---

title: “ABHI TEJA” output: html_document date: “2026-04-13”

Load libraries

install.packages(“readxl”) install.packages(“ggpubr”)

library("readxl")
library("ggpubr")

## Loading required package: ggplot2

data1 <-read_excel("A4Q2.xlsx")

Scatter plot

ggscatter(
  data1,
  x = "phone",
  y = "sleep",
  add = "reg.line",
  xlab = "Phone",
  ylab = "Sleep"
) 

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

mean(data1$phone)

## [1] 3.804609

sd(data1$phone) 

## [1] 2.661866

median(data1$phone) 

## [1] 3.270839

mean(data1$sleep)

## [1] 7.559076

sd(data1$sleep) 

## [1] 1.208797

median(data1$sleep) 

## [1] 7.524099

hist(data1$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(data1$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(data1$phone)

## 
##  Shapiro-Wilk normality test
## 
## data:  data1$phone
## W = 0.89755, p-value = 9.641e-09

shapiro.test(data1$sleep) 

## 
##  Shapiro-Wilk normality test
## 
## data:  data1$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(data1$phone,data1$sleep, method = "pearson") 

## 
##  Pearson's product-moment correlation
## 
## data:  data1$phone and data1$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(data1$phone,data1$sleep, method = "spearman")   

## 
##  Spearman's rank correlation rho
## 
## data:  data1$phone and data1$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.