A4Q2 <- read_excel(“Desktop/AA-5221-22 Applied Analytics & Methods I/A4Q2.xlsx”) View(A4Q2)
ggscatter(A4Q2, “sleep”, “phone”, add = “reg.line”, xlab = “sleep”, ylab = “phone”) #The dots form a linear pattern and is required for a Pearson Correlation #The relationship direction indicates a negative relationship between the variables #The dots are tightly hugging the line and this indicates a medium to strong relationship between the variables #There are no outlines mean(A4Q2\(sleep) [1] 7.559076 sd(A4Q2\)sleep) [1] 1.208797 median(A4Q2\(sleep) [1] 7.524099 mean(A4Q2\)phone) [1] 3.804609 sd(A4Q2\(phone) [1] 2.661866 median(A4Q2\)phone) [1] 3.270839 hist(A4Q2\(sleep) hist(A4Q2\)phone) #Variable 1 Sleep #The first variable looks normally distributed #The data is negatively skewed #The data has a proper bell curve #Variable 2 Phone #The second variable looks abnormally distributed #The data is positively skewed #The data does not have a proper bell curve shapiro.test(A4Q2$sleep)

Shapiro-Wilk normality test

data: A4Q2$sleep W = 0.91407, p-value = 8.964e-08

shapiro.test(A4Q2$phone)

Shapiro-Wilk normality test

data: A4Q2$phone W = 0.89755, p-value = 9.641e-09

#Variable 1: sleep #The first variable is abnormally distributed (P = .001) #Variable 2: phone #The second variable is abnormally distributed (P = .001) #Overall data is not normal. Use Spearman Correlation cor.test(A4Q2\(sleep, A4Q2\)phone, method = “spearman”)

Spearman's rank correlation rho

data: A4Q2\(sleep and A4Q2\)phone S = 908390, p-value < 2.2e-16 alternative hypothesis: true rho is not equal to 0 sample estimates: rho -0.6149873

#A Spearman correlation was conducted to test the relationship between sleep (mdn = 7.52) and phone (mdn = 3.27) #There was a statistical significant relationship between the two variables, p = .001, p = .001 #The relationship was negative and strong #As sleep decreased, phone usage increased library(rmarkdown)