library(readxl) library(ggpubr) 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\(age) ##[1] 35.32634 sd(A4Q1\)age) ##[1] 11.45344 median(A4Q1$age) ##[1] 35.79811

mean(A4Q1\(education) ##[1] 13.82705 sd(A4Q1\)education) ##[1] 2.595901 median(A4Q1$education) ##[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.test(A4Q1$education)

##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

#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.