Assignment 4 Question 1

{r}A4Q1 <- read_excel("Downloads/A4Q1.xlsx")

{r}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 to strong. There are no outliers.

{r}mean(A4Q1$age) sd(A4Q1$age) median(A4Q1$age)

{r}mean(A4Q1$education) sd(A4Q1$education) median(A4Q1$education)

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

{r}hist(A4Q1$education, main = "Education", breaks = 20, col = "lightcoral", 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.

{r}shapiro.test(A4Q1$age) {r}shapiro.test(A4Q1$education)

Variable 1: Age The first variable is normally distributed (p = 0.56).

Variable 2: Education The first variable is [normally / abnormally] distributed (p = 0.44).

{r}cor.test(A4Q1$age, A4Q1$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 a statistically significant relationship between the two variables, r(148) = 0.52, p < .001. The relationship was positive and strong. As age increased, education increased.