household Income Per Capita

Percent Non-White (Minorities)

Negative Correlations

Freshmen that Graduate HS and Percent Non-White (Minorities).

Expenditures on State and Local Law Enforcement and Guns Per Household .

Guns Per Household and Income Per Capita.

Guns Per Household and Percent Non-White (Minorities).

Qu ickTim e™ og en-d ekom p rim erer

kreves for å se d ette b ild et.







Positive Correlations

Cost of State and Local Law Enforcement and Income Per Capita

Cost of State and Local Law Enforcement and Percent Non-White (Minorities)

Correlations to Violence

Positive

Percent Non-White (Minorities) Income Per Capita State and Local Law Enforcement Expenditures

Negative Guns Per Household

Freshmen that Graduate HS All negative and positive correlations are statistically significant

Violent Crimes vs. Ethnicity

Dependent Variable: VIOLENTCRIMEPER

Method: Least Squares

Date: 12/02/09 Time: 13:25

Sample(adjusted): 1 51

Included observations: 51 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob.

PERCENTNONWHITE 9.250446 1.547537 5.977530 0.0000

C 191.0509 47.71586 4.003929 0.0002

R-squared 0.421698 Mean dependent var 431.6078

Adjusted R-squared 0.409896 S.D. dependent var 238.3361

S.E. of regression 183.0855 Akaike info criterion 13.29621

Sum squared resid 1642495. Schwarz criterion 13.37197

Log likelihood -337.0533 F-statistic 35.73086

Durbin-Watson stat 1.972195 Prob(F-statistic) 0.000000

H(0): t0.025,50 =2.009 n=51 H(1): t0.025,50 ≠ 2.009

α=5

Percent of population that is non white is a significant explanatory variable for violent crimes per 100,000 capita

Violent Crimes Regressed Against Possible Factors Data

Possible Factors R-squared t-Statistic

Non-whites 0.421698 0.0000

Income per capita 0.098054 0.0253

Guns per household 0.108723 0.0181

Expend. on public security 0.333322 0.0000

Freshmen to graduate HS 0.322653 0.0000

Multiple Regressions

Dependent Variable: VIOLENTCRIMEPER Method: Least Squares Date: 12/02/09 Time: 14:16 Sample(adjusted): 1 51 Included observations: 51 after adjusting endpoints


AVGFRESHMANGRAD

-1402.599 314.2518 -4.463295 0.0001

EXPEDITURECAPITA

0.570637 0.174902 3.262603 0.0021

HOUSEHOLDGUNS -252.7796 193.8526 -1.303978 0.1986 C 1285.674 309.1428 4.158834 0.0001

R-squared 0.535028 Mean dependent var 431.6078 Adjusted R-squared 0.505349 S.D. dependent var 238.3361 S.E. of regression 167.6252 Akaike info criterion 13.15652 Sum squared resid 1320615. Schwarz criterion 13.30804 Log likelihood -331.4913 F-statistic 18.02712 Durbin-Watson stat 2.134995 Prob(F-statistic) 0.000000

- Average freshman grad and expenditure per capita are significant.

- Households with guns are no longer significant.

-R-squared = 53,5%

Regression Diagnostic

0

2

4

6

8

10

12

-300 -200 -100 0 100 200 300 400 500

Series: Residuals

Sample 1 51Observations 51

Mean 1.69E-13Median -19.93613Maximum 538.9753

Minimum -346.9274Std. Dev. 162.5186

Skewness 0.502499Kurtosis 4.262888

Jarque-Bera 5.535431

Probability 0.062805

The Jarque-Bera statistic suggests that the residuals plot are normally distributed.

Multiple Regressions

•Minority group is still significant in explaining violence per capita.

•Income per capita is not a significant explanatory variable.

• Regression is significant. Prob(F-statistic) = 0.000



Date: 12/02/09 Time: 14:10




PERCENTNONWHITE 8.679604 1.597096 5.434616 0.0000

PERCAPITAINCOME 0.006128 0.004682 1.308896 0.1968

C -1.632381 154.6450 -0.010556 0.9916







Regression Diagnostic

0

2

4

6

8

10

12

-600 -400 -200 0 200 400

Series : Residuals

Sample 1 51

Observations 51

Mean 7.13E-14Median -25.67716

Maximum 503.4726

Minimum -581.7378

Std. Dev. 178.0950

Skewness 0.079576Kurtos is 4.774282

Jarque-Bera 6.743485

Probability 0.034330

The Jarque-Bera p-statistic suggests that the residuals are not normally distributed.

Data Issues

Residuals plot against fitted violent crime per capita

-700

-500

-300

-100

100

300

500

700

0 200 400 600 800 1000 1200 1400 1600

Fitted violent crime per capita

Re

sid

ua

ls

Residuals Regressions

Residuals plotted against the fitted violent crime

per capita.

White Heteroskedasticity Test:

F-statistic 20.93164 Probability 0.000000

Obs*R-squared 23.75862 Probability 0.000007

Results from the White Heteroskedasticity for violent crime per capita regressed against expenditures on state and local law

enforcement per capita.

Crime vs. Expenditures, DC Dummy



Date: 12/02/09 Time: 23:17




DCEXPENDITUREDUM 619.4809 185.5877 3.337941 0.0016

EXPEDITURECAPITA 0.770615 0.046868 16.44227 0.0000







Residuals plot against fitted violent crime per capita with DC dummy

-400

-300

-200

-100

0

100

200

300

400

500

0 200 400 600 800 1000 1200 1400 1600

Fitted violent crime per capita

Re

sid

ua

ls

Dummied out District of Columbia

Residuals plotted against the fitted violent crime per capita when District of Columbia is dummied out.

White Heteroskedasticity Test:

F-statistic 1.429467 Probability 0.246037

Obs*R-squared 4.264286 Probability 0.234304

Results from the White Heteroskedasticity Test for violent crime per capita regressed against expenditures on state and local law enforcement per capita when District of Columbia is dummied.

Conclusions

Income per capita, education, and ethnicity explain violent crimes more significantly than the other explanatory variables explained.

Complications:

There are strong correlations between independent variables.

Heteroskedascity was revealed within the regressions.

Hawaii and D.C. skewed the regressions

Residuals were non-normal

Crime is more prevalent in concentrated areas of high income per capita, low education, and diverse ethnicities.

Works Cited

Crime: US Justice Department, Federal Bureau of Investigation, Uniform Crime Report

Income: InfoPlease Firearm: Education: Ethnicity: US Commerce Department, Bureau of

the Census Public Safety Expenditures: US Justice

Department, Office of Justice Statistics, Expenditures and Employment Statistics

Violent Crime in America ECON 240A Group 4 Thursday 3 December 2009.

Documents

Transcript of Violent Crime in America ECON 240A Group 4 Thursday 3 December 2009.