University of Iowa



Crime Rates in the Most Populated Cities in the State of IowaSam Halsor?Jim Nichols?STAT:2010?1 May 2017Crime Rates in the Most Populated Cities in the State of IowaIntroductionThe dataset selected contains the property crime and violent crime rates of the 20 most populated cities, in the state of Iowa. The Federal Bureau of Investigation collected this data, in 2013. The general data used can be seen below in Table 1. Table 1: General Information Used in Study LINK Excel.Sheet.12 "\\\\engin.uiowa.edu\\stuff\\home\\shalsor\\STAT;2010\\Iowa Crime Data.xlsx" "Sorted by City!R1C1:R21C4" \a \f 4 \h \* MERGEFORMAT Federal Bureau of Investigation defined a violent crime as the following. A?violent crime is composed of four offenses:?murder and non-negligent manslaughter, rape, robbery, and aggravated assault. Violent crimes are defined as those offenses,?which involve force or threat of force.?The Federal Bureau of Investigation defined a property crime as the following. A?property crime includes the offenses of burglary, larceny-theft, motor vehicle theft, and arson. The object of the theft-type offenses is the taking of money or property, but there is no force or threat of force against the victims.Research Questions??In the state of Iowa, is there a correlation between population and violent crimes??In the state of Iowa, is there a correlation between population and property crimes??In the state of Iowa, is there a correlation between property crime rates and violent crime rates??In the state of Iowa, is there a correlation between?violent crime rates?and city’s area??In the state of Iowa, is there a correlation between property crime rates and city’s area??Results and Discussion??The?first?portion of the data of interest was investigating the relationship that exists?between the population of a given city and the resulting violent crime rate.?It was discovered that there is a strong positive correlation between an increase in population and property crime. The computed R-value is 0.7329. The violent crimes mean is 229 in the most populated cities in the state of Iowa. The minimum and maximum amount of observed violent crimes are 30 and 1,026 respectively.?The violent crime’s 95% Confidence Intervals for these cities can be seen in Appendix A. The average regression of the violent crimes was found to be 0.89183. The data persisting to the violent crimes can be seen in Appendix A.?The?second?aspect of the data analyzed the relationship that exists?between the population of a given city and the ensuing property crime rate. It was found that there is a strong positive correlation between an increase in population and property crime. The computed R-value is 0.8807. The property crimes mean is 2,125 in the most populated cities in the state of Iowa. The minimum and maximum amount of observed property crimes are 495 and 10,015 respectively. The property crime’s 95% Confidence Intervals for these cities can be seen in Appendix A. The average regression of the property crimes was found to be -0.4617. The data pertaining to the property crimes is located in Appendix A.?The?third?facet of our dataset was to explore the relationship between property crime rates and violent crime rates within particular cities. The results indicate there is a moderate positive correlation between an increase in property crime rates and violent crime rates. The computed R-value is 0.4648. The violent crime rates mean is 0.00368. The property crime rates mean is 0.03230. The minimum and maximum amount of observed violent crime rates are 0.000857 and 0.00942 respectively. The minimum and maximum amount of observed property crime rates are 0.01231 and 0.07439 respectively. The average regression of the violent crime rate to the property crime rate was calculated to be 1.7149E-5. The data persisting to the ratios can be seen in Appendix A.?The?fourth?component of our dataset was the evaluation?of?the relationship between?violent?crime rate and?the total area?within these cities. The results show there is almost no?correlation between an increase in?city area and?violent?crime rates. The computed R-value is 0.1492. The average number of violent crimes committed per square miles is 5.929. The minimum and maximum amount of violent crimes committed per square mile are?1.08 and 14.25 respectively. The minimum and maximum number of?square miles in these cities?are 15.86 and 88.92?respectively.?The violent crimes committed per square mile has a 95% Confidence Intervals that can be found in Appendix A. The average regression of the violent crimes committed per square mile was found to be 0.00036714. The data involving the violent crimes committed per square mile can be seen in Appendix A.?The?fifth?characteristic?of our dataset was to explore the relationship between property crime rate and?the total area?within these cities. The results indicate there is a weak positive?correlation between an increase in?city area and?violent?crime rates. The computed R-value is 0.3984. The average number of violent crimes committed per square miles is 53.341. The minimum and maximum amount of violent crimes committed per square mile are?17.22 and 112.62 respectively. The minimum and maximum number of?square miles in these cities?are 15.86 and 88.92?respectively.?The property crimes committed per square mile has a 95% Confidence Intervals that is in Appendix A. The average regression of the property crimes committed per square mile was found to be -0.8882. The data persisting to the property crimes committed per square mile is located in Appendix A.ConclusionThe experiment was conducted using SAS software. The SAS code and outputs can be seen in Appendix B. Once the outputs were achieved, the data was organized using Excel. The researchers assumed that 95% of violent and property crimes were reported. This produced a 95% Confidence Interval. After evaluating 20 cities and having an estimated Confidence Interval of 95%, the T-value was calculated to be 0.0635. This T-value was used to validate the accuracy of the regression coefficients. With the R-values being relatively low, calculating exact predictions can be difficult. As a result, the regression values were found to be small compared to their uncertainty. The first three questions that were analyzed produced a moderately strong relationship. It was discovered that there is a moderate positive correlation between an increase in population and violent crime. There is a strong positive correlation between an increase in population and property crime. There is a moderate positive correlation between an increase in property crime rates and violent crime rates. The final two research questions yielded a small relationship. There is almost no?correlation between an increase in?city area and?violent?crime rates. The results also indicate there is a weak positive?correlation between an increase in?city area and?property crime rates. In conclusion, the experiment conducted was a success. Members’ TasksBoth of us, Jim and Sam, worked on this project together. We worked on every task together. This includes the SAS coding, calculations, the report and the excel formatting. Appendix A Table 2: Data for Violent and Property Crime Rates Table 3: Data for Violent and Property Crimes per Square MileTable 4: Regression Values of Violent Crime, Property Crime, and Violent Crime RatesTable 5: Regression Values of Violent and Property Crimes per Square MileTable 6: Confidence IntervalsTable 7: Regression UncertaintyAppendix BQuestion 1 and 2 CodeQuestion 1 OutputQuestion 2 OutputQuestion 3 CodeQuestion 3 OutputQuestion 4 and 5 CodeQuestion 4 OutputQuestion 5 Output ................
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