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title: “r markdown data 1” output: html_document date: “2026-04-11” —(r) library(“readxl”) library(“ggpubr”) data1 <-read_excel(“A4Q1.xlsx”) 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 or strong.

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)

Variable 1: age

The first variable looks normally distributed.

Variable 2: education

The second variable looks normally distributed.

shapiro.test(data1\(age) shapiro.test(data1\)education)

Variable 1: age

The first variable is normally distributed (p =0.5581).

Variable 2:education

The second variable is normally distributed (p = 0.4385).

cor.test(data1\(age, data1\)education, method = “pearson”)

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

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