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Statistics of Crime

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Statistical Analysis and the common perception of property crime

This project will determine whether or not a relationship exists between various variables as they pertain to, or not, property crime. Property crime affects everyone in the population, directly or indirectly. The ability to understand and predict indicators (independent variables) of property crime can help focus the finite assets of society and the government to reduce, or in a utopic vision eliminate, property crime. To help determine the applicability of the variables, a multiple regression was performed on the property crime dataset (appendix A) to analyze the dependent or independent relations to the crime variable. APPX A shows the results of the regression analysis between each of the 8 variables when associated with crime. By using Minitab 16 for this procedure, tests were run to measure the relative p-values among the independent variables. Each independent variable in the regression models had tolerance values close to 0.1 and variance inflation factors (VIF) well below the target value (below 0.05, Allen tutorials cconline.net/browna/tutorials_mt16.htm). In fact, all were below 0.05 indicating the predictive value of the regression models and the rejection of Ho (null hypothesis)
Ho: none of the variables predict Property Crime
Ha: at least one of the independent variables is helpful to predict property crime
The dependent variable is CRIMES: Property crime rate per 100k inhabitants. The independent variables are: PINCOME: Primary Income per capita income for each state, DROPOUT: High school dropout rate percentage, PRECIP: Average precipitation in inches in the major city of each state, PUBAID: Percentage of public aid recipients, Density: population/ total square miles, Kids: Public aid for families with children, in dollars per family, UNEMPLOY: Percentage of unemployed

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