Property Crimes Case #49 Essay

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Property Crimes Case #49 GM 533 – Applied Managerial Statistics February 19, 2012 Introduction I chose Case #49, Property Crimes because I work in a State Correctional Institution and work with inmates on a daily basis. I am curious if some of the assumptions I have, or hear from co-workers, are supported with data. Most of us in the prison system assume that inmates are uneducated, poverty stricken, gang-oriented individuals from the ghetto. In fact, this may be the same stereotype that many people outside the prison system often have too. This case seeks to provide evidence for or against common stereotypes regarding property crimes. Are criminals convicted of property crimes uneducated dropouts? Are they on welfare? Do they support families via welfare? Are crime rates higher in urban or rural areas? Does unemployment lead to an increase in property crimes? What about the effect of per capita income on crime rates? Is a state with higher average precipitation more likely to have high property crime rates? Why would precipitation have an effect at all? In Pennsylvania, property crimes are those crimes that involve the taking of money or property without the use of force or threats. These crimes are considered non-violent and include burglary, larceny, theft, motor vehicle theft, arson, shoplifting and vandalism. Property crimes occur at residences, retail stores and other commercial properties. Robbery is not considered a property crime. Robbery involves force or threats and is therefore considered a violent crime against a person. Pennsylvania’s definition of a property crime differs slightly from the data set provided in the case; it is more inclusive. I will begin by defining each variable within the data set. Then I will describe the independent variables based on their relationship to the dependent variable of CRIMES using Minitab’s Fitted Line

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