library(readxl)
library(ggpubr)
## Loading required package: ggplot2
library(rmarkdown)
library(readxl)
A4Q2 <- read_excel("C:/Users/edavi/OneDrive/Desktop/A4Q2.xlsx")
View(A4Q2)
ggscatter(A4Q2,x="phone",y="sleep",add="reg.line",xlab="Phone (hours)",ylab="Sleep (hours)")

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

mean(A4Q2$phone)
## [1] 3.804609

[1] 3.804609

sd(A4Q2$phone)
## [1] 2.661866

[1] 2.661866

median(A4Q2$phone)
## [1] 3.270839

[1] 3.270839

mean(A4Q2$sleep)
## [1] 7.559076

[1] 7.559076

sd(A4Q2$sleep)
## [1] 1.208797

[1] 1.208797

median(A4Q2$sleep)
## [1] 7.524099

[1] 7.524099

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

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

Phone is abnormally distributed. The data is positively skewed. The data does not have a proper bell curve. Sleep is abnormally distributed. The data is negatively skewed. The data has a proper bell curve.

shapiro.test(A4Q2$phone)
## 
##  Shapiro-Wilk normality test
## 
## data:  A4Q2$phone
## W = 0.89755, p-value = 9.641e-09

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

Shapiro-Wilk normality test

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

Phone is abnormal Sleep is abnormal

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

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 Spearman correlation was conducted to test the relationship between phone use (Mdn = 3.27) amd sleep (Mdn = 7.52). There was a statistically significant relationship between the two variables, rho = -.65, p < .001. The relationship was negative and strong. As phone use increased, sleep decreased.