library(readxl)
library(ggpubr)
## Loading required package: ggplot2
A4Q1 <- read_excel("~/Downloads/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 or strong. 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\(age), Error: unexpected ',' in "hist(A4Q1\)age),”

install.packages(hist) Error in as.character(x) : cannot coerce type ‘closure’ to vector of type ‘character’

hist(A4Q1\(age), Error: unexpected ',' in "hist(A4Q1\)age),”

hist(A4Q1$age) main = “age”, Error: unexpected ‘,’ in “main =”age”,”

hist(A4Q1$age) main = “age” breaks = 20, Error: unexpected ‘,’ in “breaks = 20,”

hist(A4Q1$age) main = “age” breaks = 20 col = “lightblue” border = “white” cex.main = 1, Error: unexpected ‘,’ in “cex.main = 1,”

hist(A4Q1$age) main = “age” breaks = 20 col = “lightblue” border = “white” cex.main = 1 cex.axis = 1 cex.lab = 1 , Error: unexpected ‘,’ in “,”

hist(A4Q1$age, + main = “age”, + breaks = 20, + col = “lightblue” + border = “white”, Error: unexpected symbol in: “col =”lightblue” border”

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

hist(A4Q1\(education, + main = "education", + breaks = 20, + col = "lightblue", + border = "white", + cex.main = 1, + cex.axis = 1, + + col = "lightcoral", + cex.lab = 1) Error in hist.default(A4Q1\)education, main = “education”, breaks = 20, : formal argument “col” matched by multiple actual arguments

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 = "white",
 cex.main = 1,
 cex.axis = 1,
 cex.lab = 1)

Variable 1: age The first variable looks normally distributed. The data is positively skewed. The data does not have a proper bell curve.

Varible 2: education The second varible looks normally distributed. The data is negatively skewed. 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 = 0.55)

Variable 2: Education The second variable is normally distributed (p = .44)

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 Pearsons correlation was conducted to test the relationship between Variable 1 (M = 35.33, SD = 11.45) and Variable 2 (M = 13.83, SD = 2.60). There was a statistically significant relationship between the two variables, r(148) = .52 p<.001. The relationship was positive and moderate. As age increased, education increased