library(readxl)
library(ggpubr)
## Loading required package: ggplot2
A5Q2 <- read_excel("//apporto.com/dfs/SLU/Users/sararaewomack_slu/Desktop/Week 5/Assignment 5/A5Q2.xlsx")
ggscatter(
A5Q2,
x = "sleep",
y = "phone",
add = "reg.line",
xlab = "sleep",
ylab = "phone"
)

# The relationship is linear.
# The relationship is negative.
# There no 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 = "sleep",
breaks = 20,
col = "lightblue",
border = "white",
cex.main = 1,
cex.axis = 1,
cex.lab = 1)

hist(A5Q2$phone,
main = "phone",
breaks = 20,
col = "lightcoral",
border = "white",
cex.main = 1,
cex.axis = 1,
cex.lab = 1)

# Variable 1: sleep
# The variable looks abnormally distributed.
# The data is negatively skewed.
# The data does not have a proper bell curve.
# Variable 2: phone
# The variable looks abnormally distributed.
# The data is positively skewed.
# The data does not have a proper bell curve.
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
# Variable 1: sleep
# The variable is abnormally distributed (p < .05).
# Variable 2: phone
# The variable is abnormally distributed (p < .05).
cor.test(
A5Q2$sleep,
A5Q2$phone,
method = "spearman"
)
##
## Spearman's rank correlation rho
##
## data: A5Q2$sleep and A5Q2$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 (Mdn = 7.52) and phone (Mdn = 3.27).
# There was a statistically significant relationship between the two variables, ρ = .61, p < .001
# The relationship was negative and strong.
# As sleep increased, phone decreased.