library(readxl)
library(ggpubr)

## Loading required package: ggplot2

A5Q2 <- read_excel("C:/Users/SHRUTI/Downloads/A5Q2.xlsx")
A5Q2

## # A tibble: 150 × 2
##    sleep  phone
##    <dbl>  <dbl>
##  1  9.03  1.78 
##  2  6.76  6.62 
##  3  9.18  0.289
##  4  7.20  3.33 
##  5  3    10    
##  6  6.71  4.24 
##  7  7.99  0.701
##  8 10.1   0.261
##  9  6.91  6.75 
## 10  7.50  3.95 
## # ℹ 140 more rows

nrow(A5Q2)

## [1] 150

names(A5Q2)

## [1] "sleep" "phone"

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

# 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",
     breaks = 20,
     col = "lightblue",
     border = "white",
     cex.main = 1,
     cex.axis = 1,
     cex.lab = 1)

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

# Variable 1: Phone
# The variable looks abnormally distributed.
# The data is positively skewed.
# The data does not have a proper bell curve.

# Variable 2: Sleep
# The variable looks abnormally distributed.
# The data is negatively skewed.
# The data does not have a proper bell curve.

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
# The variable is abnormally distributed (p < .001).

# Variable 2: Sleep
# The variable is abnormally 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 (Mdn = 3.27) and sleep (Mdn = 7.52).
# There was a statistically significant relationship between the two variables, ρ = -.61, p < .001.
# The relationship was negative and strong.
# As phone use increased, sleep decreased.