A Crime in Which the Offender Uses Force against the Victim Essay Example

purposes of this analysis we will only be able to examine a handful.In areas where poverty is abundant, individuals often find themselves with fewer options in life. These people are not only more likely to have adverse health issues, but are also more likely to lack education. These factors lead to a higher risk of delinquent behavior and ultimately higher violent crime rates.

Also, as population density increases we believe that violent crime rates will rise. As cities develop and expand, human interactions tend to increase. These interactions create more opportunities for violent crime.We also expect greater percentages of minority groups to influence violent crime rates. These groups do not always have access to all opportunities offered by the community. Although social programs are in place to provide guidance to these groups, they do not always take advantage of such programs and often resort to gang membership and lives of crime.

After performing statistical analysis on the violent crime data, we are able to provide a few recommendations that will help determine whether or not a particular population is a good candidate for penal reform. The variations within conditions that we chose to analyze (unemployment rate, mobile home percentage, percentage of population over 65 living in poverty, percent white, median age, percentage of development and location) account for about 60% of the variation that we see among violent crime rates.

Also, the presence of minorities appears to influence violent crime rates. As seen in the table, the percentage of whites in the area has a negative relationship with violent crime rates; statistics suggest that for every 5% decrease in white population (5% increase in minorities), there is an

additional violent crime per 1,000 people. We also see strong statistical evidence that percentage of urban development influences violent crime rates. As we expected, for every 7% increase in development, we see an additional violent crime per 1,000 people.

However, after analyzing the affect that percentage of mobile homes had on the violent crime rate, we found that there is no statistical evidence suggesting an influential relationship. Likewise, we observe that there is little to no statistical evidence suggesting that median age or median income has any influence on violent crime rates.

Our thinking behind a quadratic relationship was that after an area reaches a certain percentage of development, human interactions will reach a point at which violent crime rates will begin to fall. Nobody wants to commit a crime when there’s a huge group of people around to witness it! After completing the analysis, we find that statistical evidence suggests a linear relationship etween development and violent crime rates is much more likely. We also decided to test if any of our variables had an interactive relationship with each other.Statistical evidence suggests that percentage of urban development and sates in the North East indeed have an interactive relationship just as we had predicted. We also performed a logarithmic transformation to overcome any violations to the equal-variance assumption.

On average, for each additional 1% increase in population over 65 under the poverty level, we would expect a 0. 75% increase in violent crimes per 1,000 people. The percent of urban development appears to have the greatest impact on violent crime rates (1. %) and statistical evidence now suggests that the log of percentage of mobile homes in fact

influences violent crime rates. Conclusion Through statistical analysis we have determined that unemployment rate, percentage of the population over 65 living under the poverty level, percent white, and percent of urban development all influence violent crime rates throughout the United States. As an elected official attempting to close several prisons around the country, this study should help you determine which areas are at high risk of high violent crime rates (poor targets for prison closures).

However, as seen by our relatively low coefficient of determination (about 60%) there are numerous other components that add to the variation observed between violent crime rates (making up the other 40%). Perhaps local economies, drug use, alcoholism or family values influence violent crime rates. Unfortunately Sandy Camel Consulting does not have access to this data, so we are unable to consider such scenarios in our statistical analysis. We can, however, make a few recommendations regarding the data that we do have.First, you must determine exactly what level of violent crime you are willing to accept in an area in which you wish to apply prison budget cuts and/or closures.

Based on the data, we feel that any target area should ideally have violent crime rates below 2. 57 per 1,000 people. The best candidates for prison budget cuts/removals will have low unemployment rates (less than 4. 6%), percentage of population over 65 living under the poverty level below 7%, percent of the population that is white more than 90%, and percent of urban development below 67%.