AQ42

# Load libraries
library(readxl)
library(ggpubr)

## Loading required package: ggplot2

# Read the Excel file
A4Q2 <- read_excel("C:/Users/NITHIN KUMAR/Downloads/A4Q2.xlsx")

# Scatter plot
ggscatter(
  A4Q2,
  x = "phone",
  y = "sleep",
  add = "reg.line",
  xlab = "Phone Usage (IV)",
  ylab = "Sleep (DV)"
) 

# The relationship is linear.

# The relationship is negative.

# The relationship is moderate.

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

#Variable 1: phone

# The first variable look normal distributed.
# The data is positively symmetrical.
# The data doesnt have a bell curved.


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

#Variable 2: sleep 

# The first variable looks [normal] distributed.
# The data is positively symmetrical.
# The data doesnot  have a curve.

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

# Variable 1: Phone
# The first variable is abnormally distributed (p = 9.641e-09).

# Variable 2: sleep 
# The second variable is abnormallydistributed (p = 8.964e-08). 

cor.test(A4Q2$phone,A4Q2$sleep, method = "pearson") 

## 
##  Pearson's product-moment correlation
## 
## data:  A4Q2$phone and A4Q2$sleep
## 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

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

# A Pearson correlation was conducted to test the relationship between phone (M = 3.804609, SD = 2.661866) and sleep (M = 7.559076, SD = 1.208797).
# There was a statistically significant relationship between the sleep, r(df) = 148, p = 2.2e-16. 
# The relationship was negative and strong.
# As the independent variable increased, the dependent variable decreased.

# A Spearman correlation was conducted to test the relationship between Variable 1 (Mdn = 3.270) and Variable 2 (Mdn = xx.xx).
# There was a statistically significant relationship between the two variables, ρ = -0.614, p = 2.2e-16.
# The relationship wasnegative and strong.
# As the independent variable increased, the dependent variable decreased.