library(readxl)
library(ggpubr)
library(rmarkdown)
A4Q2 <- read_excel("/Users/murphy/Downloads/A4Q2.xlsx")
ggscatter(
  A4Q2,
  x = "phone",
  y = "sleep",
  add = "reg.line",
  xlab = "Phone",
  ylab = "Sleep"
)

The relationship is linear. The relationship is negative. The relationship is moderate. There are outliers (the three data points where the phone usage is greater than 10 and the sleep is less than 4).

mean(A4Q2$phone)
## [1] 3.804609
sd(A4Q2$phone)
## [1] 2.661866
median(A4Q2$phone)
## [1] 3.270839
mean(A4Q2$sleep)
## [1] 7.559076
sd(A4Q2$sleep)
## [1] 1.208797
median(A4Q2$sleep)
## [1] 7.524099

Phone Time mean - 3.80 sd - 2.66 median - 3.27

Sleep hours mean - 7.56 sd - 1.21 median - 7.52

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)

Variable 1: Hours of Phone User The first variable looks abnormally distributed. The data is positively skewed. The data does not have a proper bell curve.

Variable 2: Hours of Sleep The second variable looks 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.test(A4Q2$sleep)
## 
##  Shapiro-Wilk normality test
## 
## data:  A4Q2$sleep
## W = 0.91407, p-value = 8.964e-08

Shapiro-Wilk normality test data: A4Q2$phone W = 0.89755, p-value = 9.641e-09

Shapiro-Wilk normality test data: A4Q2$sleep W = 0.91407, p-value = 8.964e-08

Variable 1: Phone Phone is abnormally distributed (p < 0.05).

Variable 2: Name of Variable Sleep is abnormally distributed (p < 0.05).

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) and sleep (Mdn = 7.52). There was a statistically significant relationship between the two variables, ρ = -0.61, p < .001. The relationship was negative and strong. As phone use increased, sleep decreased.