U.S. Customs and Border Protection

The U.S. Customs and Border Protection (CBP) is a law enforcement agency that secures the nation’s borders. The organization engages in the seizure of drugs and records the number of drugs seized every year. From the given statistics, cocaine was the second most seized drug after marijuana. The amount of heroin seized was relatively low. For this analysis, the variables chosen were that of ‘Heroin at points of entry’ and ‘Cocaine at points of entry’ (“U.S. CBP drug seizure statistics”, n.d.). The study aimed to analyze the relationship between the variables of cocaine seized at points of entry and heroin seized at points of entry.

An analysis of the variables would explain whether the amount of cocaine seized over the years can be used to explain the growth or decrease in the amount of heroin seized. The relationship can further explain whether there is a correlation between the amount of cocaine trafficked and heroin trafficked. Therefore, ‘cocaine at points of entry’ was chosen as the independent or explanatory variable while ‘heroin at points of entry’ was chosen as the dependent variable or response variable. It is expected that a decrease in the amount of cocaine being seized would increase the amount of heroin being seized. As cocaine becomes less popular, heroin is likely to be seized more often as it becomes a popular alternative drug.

Analysis 

Scatterplot, Correlation Coefficient, and Outliers 

The analysis was done by first generating a scatterplot as shown in figure 1. The correlation coefficient was computed as -0.41533486 . There was a negative correlation between the amount of heroin and cocaine seized. However, the value of -0.4 was considered a weak correlation. The weak correlation could have been caused by outliers in the x and y variables of (47945,4813) and (39075,3990).

Figure 1 

Linear Association Model 

A simple linear regression statistical analysis yielded the following results.

Simple linear regression results: Dependent Variable: Heroin (At Points of Entry) Independent Variable: Cocaine (At Points of Entry) Heroin (At Points of Entry) = 5748.2928 - 0.029559213 Cocaine (At Points of Entry) Sample size: 7 R (correlation coefficient) = -0.41533486 R-sq = 0.17250304 Estimate of error standard deviation: 611.1454 

Parameter estimates: 

Analysis of variance table for the regression model: 

Figure 2 

The linear regression model was as shown in figure 2. It had a slope of - 0.029559213 which indicated a negative relationship between the variables. The y-intercept was 5748.2928 showing that when the cocaine seized at points of entry was zero, it is expected that the amount of heroin seized would be as high as 5748.2928. The correlation coefficient of -0.41533486 had also predicted a negative relationship. The negative relationship showed that an increase in the amount of cocaine seized at points of entry would result in a decrease in the amount of heroin seized at points of entry. The negative correlation of -0.4 was considered weak and this was supported by the p-value > 0.05. A p-value of 0.3541 showed that the relationship was not statistically significant.

Conclusion 

The statistical analysis sought to identify the relationship between cocaine seized at points of entry and heroin seized. The correlation coefficient showed a negative relationship between the given variables. It was expected that a decrease in the amount of cocaine seized would increase the amount of heroin seized. However, the relationship was considered weak due to the value of the correlation coefficient and a p-value that was greater than 0.05.

Reference

U.S. CBP drug seizure statistics. (n.d.). Stat Crunch. https://www.statcrunch.com/app/index.php?dataid=2896864