Property Crime Report

Overview: This paper presents statistics on major factors that affects the property crime rates in the U. S. Abstract: The property crime rates of 45. 7% occurs more in urban areas. About 16. 8% of the crimes were committed by high school dropouts and only 0. 4% of the crimes that occurs were related to the population density. The type of property crimes that happens includes larceny-theft, home burglary, home invasion, grand theft auto, forgery, and arson. These types of crimes may be caused by factors such as high school dropouts, the population density per square mile, and people living in urban areas.

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The paper will focus on the crimes against properties such as larceny-theft, home burglary, and grand theft auto, not a person. Crimes of property happen more often than other crimes. Larceny is a type of theft when someone takes something that does not belong to them. Home burglary is breaking into a private resident with the intent of stealing something. Grand theft auto is an act of stealing a motor vehicle. Are the property crime rates higher in urban areas? Does the level of education have any effect on the percentage of crimes that are happening?

How about the percentage of people living in a population per square mile? All of these factors may play an important role with the number of crimes that are happening today. Method: Louis J. Moritz, an Operations Manager, collected data from a variety of U. S. government sources. He provided a sample data set of 8 randomly selected factors from 50 states. From those random samples, I used the percentage of dropouts, the size of the population density, and the percentage of residents living in urban areas per state to compare to the percentage of property crimes being committed in the U. S.

Multiple Regression Output: •I identified the individual p-value to test the significance of each of the proposed independent variables •I used the multiple regression equation of the least squares point estimates of y? = b? + b? x? + b? x? + b? x? to study more than one independent variables. The intercept of the slope of the line is b?. oDependent variable: ?Y = the percentage of property crimes that occur in the U. S. oIndependent variables: It summarizes the central tendency of the data provided. ? x? = the percentage of Dropouts in the U. S. ? x? = the percentage of the population Density in the U.

S. ? x? = the percentage of residents living in an Urban area in the U. S. •The standard error, also referred to as s, is the idea of the scattered of the actual points around the regression line. •The adjusted multiple coefficient of determination, also referred to as Adjusted R?. •Inferences to test the hypothesis and confidence intervals, the overall F-test, and the prediction of the dependent variable •Investigate the multicollinearity by examining if there are correlations among the independent variables that are so high that they may be used as a separate independent variable. Results:

Descriptive Statistics •The p-value is basically the area under the left or right tail of a normal curve. I have tested the p-value for each independent variable and compared the value to alpha 0. 10. These following p-values (see Exhibit attached) are x? p-value = . 0005, x? p-value = . 0006, and x? p-value = 2. 30E-10. After identifying the p-values from the regression output, I was able to formulate a multiple regression equation of y? = -1052. 5531 + 57. 7544x? -1. 9318x? + 67. 8889x? from the model. This equation will help explain the relationships between the independent variables of Dropout (x? , Density (x? ), Urban (x? ) and the dependent variable of Crimes. •According to the output, the standard error, s, is the point estimate of the standard deviation of the square root of s?. The regression analysis shows that s is equal to 745. 822. This gives me a rough idea of the actual points that are scattered around the regression line. The less standard error, the more precise the true value is. •The adjusted multiple coefficient of determination, also referred to as R? , is equal to 0. 633. The adjusted R? helps avoid verestimating the independent variables. This means that 63. 3% of the variability of Crimes can be best explained by the values of x? , x? , and x?. •According to the scattered plots for each independent variables, when property crimes goes up, the number of dropouts and the number of residents living in urban areas goes up. As for the population density, the slope of the line has a slight increase. Inferences about the Regression Coefficient •As stated above for the p-value, I was able to perform a hypothesis test for the regression coefficients of ?? ?? , ??. The coefficients of ?? , ?? , ?? are the same as x? , x? , and x?. This was examined by looking at the individual p-values to verify if these coefficients will be used in the model for explanation. •The overall F-test of the relationship between the property crimes committed and the percentage of dropouts, density and residents living in an urban area shows strong evidence that the number of property crimes has a statistically significant relationship to dropouts, density and urban area. •The statistical model for Dropout (x? shows that we are 95% confident that for each student that drops out of school, the Crimes will increase between 2,692,620 and 8,858,260 thefts. •For Density (x? ), the model shows that we are 95% confident that for the amount of people in the population per total square miles, the Crimes will go down between 299,260 and 87,100. •As for Urban (x? ), the statistical model shows that we are 95% confident that for each resident living in the urban area, Crimes will go up between 5,096,500 and 8,481,270. I randomly selected the predicted values for each independent variable (see Exhibit below) to see how confident the data’s are. I selected 25 for the predicted value for Dropout (x? ), 600 for Density (x? ), and 60 for Urban (x? ). oThe statistical model shows in numerous periods that we are 95% confident when the number of high school dropouts is 25, the number of people per density is 600, and the number of people living in an urban area is 60, that the mean determinants of property crimes in the United States will be 273,643,160 and 387,470,020. The model also shows in a single period that we are 95% confident when the number of high school dropouts is 25, the number of people per density is 600, and the number of people living in an urban area is 60, that the determinants of property crimes in the United States will be 170,004,470 and 491,108,710. •Multicollinearity is present when the independent variables are linked with each other. I tested for multicollinearity and none of the independent variables correlations were . 9 or higher. I did not find any evidence of multicollinearity. Conclusion:

The analysis revealed that the percentage of people living in urban areas, the percentage of high school dropouts and the percentage of population density of people per square miles does have a significant relationship to the determinants of property crimes that happens throughout the United States. The scattered plots (see Exhibit attached) show a linear or a bit linear relationship between Dropout and Crimes, Density and Crimes, and Urban and Crimes. The plots for Dropout vs. Urban look more linear than Density. The reason for this is because the points are clustered more closely to the regression line than the points for Density.

In my opinion, I think that the number of high school dropouts would commit more crimes in the United States. The results above prove that when the number of high school dropouts increases, the determinants of property crimes increases. Then again, dropping out of high school does not necessarily mean that they will commit a crime. There are many high school drop outs that are good citizens who obey by the laws. A majority of criminals in jail caught for larceny-theft, grand auto theft, home burglary or home invasion are high school dropouts.

People who live in urban areas are also more at risk of having offenders break into their residence or other properties. Offenders target big cities because of the features of an urban life. These features may include nice homes, expensive tastes, and valuable items. References: 1. Smith, Bryant. (2010). Property Crimes. Practical Data Analysis, Volume II, E-book. (pp. 805-807). New York, NY: The Tim McGraw-Hill Companies, Inc. 2. McGraw-Hill, Irwin. (3rd Ed. ). (2010). Essentials of Business Statistics and Applied Managerial Statistics Cases (pp. 2-809). New York, NY: The Tim McGraw-Hill Companies, Inc.

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