A4Q2

  library(readxl)
  library(ggpubr)

## Loading required package: ggplot2

  A4Q2 <- read_excel("C:/Users/sunde/OneDrive/Desktop/A4Q2.xlsx")
  
  ggscatter(
    A4Q2,
    x = "phone",
    y = "sleep",
    add = "reg.line",
    xlab = "phone",
    ylab = "sleep"
  )

  # The relationship is linear.
  
  # The relationship is negitive.
  
  # The relationship is moderate or strong.
  
  # There are 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 = "sleep",
       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: sleep"
  # "The first variable looks abnormally distributed".
  # "The data is negatively skewed".
  # "The data  does not has a proper bell curve".
  
  # "Variable 2: phone"
  # "The second variable looks abnormally distributed".
  # "The data is positively skewed".
  # "The data does not has a proper bell curve".
  
  shapiro.test(A4Q2$sleep)

## 
##  Shapiro-Wilk normality test
## 
## data:  A4Q2$sleep
## W = 0.91407, p-value = 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 abnormally distributed (p < .001) ".
  
  # "Variable 2: phone"
  # "The second variable is abnormally distributed (p < .001)".
  
  #"since Both histograms are abnormal & both Shapiro-Wilk tests are abnormal,Use Spearman Correlation".
  
  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 a spearman's phone (M =3.804609) and sleep (M = 7.559076)".
  # "There was a statistically significant relationship between the two variables, p < .001".
  # "The relationship was negative and strong".
  # "As usage of phone increased, sleep decreased".