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

The relationship is negatrive.

The relationship is moderate or strong.

There are outliers

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
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 looks abnormally distributed. The data is negatively skewed. The data does not have a bell curve.

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

Variable 2:sleep The second variable looks abnormally distributed. The data is negatively skewed. The data does not have 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

Variable 1: phone The first variable is normally distributed (p = 9.641e-09).

Variable 2: education The second variable is normally distributed (p = 8.964e-08).

cor.test(A4Q2$phone, A4Q2$sleep, method = "pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  A4Q2$phone and A4Q2$sleep
## t = -11.813, df = 148, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7708489 -0.6038001
## sample estimates:
##        cor 
## -0.6966497

A Pearson correlation was conducted to test the relationship between Variable 1 (M = 3.804609 , SD = 2.661866) and Variable 2 (M = 7.559076, SD = 1.208797 ). There [was / was not] a statistically significant relationship between the two variables, r(148) = _.61, p = < .001. The relationship was positive and strong. As age increased, income increased.