PAD 6833 Omar Nesbit Homework 5

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(readxl)

Hot<-read_excel("HOTax.xlsx")

Hot_cleaned<-Hot%>%filter(!is.na(`Tax Code Ch. 351 Revenue`)&!is.na(`Signage Revenue`))

cor(Hot_cleaned$`Tax Code Ch. 351 Revenue`,Hot_cleaned$`Signage Revenue`)

## [1] -0.001616619

hot_pairs<-Hot_cleaned%>%select(`Tax Code Ch. 351 Revenue`,`Signage Revenue`,`Advertising Revenue`)

pairs(hot_pairs)

Hot_cleaned_2<-Hot%>%filter(!is.na(`Information Centers Revenue`))

cor.test(Hot_cleaned_2$`Fiscal Year`,Hot_cleaned_2$`Information Centers Revenue`,method = "kendall")

## 
##  Kendall's rank correlation tau
## 
## data:  Hot_cleaned_2$`Fiscal Year` and Hot_cleaned_2$`Information Centers Revenue`
## z = -0.85194, p-value = 0.3942
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.01748753

# The findings indicate that there is a very weak or no correlation between the variables. COVID severely affected revenues over the last few years, so I doubted a linear relationship or a normal distribution of data (nonparametric) between the variables. Consequently, I ruled out utilizing the Pearson method. Additionally, I assumed that a monotonic relationship did not exist, thus, I selected the Kendall method.