A4Q1

library(readxl)

## Warning: package 'readxl' was built under R version 4.5.3

library(ggpubr)

## Warning: package 'ggpubr' was built under R version 4.5.3

## Loading required package: ggplot2

## Warning: package 'ggplot2' was built under R version 4.5.3

A4Q1 <- read_excel("C:/Users/jrutebuka/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 weak. 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 = "blue",
     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). 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.

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”) cor.test(A4Q1\(age, A4Q1\)education, method = “spearman”)

A Pearson correlation was conducted to test the relationship between Variable 1 (M = 35.32634, SD = 11.45344) and Variable 2 (M = 13.82705, SD = 2.595901). There [was / was not] a statistically significant relationship between the two variables, r(df) =148, p = 9.113e-12. The relationship was positive/ negative and strong/ moderate/ weak. As the independent variable increased, the dependent variable increased/ decreased.

median(A4Q1\(education) mean(A4Q1\)education) sd(A4Q1\(education) median(A4Q1\)education)

A Spearman correlation was conducted to test the relationship between Variable 1 (Mdn =35.79811) and Variable 2 (Mdn =14.02915). There [was / was not] a statistically significant relationship between the two variables, ρ =0.5581, p =0.4385. The relationship was positive/ negative and strong/ moderate/ weak. As the independent variable increased, the dependent variable increased/ decreased. ```