library(readxl)
library(ggpubr)
## Loading required package: ggplot2
A4Q2 <- read_excel("~/Downloads/A4Q2.xlsx")
 ggscatter(
 A4Q2,
 x = "sleep",
 y = "phone",
 add = "reg.line",
 xlab = "sleep",
 ylab = "phone"
 )

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

mean(A4Q2\(sleep) [1] 7.559076 sd(A4Q2\)sleep) [1] 1.208797 median(A4Q2$sleep) [1] 7.524099

mean(A4Q2\(phone) [1] 3.804609 sd(A4Q2\)phone) [1] 2.661866 median(A4Q2) Error in median.default(A4Q2) : need numeric data

median(A4Q2$phone) [1] 3.270839

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

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

Variable 1: Sleep The first variable looks abnormally distributed The data is negatively skewed The data does not have a proper bell curve

Varible 2: Phone The second variable looks abnormally distributed The data is positively skewed The data does not have a proper bell curve

shapiro.test(A4Q2$sleep)

Shapiro-Wilk normality test

data: A4Q2$sleep W = 0.91407, p-value = 8.964e-08

shapiro.test(A4Q2$phone)

Shapiro-Wilk normality test

data: A4Q2$phone W = 0.89755, p-value = 9.641e-09

Variable 1: Sleep The first variable is abnormally distributed (p = 8.964e-08) Variable 2: Phone The second variable is abnormally distributed (p = 9.641e-09)

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

Spearman's rank correlation rho

data: A4Q2\(sleep and A4Q2\)phone S = 908390, p-value < 2.2e-16 alternative hypothesis: true rho is not equal to 0 sample estimates: rho -0.6149873

A Spearman correlation was conducted to test the relationship between Variable 1 (Mdn = 7.52) and Variable 2 (Mdn = 3.27) There was a statistically significant relationship between the two variables, p = -0.614, p < .001. The relationship was negative and strong. As sleep increases, phone decreased.