A4Q1

install.packages(“readxl”) install.packages(“ggpubr”)

library(readxl)

## Warning: package 'readxl' was built under R version 4.4.3

library(ggpubr)

## Warning: package 'ggpubr' was built under R version 4.4.3

## Loading required package: ggplot2

## Warning: package 'ggplot2' was built under R version 4.4.3

A4Q1 <- read_excel("C:/Users/vinay_17rmu0l/Desktop/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 obvious outliers.

Statstics

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")

hist(A4Q1$education,
     main = "Education",
     breaks = 20,
     col = "lightcoral",
     border = "white")

Age looks normally distributed. Education looks normally distributed.

shapiro

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

both the variables are normally distributed. We are going to do pearson test as histogram and variables are 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

A Pearson correlation was conducted to test the relationship between age (m= 35.32, sd = 11.45) and education (m = 13.82 , sd = 2.59) There was a statistically significant relationship between the two variables, r(df) = .xx, p = .9.113e-12. The relationship was positive As age increased, education increased.