library(readxl)
library(ggpubr)

## Loading required package: ggplot2

A5Q2 <- read_excel("//apporto.com/dfs/SLU/Users/brentgallagher_slu/Desktop/A5Q2.xlsx")
ggscatter(
  A5Q2,
  x = "phone",
  y = "sleep",
  add = "reg.line",
  xlab = "phone",
  ylab = "sleep"
)

# The relationship is linear.
# The relationship is negative.
# There are outliers.
# I was incorrect with my previous discription with line 16.

mean(A5Q2$phone)

## [1] 3.804609

sd(A5Q2$phone)

## [1] 2.661866

median(A5Q2$phone)

## [1] 3.270839

mean(A5Q2$sleep)

## [1] 7.559076

sd(A5Q2$sleep)

## [1] 1.208797

median(A5Q2$sleep)

## [1] 7.524099

hist(A5Q2$phone,
     main = "phone",
     breaks = 20,
     col = "lightblue",
     border = "white",
     cex.main = 1,
     cex.axis = 1,
     cex.lab = 1)

hist(A5Q2$sleep,
     main = "phone",
     breaks = 20,
     col = "lightcoral",
     border = "white",
     cex.main = 1,
     cex.axis = 1,
     cex.lab = 1)

# Variable 1: Phone
# The variable looks abnormally distributed (p < .05).

# Variable 2: Sleep
# The variable looks abnormally distributed.
# I was incorrect in my first attempt discription of line 49 (p < .05).

shapiro.test(A5Q2$phone)

## 
##  Shapiro-Wilk normality test
## 
## data:  A5Q2$phone
## W = 0.89755, p-value = 9.641e-09

shapiro.test(A5Q2$sleep)

## 
##  Shapiro-Wilk normality test
## 
## data:  A5Q2$sleep
## W = 0.91407, p-value = 8.964e-08

# Variable 1: Phone
# The variable is abnormally distributed (p = 9.641e-09).

# Variable 2: Sleep
# The variable is abnormally distributed (p = 8.964e-08).

cor.test(
A5Q2$phone,
A5Q2$sleep,
method = "spearman"
)

## 
##  Spearman's rank correlation rho
## 
## data:  A5Q2$phone and A5Q2$sleep
## S = 908390, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.6149873

# I was wrong with the method in my fist attempt. I chose Pearson instead of Spearman. So my write up was incorrect, so everything in line 69 is different and I didnt have line 70 and 71 in my first attempt.
# A Spearman correlation was conducted to test the relationship between phone use (Mdn = 3.27) and sleep duration (Mdn = 7.52).
# There was a statistically significant relationship between th two variables, p = 1.61, p < .001.
# The relationship was negative and strong.
# As phone use increased, sleep decreased.