```{library(readxl) library(ggpubr) library(rmarkdown)

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.

#descriptive statistics

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

#histogram for age

hist(A4Q1$age, main = “Age”, breaks = 20, col = “white”, border = “yellow”, cex.main = 1, cex.axis = 1, cex.lab = 1) #Age is normally distributed. data is symmetrical. data has a proper bell curve.

#histogram for education

hist(A4Q1$education, main = “Education”, breaks = 20, col = “light blue”, border = “white”, cex.main = 1, cex.axis = 1, cex.lab = 1)

#education is normally distributed. data is symmetrical. data has a proper bell curve.

#normality test

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 # Age is normal. # Education is normal.

Conduct inferential test (pearson correlation)

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. ```