A5Q2

library(readxl)
library(ggpubr)

## Loading required package: ggplot2

A5Q2 <- read_excel("A5Q2.xlsx")

ggscatter(
  A5Q2,
  x = "phone",
  y = "sleep",
  add = "reg.line",
  xlab = "Phone Use",
  ylab = "Sleep Duration"
)

# The relationship is linear.
# The relationship is negative.
# There are outliers.

mean(A5Q2$phone)

## [1] 3.804609

sd(A5Q2$phone)

## [1] 2.661866

median(A5Q2$phone)

## [1] 3.270839

mean(A5Q2$sleep)

## [1] 7.559076

sd(A5Q2$sleep)

## [1] 1.208797

median(A5Q2$sleep)

## [1] 7.524099

hist(A5Q2$phone,
     main = "Phone Use",
     breaks = 20,
     col = "lightblue",
     border = "white")

hist(A5Q2$sleep,
     main = "Sleep Duration",
     breaks = 20,
     col = "lightcoral",
     border = "white")

# Variable 1: Phone Use
# The variable does not look normally distributed.
# The data is right-skewed.
# There are possible outliers.

# Variable 2: Sleep Duration
# The variable does not look normally distributed.
# There are possible outliers at the lower end.

shapiro.test(A5Q2$phone)

## 
##  Shapiro-Wilk normality test
## 
## data:  A5Q2$phone
## W = 0.89755, p-value = 9.641e-09

shapiro.test(A5Q2$sleep)

## 
##  Shapiro-Wilk normality test
## 
## data:  A5Q2$sleep
## W = 0.91407, p-value = 8.964e-08

# Variable 1: Phone Use
# The variable is not normally distributed (p < .001).

# Variable 2: Sleep Duration
# The variable is not normally distributed (p < .001).

cor.test(A5Q2$phone, A5Q2$sleep, method = "spearman")

## 
##  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 test the relationship between phone use
# and sleep duration.

# There was a statistically significant relationship between the two variables,
# rs = -.61, p < .001.

# The relationship was negative and strong.

# As phone use increased, sleep duration decreased.