library(readxl)
library(ggpubr)

## Loading required package: ggplot2

A5Q2 <- read_excel("//apporto.com/dfs/SLU/Users/hannahsmith3_slu/Downloads/A5Q2.xlsx")

ggscatter(
  A5Q2,
  x = "phone",
  y = "sleep",
  add = "reg.line",
  xlab = "phone",
  ylab = "sleep"
)

#The relationship is linear. 
#The relationship is negative. 
#There are not outliers.

mean(A5Q2$phone)

## [1] 3.804609

sd(A5Q2$phone)

## [1] 2.661866

median(A5Q2$phone)

## [1] 3.270839

mean(A5Q2$sleep)

## [1] 7.559076

sd(A5Q2$sleep)

## [1] 1.208797

median(A5Q2$sleep)

## [1] 7.524099

hist(A5Q2$phone,
     main="phone",
     breaks=20,
     col="lightblue",
     border="white")

#Variable 1: Phone Usage
#The variable looks abnormally distributed. 
#The data is negatively skewed. 
#The data does not have a proper bell curve.

hist(A5Q2$sleep,
     main="sleep",
     breaks=20,
     col="lightblue",
     border="white")

#Variable 2: Sleep
#The variable looks abnormally distributed. 
#The data is negatively skewed. 
#The data does not have a proper bell curve.

shapiro.test(A5Q2$phone)

## 
##  Shapiro-Wilk normality test
## 
## data:  A5Q2$phone
## W = 0.89755, p-value = 9.641e-09

#Variable 1: Phone Usage
#The variable is abnormally distributed (p= <.001).

shapiro.test(A5Q2$sleep)

## 
##  Shapiro-Wilk normality test
## 
## data:  A5Q2$sleep
## W = 0.91407, p-value = 8.964e-08

#Variable 2: Sleep
#The variable is abnormally distributed (p= <.001).

cor.test(
  A5Q2$phone, 
  A5Q2$sleep,
  method = "spearman"
)

## 
##  Spearman's rank correlation rho
## 
## data:  A5Q2$phone and A5Q2$sleep
## 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 phone usage (Mdn = 3.27) and sleep (Mdn = 7.52)
#There was a statistically significant relationship between the two variables, p = <.001, p = <.001
#The relationship was negative and strong. 
#As phone usage increased, sleep decreased.