Homework 4

library(tidyverse)

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library(readxl)
library(pastecs)

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library(car)

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Cities<-read_xlsx("FiSC-Full-Dataset-2023-Update.xlsx")

stat.desc(Cities$education_city)

##      nbr.val     nbr.null       nbr.na          min          max        range 
## 1.024600e+04 3.180000e+03 0.000000e+00 0.000000e+00 6.196860e+03 6.196860e+03 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 4.392838e+06 2.968000e+01 4.287369e+02 9.666649e+00 1.894852e+01 9.574283e+05 
##      std.dev     coef.var 
## 9.784827e+02 2.282245e+00

The variable I have chosen for this excercise is the spending for all education of cities within the data set. This is to include K-12 education, higher education, and libraries. The values represented are per capita dollars, so the value given is spending per resident of each city. The range of the values is fairly large, with the maximum spending recorded 6196.86 per capita, and the lowest being 0. It would seem that most values tend towartds the lower end, as the median is 29.68. Even so, it would seem that the vlaues are right skewed, as the mean is pulled higher to 428.73.

The following histogram shows the frequency of amounts of spending, with the zeros included

hist(Cities$education_city)

The histogram shows that an overwhelming majority of cities in this dataset do not supply any additional spending towards education.Miuch of this can be explained by school districts providing the majority of funding, which is also included in the data set. So, we can remove the zeros in the data to show only the municipalities that send additional funds on education.

Cities_clean<-Cities %>% drop_na(education_city) %>% filter(education_city>0)

hist(Cities_clean$education_city)

Even with all the zeros taken out from the data, the histogram has not changed significantly, showing the majority of values are still on the very low end.

Cities_clean<-Cities_clean %>% mutate(education_city_SQRT=sqrt(education_city))

hist(Cities_clean$education_city_SQRT)

Squaring the values does some to help differentiate the degrees of municiple education spending, but not by a large degree. It is still largely right scewed, but there is some difference in the lower end values. It seems that while a great many cities choose to spend very little on education, a larger porportion choose to spend slightly more, though is still dwarfed by a few number of cities.