```{r install.packages(“readxl”) install.packages(“ggpubr”) library(readxl) library(ggpubr) ggscatter( A5Q1, x = “age”, y = “education”, add = “reg.line”, xlab = “age”, ylab = “education” )

The relationship is linear.

The relationship is positive.

There are outliers.

mean(A5Q1\(age) sd(A5Q1\)age) median(A5Q1$age)

median(A5Q1\(education) sd(A5Q1\)education) median(A5Q1$education)

hist(A5Q1$age, main = “age”, breaks = 40, col = “lightblue”, border = “white”)

hist(A5Q1$education, main = “education”, breaks = 20, col = “lightcoral”, border = “white”)

Variable 1: Age

The variable looks normally distributed.

The data is symmetrical.

The data does have a proper bell curve.

Variable 2: Education

The variable looks normally distributed.

The data is positively skewed.

The data does not have a proper bell curve.

shapiro.test(A5Q1\(age) shapiro.test(A5Q1\)education)

Variable 1: age

The variable is normally distributed (p = .56).

Variable 2: education

The variable is normally distributed (p = .44)

cor.test( A5Q1\(age, A5Q1\)education, method = “pearson” )

A 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 not a statistically significant relationship between the two variables, r(148) = .74, p < .001.

The relationship was positive and moderate.

As age increased, education increased.

```