a4q2 resub

library(ggpubr)
library(rmarkdown)

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 outliers.

#descriptive statistics

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

#histogram for phone

hist(A4Q2$phone,
     main = "Phone (hours)",
     breaks = 20,
     col = "green",
     border = "yellow",
     cex.main = 1,
     cex.axis = 1,
     cex.lab = 1)

#histogram for sleep
hist(A4Q2$sleep,
     main = "Sleep (hours)",
     breaks = 20,
     col = "black",
     border = "pink",
     cex.main = 1,
     cex.axis = 1,
     cex.lab = 1)

# Phone is abnormally distributed. The data is positively skewed. The data does not have a proper bell curve.
# Sleep is abnormally distributed. The data is negatively skewed. The data has a proper bell curve.

#Normality tests

shapiro.test(A4Q2$phone)
# Shapiro-Wilk normality test
# data: A4Q2$phone
# W = 0.89755, p-value = 9.641e-09
shapiro.test(A4Q2$sleep)

# Shapiro-Wilk normality test

# data: A4Q2$sleep
# W = 0.91407, p-value = 8.964e-08
# Phone is abnormal.
# Sleep is abnormal.

#spearman correlation
cor.test(A4Q2$phone,A4Q2$sleep,method="spearman")

# spearman 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 correlation was conducted to test the relationship between phone use (Mdn = 3.27) and sleep (Mdn = 7.52). There was a statistically significant relationship between the two variables, ρ = -.65, p < .001. The relationship was negative and strong. As phone use increased, sleep decreased.