A4Q2

library(readxl)
library(ggpubr)

## Warning: package 'ggpubr' was built under R version 4.4.3

## Loading required package: ggplot2

## Warning: package 'ggplot2' was built under R version 4.4.3

DatasetZ <- read_excel("C:/Users/niha/Downloads/A4Q2.xlsx")
ggscatter(
  DatasetZ,
  x = "phone",
  y = "sleep",
  add = "reg.line",
  xlab = "phone usage",
  ylab = "sleep"
)

## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the ggpubr package.
##   Please report the issue at <https://github.com/kassambara/ggpubr/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the ggpubr package.
##   Please report the issue at <https://github.com/kassambara/ggpubr/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

mean(DatasetZ$phone)

## [1] 3.804609

sd(DatasetZ$phone)

## [1] 2.661866

median(DatasetZ$phone)

## [1] 3.270839

mean(DatasetZ$sleep)

## [1] 7.559076

sd(DatasetZ$sleep)

## [1] 1.208797

median(DatasetZ$sleep)

## [1] 7.524099

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

hist(DatasetZ$sleep,
     main = "sleep",
     breaks = 20,
     col = "pink",
     border = "white",
     cex.main = 1,
     cex.axis = 1,
     cex.lab = 1)

# Variable 1: phone # The first variable looks abnormally distributed. # The data is positively skewed.

Variable 2: sleep

The first variable looks abnormally distributed.

The data is symmetrical / negatively skewed.

The data does not have a proper bell curve.

shapiro.test(DatasetZ$phone)

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

shapiro.test(DatasetZ$sleep)

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

#Variable 1: phone # The first variable is abnormally distributed (p < .001). # Variable 2: sleep # The second variable is abnormally distributed (p < .001).

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

## 
##  Spearman's rank correlation rho
## 
## data:  DatasetZ$phone and DatasetZ$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 Variable 1 (Mdn = 3.27) and Variable 2 (Mdn = 7.52).

There is a statistically significant relationship between the two variables, ρ = -.61, p < .001.

The relationship was negative and strong.

As the phone increased, the dependent sleep decreased.

The relationship is linear.

The relationship is not positive.

The relationship is moderate to strong.

There are few outliers.