library(readxl)
library(ggpubr)
## Loading required package: ggplot2
A5Q2 <- read_excel("C:/Users/nehab/OneDrive/A5221/Assignment 5/A5Q2.xlsx")
ggscatter(
A5Q2,
x = "phone",
y = "sleep",
add = "reg.line",
xlab = "Daily Phone Use",
ylab = "Nightly Sleep Duration"
)

# The relationship appears negative.
# As phone use increases, sleep duration tends to decrease.
# The pattern is generally linear, but there are some possible outliers.
mean(A5Q2$sleep)
## [1] 7.559076
sd(A5Q2$sleep)
## [1] 1.208797
median(A5Q2$sleep)
## [1] 7.524099
mean(A5Q2$phone)
## [1] 3.804609
sd(A5Q2$phone)
## [1] 2.661866
median(A5Q2$phone)
## [1] 3.270839
hist(
A5Q2$sleep,
main = "Distribution of Sleep Duration",
xlab = "Nightly Sleep Duration"
)

hist(
A5Q2$phone,
main = "Distribution of Phone Use",
xlab = "Daily Phone Use"
)

# Sleep has possible outliers and may not be normally distributed.
# Phone use appears right-skewed and is not normally distributed.
shapiro.test(A5Q2$sleep)
##
## Shapiro-Wilk normality test
##
## data: A5Q2$sleep
## W = 0.91407, p-value = 8.964e-08
shapiro.test(A5Q2$phone)
##
## Shapiro-Wilk normality test
##
## data: A5Q2$phone
## W = 0.89755, p-value = 9.641e-09
# Sleep is not normally distributed because p < .001.
# Phone use is not normally distributed because p < .001.
# Both p-values are less than .05.
# Therefore, a Spearman correlation will be used.
cor.test(
A5Q2$phone,
A5Q2$sleep,
method = "spearman",
exact = FALSE
)
##
## 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 examine the relationship
# between daily phone use and nightly sleep duration.
# There was a statistically significant strong negative relationship,
# rho = -.61, p < .001.
# As daily phone use increased, nightly sleep duration tended to decrease.
# Therefore, the null hypothesis was rejected.