#Open packages library(readxl) library(ggpubr) library(rmarkdown)
#Import dataset library(readxl) A4Q2 <-
read_excel(“Desktop/AA-5221-22 Applied Analytics & Methods
I/A4Q2.xlsx”) View(A4Q2)
#create scatterplot ggscatter(A4Q2, x = “phone”, y = “sleep”, add =
“reg.line”, xlab = “phone”, ylab = “sleep”) #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 moderate
relationship between the variables #There are outliers #Calculate
descriptive statistics 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 #create histogram for phone
hist(A4Q2\(phone,
main = "Phone (hours)",
breaks = 20,
col = "lightblue",
border = "white",
cex.main = 1,
cex.axis = 1,
cex.lab = 1)
#Create histogram for sleep
hist(A4Q2\)sleep, main = “Sleep (hours)”, breaks = 20, col =
“lightcoral”, border = “white”, cex.main = 1, cex.axis = 1, cex.lab = 1)
# Phone is abnormally distributed. # The phone data is positively
skewed. # The data phone does not have a proper bell curve. # Sleep is
abnormally distributed. # The sleep data is negatively skewed. # The
sleep data has a proper bell curve. #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