library(readxl)
library(ggpubr)
## Loading required package: ggplot2
A4Q2 <- read_excel("A4Q2.xlsx")
ggscatter(
A4Q2,
x = "phone",
y = "sleep",
add = "reg.line",
xlab = "phone",
ylab = "sleep"
)

# The relationship is linear.
# The relationship is negative.
# The relationship is weak.
# There are no outliers
mean(A4Q2$phone)
## [1] 3.804609
sd(A4Q2$phone)
## [1] 2.661866
median(A4Q2$phone)
## [1] 3.270839
mean(A4Q2$sleep)
## [1] 7.559076
sd(A4Q2$sleep)
## [1] 1.208797
median(A4Q2$sleep)
## [1] 7.524099
hist(A4Q2$phone,
main = "phone",
breaks = 20,
col = "lightblue",
border = "white",
cex.main = 1,
cex.axis = 1,
cex.lab = 1)

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

# Variable 1:phone
# The first variable looks abnormally distributed.
# The data is positive skew.
# The data have no proper bell curve.
# Variable 2:sleep
# The second variable looks abnormally distributed.
# The data is negative skew .
# The data have no proper bell curve.
shapiro.test(A4Q2$phone)
##
## Shapiro-Wilk normality test
##
## data: A4Q2$phone
## W = 0.89755, p-value = 9.641e-09
shapiro.test(A4Q2$sleep)
##
## Shapiro-Wilk normality test
##
## data: A4Q2$sleep
## W = 0.91407, p-value = 8.964e-08
# Variable 1:phone
# The first variable is abnormally distributed (p < .001)
# Variable 2:sleep
# The second variable is abnormally distributed (p < .001).
cor.test(A4Q2$phone, A4Q2$sleep, method = "spearman")
##
## Spearman's rank correlation rho
##
## data: A4Q2$phone and A4Q2$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 hours (Mdn = 3.27) and hours of sleep(Mdn = 7.55 ).
# There was a statistically significant relationship between phone hours and hours of sleep , ρ<-.62 , p < .001
# The relationship was negative and strong.
# As phone hours increased, sleeping hours decreased.