library(readxl)
library(ggpubr)
## Loading required package: ggplot2
DatasetZ <- read_excel("C:/Users/raadrish/Downloads/A4Q2.xlsx")

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

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

mean(DatasetZ$phone)
## [1] 3.804609
sd(DatasetZ$phone)
## [1] 2.661866
median(DatasetZ$phone)
## [1] 3.270839
mean(DatasetZ$sleep)
## [1] 7.559076
sd(DatasetZ$sleep)
## [1] 1.208797
median(DatasetZ$sleep)
## [1] 7.524099
hist(DatasetZ$phone,
main = "Phone",
breaks = 20,
col = "lightblue",
border = "white",
cex.main = 1,
cex.axis = 1,
cex.lab = 1)

hist(DatasetZ$sleep,
main = "sleep",
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.

Variable 2: phone The second variable phone looks abnormally distributed. The data is positively skewed. The data does not have a proper bell curve.

shapiro.test(DatasetZ$phone)
## 
##  Shapiro-Wilk normality test
## 
## data:  DatasetZ$phone
## W = 0.89755, p-value = 9.641e-09
shapiro.test(DatasetZ$sleep)
## 
##  Shapiro-Wilk normality test
## 
## data:  DatasetZ$sleep
## W = 0.91407, p-value = 8.964e-08

Variable 1: phone The first variable is abnormally distributed (p < .001).

Variable 2: sleep The second variable is abnormally distributed (p < .001).

cor.test(DatasetZ$phone, DatasetZ$sleep, method = "spearman")
## 
##  Spearman's rank correlation rho
## 
## data:  DatasetZ$phone and DatasetZ$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 usage (Mdn = 3.27) and sleep (Mdn = 7.52).

There was a statistically significant relationship between the two variables, ρ = -.61, p < .001.

The relationship was negative and strong.

As the independent variable increased, the dependent variable decreased.