A4Q1 <- read_excel(“Desktop/AA-5221-22 Applied Analytics & Methods I/A4Q1.xlsx”) View(A4Q1)
ggscatter(A4Q1, “age”,“education”, add = “reg.line”, xlab = “Age”, Ylab=“Education”) ggscatter(A4Q1, “Age”,“Education”, add = “reg.line”, xlab = “Age”, Ylab=“Education”) 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 weak relationship between the variables due to the dots being loosely around the line # There are no meaningful outliners that would change the direction or slope of the line mean(dataset A4Q1\(age) mean(A4Q1\)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) hist(A4Q1\(education) hist(A4Q1\)education,) hist(A4Q1\(education, main = "education", breaks = 20, col = "lightblue", border = "white") hist(A4Q1\)age) hist(A4Q1\(education) #Variable 1 Age #Variable 1 age looks normally distributed #The data is symetrical #The data has a proper bell curve #Variable 2 Education #Variable 2 Educatioin looks normally distributed #The data is symetrical #The data has a proper bell curve shapiro.test(A4Q1\)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 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 = 2.59). #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”)