As4Qu1

library(ggpubr)

## Loading required package: ggplot2

library(readxl)

A4Q1 <- read_excel("A4Q1.xlsx")

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

Age is normal Education is normal

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

The 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 is strong and positive and as age increased so did education.