title: “ABHI TEJA”

output: html_document

date: “2026-04-13”

{r} # Load libraries library(“readxl”) library(“ggpubr”) #Read the excel file data1 <-read_excel(“A4Q1.xlsx”)

#scatter plot ggscatter( data1, x = “age”, y = “education”, add = “reg.line”, xlab = “age”, ylab = “education” ) # The relationship is linear.

The relationship is positive.

The relationship is moderate.

There are no outliers

mean(data1\(Age) sd(data1\)Age) median(data1$Age)

mean(data1\(education) sd(data1\)education) median(data1$education)

hist(data1$age, main = “age”, breaks = 20, col = “lightblue”, border = “white”, cex.main = 1, cex.axis = 1, cex.lab = 1)

hist(data1$education, main = “education”, breaks = 20, col = “lightcoral”, border = “white”, cex.main = 1, cex.axis = 1, cex.lab = 1)

Age

The age [ normally] distributed.

The data is [symmetrical].

The data has a proper bell curve.

#education #The education looks normal distributed. #The data is symmetrical. #The data has a proper bell curve.

shapiro.test(A4Q1\(age) #age # the first variable is normal (p= 0.5581) shapiro.test(A4Q1\)education) #education # the variablbe is normal (p = 0.4385)

cor.test(A4Q1\(age, A4Q1\)education, method = “pearson”) cor.test(A4Q1\(age, A4Q1\)education, method = “spearman”) # A Pearson correlation was conducted to test the relationship between age (M = 35.32634, SD = 11.45344) and education (M = 13.82705, SD = 2.595901). # There was a statistically significant relationship between the two variables, r(df) = .148, p = 9.113e-12 # The relationship was weak. # As the independent variable increased, the dependent variable increase.

A Spearman correlation was conducted to test the relationship between (Mdn = 35.80, M = 35.33, SD = 11.45) and education (Mdn = 14.03, M = 13.83, SD = 2.60).

There was a statistically significant relationship between the two variables, ρ = 0.5244375, p = 2.2e-16.

The relationship was positive.

As the independent variable increased, the dependent variable increased.

Load libraries

library(readxl) library(ggpubr)

Read the Excel file

A4Q2 <- read_excel(“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\(phone) sd(A4Q2\)phone) median(A4Q2$phone)

mean(A4Q2\(sleep) sd(A4Q2\)sleep) median(A4Q2$sleep)

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\(phone) shapiro.test(A4Q2\)sleep)

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”) cor.test(A4Q2\(phone,A4Q2\)sleep, method = “spearman”)

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.