Essay on Correlation

A good example of the correlation between variables is the measure of the GPA against the amount of school time attended. If this measure is done, it is found that with increased school time, the GPA is more likely to increase, and with reduced school time, the GPA is likely to reduce. Another example is that of age and time taken to run a lap. With increased age, the time taken to run a lap increases, and with reduced age, the time reduces. A third example is that of a correlation is absence class and SAT scores, with increased absences, SAT scores are seen to decrease. In looking at the correlation, three types of explanations are normally used, direct causation, indirect causation, and coincidence.

Direct causation means that the change in one variable causes a direct effect in either direction in the second variable. Indirect causation means that the first variable affects another variable, which in turn affects the second variable. For example, variable A causes an increase in variable B, which in turn causes a decrease in variable C. conversely, coincidence means that the two variables just happen to be correlated, without any connection of common source. This number would increase by a big margin. No, the question is, would this reduce the crime attributed to drug use and abuse? Maybe, if the crimes committed in the process of acquisition and transportation of the drug falls by a far greater margin than the crime committed by fellows exposed to drugs and have nothing else to do.

Assuming that now, of 1 million hardcore drug users, at least 200,000 of them have been incapacitated by the drugs, and are unable to hold to jobs or employment. This means that they will have no source of income, meaning that they will resort to criminal activities to survive. With the legalization of drugs, there will be more users, say 10 million, and using a comparable margin, we would expect to have 2 million of them in the streets not having jobs because of incapacitation (French et al 2010), meaning that crime indices would generally increase. The sample correlation coefficient found is -0. implying that there is a weak negative correlation between the number of people consuming cocaine and heroin and the expenditure on law enforcement per person.

While this statistical conclusion is correct, it does not depict the actual measure of the increase or reduction in crime numbers with an increased usage of drugs. For example, it could be that some cities have a better training for individual police officers, meaning that the cost per law enforcement officer is not affected by the number of individuals. In this case, a better statistical analysis would be to use actual numbers of crime cases with different numbers of people using drugs in the cities. Chronic drug use and crime. Substance Abuse, Vol.