Install Packsleeps

library(readxl)
library(ggpubr)

## Loading required package: ggplot2

Import Data

A5Q2 <- read_excel("~/Library/CloudStorage/OneDrive-SaintLouisUniversity/AA-5221-12/Assignment 5/A5Q2/A5Q2.xlsx")

Create a Scatterplot

ggscatter(
  A5Q2,
  x = "sleep",
  y = "phone",
  add = "reg.line",
  xlab = "sleep",
  ylab = "phone"
)

## Interpret the Scatterplot

# The relationship is linear.
# The relationship is negative.
# There are outliers.

Calculate Descriptive Statistics

mean(A5Q2$sleep)

## [1] 7.559076

sd(A5Q2$sleep)

## [1] 1.208797

median(A5Q2$sleep)

## [1] 7.524099

mean(A5Q2$phone)

## [1] 3.804609

sd(A5Q2$phone)

## [1] 2.661866

median(A5Q2$phone)

## [1] 3.270839

Create Histograms

hist(A5Q2$sleep,
     main = "sleep",
     breaks = 20,
     col = "lightblue",
     border = "white")

hist(A5Q2$phone,
     main = "phone",
     breaks = 20,
     col = "lightcoral",
     border = "white")

## Interpret the Histograms

# Variable 1: sleep
# The variable looks abnormally distributed.
# The data is negatively skewed.
# The data does not have a proper bell curve.

# Variable 2: phone
# The variable looks abnormally distributed.
# The data is positively skewed.
# The data does not have a proper bell curve.

Run the Shapiro-Wilk Tests

options(scipen = 999)
shapiro.test(A5Q2$sleep)

## 
##  Shapiro-Wilk normality test
## 
## data:  A5Q2$sleep
## W = 0.91407, p-value = 0.00000008964

shapiro.test(A5Q2$phone)

## 
##  Shapiro-Wilk normality test
## 
## data:  A5Q2$phone
## W = 0.89755, p-value = 0.000000009641

Interpret the Shapiro-Wilk Tests

# Variable 1: sleep
# The variable is abnormally distributed (p <.001).

# Variable 2: phone
# The variable is abnormally distributed (p <.001).

Conduct the Spearman Correlation

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

## 
##  Spearman's rank correlation rho
## 
## data:  A5Q2$sleep and A5Q2$phone
## S = 908390, p-value < 0.00000000000000022
## 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 (M = 7.56, SD = 1.21) and phone (M = 3.80, SD = 2.66).
# There was not a statistically significant relationship between the two variables, ρ = -0.61, p <.001.
# The relationship was negative and strong.
# As sleep increased, phone increased.