library(readxl)
library(ggpubr)
## Loading required package: ggplot2
A4Q2 <- read_excel("~/Downloads/A4Q2 (1).xlsx")
names(A4Q2)
## [1] "sleep" "phone"
ggscatter(
  A4Q2,
  x = "sleep",
  y = "phone",
  add = "reg.line",
  xlab = "Hours of Sleep",
  ylab = "Hours of Phone Use"
)

#The relationship is linear.
#The relationship is negative.
#The relationship is strong.
#There are no meaningful outliers.

mean(A4Q2$sleep)
## [1] 7.559076
sd(A4Q2$sleep)
## [1] 1.208797
median(A4Q2$sleep)
## [1] 7.524099
mean(A4Q2$phone)
## [1] 3.804609
sd(A4Q2$phone)
## [1] 2.661866
hist(A4Q2$sleep,
     main = "sleep",
     breaks = 20,
     col = "lightblue",
     border = "white",
     cex.main = 1,
     cex.axis = 1,
     cex.lab = 1)

hist(A4Q2$phone,
     main = "phone",
     breaks = 20,
     col = "lightcoral",
     border = "white",
     cex.main = 1,
     cex.axis = 1,
     cex.lab = 1)

#Variable 1: Sleep
#The first variable looks normally distributed.
#The data is symmetrical.
#The data has a proper bell curve.

#Variable 2: Phone
#The second variable looks abnormally distributed.
#The data is positively skewed.
#The data does not have a proper bell curve.
shapiro.test(A4Q2$sleep)
## 
##  Shapiro-Wilk normality test
## 
## data:  A4Q2$sleep
## W = 0.91407, p-value = 8.964e-08
shapiro.test(A4Q2$phone)
## 
##  Shapiro-Wilk normality test
## 
## data:  A4Q2$phone
## W = 0.89755, p-value = 9.641e-09
#Variable 1: Sleep
#The first variable is not normally distributed (p = 8.964e-08).
#The second variable is not normally distributed (p = 9.641e-09).
#Overall data is not normal. Use Spearman Correlation.

cor.test(A4Q2$sleep, A4Q2$phone, method = "spearman")
## 
##  Spearman's rank correlation rho
## 
## data:  A4Q2$sleep and A4Q2$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 hours of sleep (M = 7.56, SD= 1.21) and hours of phone use (M = 3.80, SD = 2.66).

There was a statistically significant relationship between the two variables, rs(148) = -.61, p=2.2e-16.

The relationship was negative and strong.

As hours of sleep increased, hours of phone used decreased.