library(readxl)
library(ggpubr)

## Loading required package: ggplot2

data2026 <- read_excel("C:/Users/siqoe/OneDrive - Saint Louis University/AA 5221/Assignment 5/A5Q2(Sheet1).xlsx")

ggscatter(
  data2026,
  x = "sleep",
  y = "phone",
  add = "reg.line",
  xlab = "sleep",
  ylab = "phone"
)

# The relationship is linear.
# The relationship is ne.
# There are  outliers.

mean(data2026$sleep)

## [1] 7.559076

sd(data2026$phone)

## [1] 2.661866

median(data2026$sleep)

## [1] 7.524099

mean(data2026$phone)

## [1] 3.804609

sd(data2026$phone)

## [1] 2.661866

median(data2026$phone)

## [1] 3.270839

hist(data2026$sleep)

hist(data2026$phone)

# Variable 1: sleep
# The variable  does not look normally distributed.
# The data is assymmetrical.
# The data has no proper bell curve.

# Variable 2: phone
# The variable  does not look normally distributed.
# The data is assymmetrical.
# The data has no  proper bell curve.



shapiro.test(data2026$sleep)

## 
##  Shapiro-Wilk normality test
## 
## data:  data2026$sleep
## W = 0.91407, p-value = 8.964e-08

shapiro.test(data2026$phone)

## 
##  Shapiro-Wilk normality test
## 
## data:  data2026$phone
## W = 0.89755, p-value = 9.641e-09

# Variable 1: sleep
# The variable is not normally distributed (p = 8.964e-08).

# Variable 2: phone
# The variable is not normally distributed (p = 9.641e-09).

cor.test(data2026$sleep, data2026$phone, method = "spearman")

## 
##  Spearman's rank correlation rho
## 
## data:  data2026$sleep and data2026$phone
## S = 908390, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.6149873

# A Spearman correlation was conducted to test the relationship between sleep and phone.
# There was a statistically significant relationship between the variables,
# The relationship was negative and moderate.
# As sleep increased, phone decreased.