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

# The relationship between the two variables is linear. # The relationship between the two variables is negative. # The relationship between the two variables is strong. # There are no 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$phone)
## [1] 2.661866
median(A4Q2$sleep)
## [1] 7.524099
hist(A4Q2$sleep,
     main = "sleep",
     breaks = 20,
     col = "lightblue",
     border = "white",
     cex.main = 1,
     cex.axis = 1,
     cex.lab = 1)

# Variable 1: Sleep # The first variable looks approximately normally distributed. # The data is fairly symmetrical around the centre. # The data shows bell-shaped curve and there are a few values at lower-end.

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

# Variable 2: Phone # The second variable does not appear to be normally distributed. # The data is not symmetrical and is positively skewed. # The histogram does not show a clear bell-shaped curve.

shapiro.test(A4Q2$sleep)
## 
##  Shapiro-Wilk normality test
## 
## data:  A4Q2$sleep
## W = 0.91407, p-value = 8.964e-08

Variable 1: sleep

The first variable is not normally distributed (p = 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 = 9.641e-09).

cor.test(A4Q2$sleep, A4Q2$phone, method = "pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  A4Q2$sleep and A4Q2$phone
## t = -11.813, df = 148, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7708489 -0.6038001
## sample estimates:
##        cor 
## -0.6966497

A Pearson correlation was conducted to test the relationship between sleep

(M = 7.559076, SD = 1.208797) and phone use.

There was a statistically significant relationship between the two variables,

r(148) = -.70, p < .001.

The relationship was negative and strong.

As sleep increased, phone use decreased.

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 sleep

(Mdn = 7.524099) and phone use (Mdn = 7.524099).

There was a statistically significant relationship between the two variables,

ρ = -.61, p < .001.

The relationship was negative and moderate.

As sleep increased, phone use decreased.