```(r) ggscatter( A4Q2, x = “phone”, y = “sleep”, add = “reg.line”, xlab = “Phone (hours)”, ylab = “Sleep (hours)” ) # the relationship is linear # the relationship is negative # the relationship is moderate # there are NO outliers

mean(A4Q2\(phone) #[1] 3.804609 sd(A4Q2\)phone) #[1] 2.661866 median(A4Q2\(phone) #[1] 3.270839 mean(A4Q2\)sleep) #[1] 7.559076 sd(A4Q2\(sleep) #[1] 1.208797 median(A4Q2\)sleep) #[1] 7.524099

hist(A4Q2$phone, main = “Phone (hours)”, breaks = 20, col = “lightblue”, border = “white”, cex.main = 1, cex.axis = 1, cex.lab = 1)

hist(A4Q2$sleep, main = “Sleep (hours)”, breaks = 20, col = “green”, border = “white”, cex.main = 1, cex.axis = 1, cex.lab = 1) #variable 1 : Phone # the first variable looks abnormally distributed # the data looks skewed # the data does have a proper bell curve

variable 2 : Sleep

the first variable looks normally distributed

the data looks symmetrical

the data does have a proper bell curve

variable 1 : phone

the first variable is normally distributed (p=8.964e-08)

variable 2 : sleep

there was a stastically significant relationship between the two variables, r(df#) = -103.00, p = 2.2e-16

the relationship was negative and strong

as phone usage increased sleep decreased

#shapiro test shapiro.test(A4Q2$phone)

#Shapiro-Wilk normality test

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

phone is abnormal

#sleep is abnormal

cor.test(A4Q2\(phone, A4Q2\)sleep, method = “spearman”) # Spearman’s rank correlation rho

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

A spearman test was concluded that phone usage relationship (mdn=3.27) and sleep (mdn=7.52).

#The relationship between the 2 were statistically relevant p=-.65, p<.001. # the relationship was negative and strong # as phone usage increased sleep decreased ’’’