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library(readxl)
library(ggpubr)

## Loading required package: ggplot2

A4Q1 <- read_excel("C:\\Users\\Tharu\\Downloads\\A4Q1.xlsx")

ggscatter(
  A4Q1,
  x = "age",
  y = "education",
  add = "reg.line",
  xlab = "age",
  ylab = "education"
)

# The relationship between the two variables is linear. # The relationship between the two variables is positive. # The relationship between the two variables is moderate. # The relationship between the two variables 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$age)

## [1] 35.79811

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

# Variable 1: Age # The first variable looks approximately normally distributed. # The data is fairly symmetrical. # The data shows a roughly bell-shaped curve.

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

# 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

Variable 1: Age

The first variable is normally distributed (p = 0.5581).

shapiro.test(A4Q1$education)

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

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 = 35.32634, SD = 11.45344) and education (M = 13.82705, SD = 2.595901).

There was a statistically significant relationship between the two variables,

r(148) = .52, p < .001.

The relationship was positive and moderate.

As age increased, education increased.

cor.test(A4Q1$age, A4Q1$education, method = "spearman")

## 
##  Spearman's rank correlation rho
## 
## data:  A4Q1$age and A4Q1$education
## S = 267492, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.5244375

A Spearman correlation was conducted to test the relationship between age

(Mdn =35.79811) and education (Mdn = 35.79811).

There was a statistically significant relationship between the two variables,

ρ = .52, p < .001.

The relationship was positive and moderate.

As age increased, education increased.