A4Q2

#Open the Installed Packages:
library("readxl")
library("ggpubr")

## Loading required package: ggplot2

#Import and Name Dataset:
A4Q2 <- read_excel("C:/Users/krish/Downloads/A4Q2.xlsx")

#Create a Scatterplot to Visualize the Relationship:
ggscatter(
  A4Q2,
  x = "phone",
  y = "sleep",
  add = "reg.line",
  xlab = "phone",
  ylab = "sleep"
)

#Interpret the Scatterplot:
# "The relationship is linear".
# "The relationship is negative".
# "The relationship is moderate to strong".
# "There are outliers".

#The Descriptive Statistics:
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

#Check Normality Visually (Histograms):
hist(A4Q2$phone,
     main = "phone",
     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)

# Interpret the Histograms:
# "Variable 1: phone"
# "The first variable looks abnormally distributed".
# "The data is negatively skewed".
# "The data  does not has a proper bell curve".
# "Variable 2: sleep"
# "The second variable looks abnormally distributed".
# "The data is positively skewed".
# "The data does not has a proper bell curve".

# Check Normality Statistically (Shapiro-Wilk Test):
shapiro.test(A4Q2$phone)

## 
##  Shapiro-Wilk normality test
## 
## data:  A4Q2$phone
## W = 0.89755, p-value = 9.641e-09

shapiro.test(A4Q2$sleep)

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

#Interpret the Shapiro-Wilk Test:
# "Variable 1: phone"
# "The first variable is abnormally distributed (p < .001) ".
# "Variable 2: sleep"
# "The second variable is abnormally distributed (p < .001)".

#"since Both histograms are abnormal & both Shapiro-Wilk tests are abnormal,Use Spearman Correlation".

#Conduct spearman Correlation:
cor.test(A4Q2$phone, A4Q2$sleep, method = "spearman")

## 
##  Spearman's rank correlation rho
## 
## data:  A4Q2$phone and A4Q2$sleep
## S = 908390, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.6149873

#Report the spearman Correlation:
# "A spearman correlation was conducted to test the relationship between a spearman's phone  (M =3.804609, SD =2.661866) and sleep (M = 7.559076, SD = 1.208797)".
# "There was a statistically significant relationship between the two variables, ρ = -.61, p < .001".
# "The relationship was negative and strong".
# "As usage of phone increased, sleep decreased".