’’’{r} library(readxl) library(ggpubr) library(rmarkdown) #import
dataset A4Q1 <- read_excel(“Desktop/AA-5221-22 Applied Analytics
& Methods I/A4Q1.xlsx”) View(A4Q1)
#create scatterplot ggscatter(A4Q1, “age”, “education”, add =
“reg.line”, xlab = “age”, ylab = “education”) # The dots form a
straight-line pattern, and the pattern is linear. This dataset best fits
what is required for a Pearson Correlation #There is a positive
relationship between the variables # There is a moderate relationship
between the variables # There are no meaningful outliners that would
change the direction or slope of the line mean(A4Q1\(age)
35.32634
sd(A4Q1\)age) 11.45344 median(A4Q1\(age)
35.79811
mean(A4Q1\)education) 13.82705 sd(A4Q1\(education)
2.595901
median(A4Q1\)education) 14.02915 #create histogram for age
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, cex.lab = 1)
#Variable 1 Age #Variable 1 age looks normally distributed #The data is
symetrical #The data has a proper bell curve #Variable 2 Education
#Variable 2 Educatioin looks normally distributed #The data is
symetrical #The data has a proper bell curve #normality tests
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 data:
A4Q1\(age
#Variable 1: Age
#The first variable is normally distributeed P=0.5581
#Variable 2: Education
#The second variable is normally distributed P=0.4385
#Overall data is normal. Use Pearson Correlation
cor.test(A4Q1\)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 Pearson correlation was conducted to test the relationship between Age (M = 35.32, SD = 11.45) and Education (M = 13.82, SD = 2.60). #There was a statistical significant relationship between the two variables, r(148) = p<.001 #The relationship was positive and strong #As Age increased, education increased install.packages(“rmarkdown”)