Data Analysis of Big Murder

Three opposing viewpoints, each with an equal chance of influencing policymakers’ choice of approach to the issue of rising murder rates in the US, have been impeding an early response to the problem’s emergence. Which of the three opposing arguments can be chosen as the key focus around which the strategy should be centered is what the decision-makers in charge of making policy need to know. These three arguments center on the concepts of economics, rational deterrence, and environmental considerations. This paper analyzes historical murder rate data of 44 American country to establish a valid argument for the policy makers to move on.

Data Analysis

Rational Deterrence

Punishment acts as a deterrent on murder. Would-be murderers rationally consider and respond to the likely consequences of their actions when deciding whether or not to commit homicide. In order to test the validity of the argument we perform a linear regression analysis on the dependent variable murder_rate and two independent variables pr_execution and time_for_mrder. The varibable murder_rate gives the number of murders per 100000 deaths in the observed state, whereas variables pr_execution and time_for_mrder give the probability of execution of a convicted murderer and the median months of time served by the murderer who gets released in the year when the study took place respectively.

The regression output is presented in table 1, which shows that the increase in execution rate has a positive effect on the rate of murders, which indicates that whether execution is carried out or not, murder will not stop. On the other hand, murder rate has a small negative relationship with the time for murder, which reveals the fact that individual murder tends to commit no murder once they are punished for their crime but the execution rate has no valid relationship that can be shown as a solid argument. Figure 1 and 2 exhibit the graphical representation of the relationship, from where we can see a small relationship exhibits between punishment and murder rate, which leads us to the fact that we cannot justify the argument of rational deterrence.

Economic Factor

Murderers are victims of circumstance. Crime is not a rational response to punishment, but often driven by criminal employment being the only economic activity. Lack of economic opportunity causes property crimes which sometimes lead to murders. So, the murder rate is driven by economic factors. In order to test the validity of the claim in the argument, let us perform a linear regression analysis to find if there any statistically significant relationship exists between the murder rate and the employment status of the murderer. Here, murder_rate will be dependent variable and unemp will be the independent variable. Table 2 gives a report on the regression analysis preformed. The result of the regression coefficient is about -76.38, which is a large negative regression relationship between the two variables. About 76% murder can be explained by the unemployment rate prevailing within the state. This makes us reject the argument, since the statistical test suggest that the more is the unemployment rate, the less is the murder rate, which is in opposition to the initial claim.

Surrounding Environment

Murderers’ behavior is not rational, but the result of crimes of passion and effectively irrational behavior grounded in poor socialization and difficult and depressing life circumstances. In order to find the validity of this claim, let us perform a regression analysis on the dependent variable murder_rate and independent variables including poverty,inc, urban, home etc. Table 4 reveals the output of the regression analysis. From the table, it becomes obvious that the first three variables except home has negative relationship with the dependent variable modern rate. The regression analysis shows that as the poverty increase, murder rate decreases, income increases, murder rate decreases, living in urban areas increases the rate of murder with its decrease. On the other hand, the home factor has the positive prelateship with the murder rate from which we can say that the two-parent family are influencing the number of murders in the society. We can conclude that this argument is highly valid from the perspective of statistical analysis. It exhibits that apart from all factors, the surrounding factors have the major influence over turning a person from a common people to a murder.

Table 1: Regression Analysis on Crime Rate and punishment variables

B

Std. Error

(Constant)

9.832

1.457

pr_execution

14.056

8.446

time_for_murder

-.039

.009

Figure 1: Scatter plot between murder rate and execution probability

Figure 2: Scatter plot between murder rate and time served by convicted murders

Table 2: Regression analysis report between murder rate and unemployment

B

Std. Error

Beta

t

Sig.

(Constant)

8.975

2.069

4.338

.000

unemp

-76.388

41.978

-.270

-1.820

.076

Table 3: Regression analysis report between murder rate and the dependent variables specified.

B

Std. Error

Beta

t

Sig.

1

(Constant)

13.790

56.501

.244

.808

poverty

-20.353

31.799

-.114

-.640

.526

inc

-.007

.002

-.639

-3.328

.002

urban

-2.129

6.012

-.073

-.354

.725

home

12.009

53.493

.034

.224

.824

a. Dependent Variable: murder_rate