library(readxl)
library(ggpubr)
## Loading required package: ggplot2
A4Q1 <- read_excel("Downloads/A4Q1.xlsx")

#This is the line that automatically imports the file whenever I want to work on analyzing the data after closing and re-opening R Studio.

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 or strong 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 = "maroon",
     border = "white",
     cex.main = 1,
     cex.axis = 1,
     cex.lab = 1)

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

Variable 1: age The first variable looks normally distributed. The data is symmetrical. The data has a proper bell curve.

Variable 2: education The second variable looks normally distributed. The data is symmetrical. The data 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

Variable 1: age The first variable is normally distributed (p = 0.5581).

Variable 2: education The second variable is normally distributed (p = 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 years of 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 the age increased, years of education increased.