QuestionQuestion

Use the following dataset 'Crime in Major Cites', to compare crime rates in different cities for the years listed in the dataset. Do correlation analyze among various variables and identify variables that contribute the most to the overall rising crime rates for each period. Complete this exercise using both R (RStudio Notebook) as well as Python (Jupyter Notebook).

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Correlation Analysis for 2010 for:
All Violent: Murder, Rape, Robbery, Assault
cor.test(crime_2010$All.Violent, crime_2010$Murder)
##
## Pearson's product-moment correlation
##
## data: crime_2010$All.Violent and crime_2010$Murder
## t = 12.474, df = 354, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4759594 0.6208480
## sample estimates:
##       cor
## 0.5525645
From the above output we can see that the correlation of Murder with Total Crime Rate is 0.55 which means Murder is Moderately correlated with the Total Crime. The pvalue of the Pearson’s test is also less than 0.05 which means there is significant correlation.
cor.test(crime_2010$All.Violent, crime_2010$Rape)
##
## Pearson's product-moment correlation
##
## data: crime_2010$All.Violent and crime_2010$Rape
## t = 7.2482, df = 354, p-value = 2.67e-12
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2654643 0.4467357
## sample estimates:
##       cor
## 0.3594867...

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