library(readxl)
library(ggpubr)
## Loading required package: ggplot2
library(rmarkdown)
 A4Q1 <- read_excel("C:/Users/edavi/OneDrive/Desktop/A4Q1.xlsx")
 View(A4Q1)
ggscatter(A4Q1,x="age",y="education",add="reg.line",xlab="Age",ylab="Education")

The relationship is linear. The relationship is positive. The relationship is moderate. There are no outliers.

mean(A4Q1$age)
## [1] 35.32634

[1] 35.32634

sd(A4Q1$age)
## [1] 11.45344

[1] 11.45344

median(A4Q1$age)
## [1] 35.79811

[1] 35.79811

 mean(A4Q1$education)
## [1] 13.82705

[1] 13.82705

sd(A4Q1$education)
## [1] 2.595901

[1] 2.595901

median(A4Q1$education)
## [1] 14.02915

[1] 14.02915

hist(A4Q1$age,main="Age",breaks=20,col="lightblue",border="white",cex.main=1,cex.axis=1,cex.lab=1)

 hist(A4Q1$education,main="Education",breaks=20,col="lightcoral",border="white",cex.main=1,cex.axis=1,cex.lab=1)

Age is normally distributed. It is symmetrical. It has a proper bell curve. Education is normally distributed. It is symmetrical. It has a proper bell curve.

shapiro.test(A4Q1$age)
## 
##  Shapiro-Wilk normality test
## 
## data:  A4Q1$age
## W = 0.99194, p-value = 0.5581

Shapiro-Wilk normality test

data: A4Q1$age W = 0.99194, p-value = 0.5581

shapiro.test(A4Q1$education)
## 
##  Shapiro-Wilk normality test
## 
## data:  A4Q1$education
## W = 0.9908, p-value = 0.4385

Shapiro-Wilk normality test

data: A4Q1$education W = 0.9908, p-value = 0.4385

cor.test(A4Q1$age,A4Q1$education,method="pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  A4Q1$age and A4Q1$education
## t = 7.4066, df = 148, p-value = 9.113e-12
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3924728 0.6279534
## sample estimates:
##       cor 
## 0.5200256

Pearson’s product-moment correlation

data: A4Q1\(age and A4Q1\)education t = 7.4066, df = 148, p-value = 9.113e-12 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: 0.3924728 0.6279534 sample estimates: cor 0.5200256

A pearson correlation was conducted to test the relationship between age (M = 35.33, SD = 11.45) and education (M = 13.83, SD = 2.60). There was a statistically significant relationship between the two variables, r(148) = .52, p < .001. The relationship was positive and strong. As age increased, education increased.