As4Qu1

’’‘{r} install.packages(“readxl”)’’’ ’’‘{r} install.packages(“ggpubr”)’’’ ’’‘{r} library(readxl)’’’ ’’‘{r} library(ggpubr)’’’ ’’‘{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. There are no outliers. ’’‘{r} mean(A4Q1\(age) sd(A4Q1\)age) median(A4Q1\(age) mean(A4Q1\)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)’’’ Age Age is normally distributed. Age is symmetrical. Age has a proper bell curve. Education Education is normally distributed. Education is symmetrical. Education has a proper bell curve. ’’‘{r} shapiro.test(A4Q1\(age) ''' '''{r} shapiro.test(A4Q1\)education)’’’ Age is normal Education is normal ’’‘{r} cor.test(A4Q1\(age, A4Q1\)education, method = “pearson”)’’’ The 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) = .52, P < .001. The relationship is strong and positive and as age increased so did education.