library(readxl)
library(ggpubr)
## Loading required package: ggplot2
library(ggplot2)
data_phone <-read_excel("A4Q2.xlsx")
data_phone
## # A tibble: 150 × 2
##    sleep  phone
##    <dbl>  <dbl>
##  1  9.03  1.78 
##  2  6.76  6.62 
##  3  9.18  0.289
##  4  7.20  3.33 
##  5  3    10    
##  6  6.71  4.24 
##  7  7.99  0.701
##  8 10.1   0.261
##  9  6.91  6.75 
## 10  7.50  3.95 
## # ℹ 140 more rows
ggscatter(
  data_phone,
  x = "phone",
  y = "sleep",
  add = "reg.line",
  xlab = "phone",
  ylab = "sleep"
)

The relationship is linear. The relationship is positive. The relationship is weak. There are no clear outliers.

mean(data_phone$phone)
## [1] 3.804609
sd(data_phone$phone)
## [1] 2.661866
median(data_phone$phone)
## [1] 3.270839
mean(data_phone$sleep)
## [1] 7.559076
sd(data_phone$sleep)
## [1] 1.208797
median(data_phone$sleep)
## [1] 7.524099
hist(data_phone$phone,
     main = "Phone",
     breaks = 20,
     col = "lightblue",
     border = "white",
     cex.main = 1,
     cex.axis = 1,
     cex.lab = 1)

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

#Variable 1: Phone
#The variable is not normally distributed.
#The data is not symmetrical.
#The data does not form a proper bell curve.
#Variable 2: Sleep
#The variable is not normally distributed.
#The data is not symmetrical.
#The data does not form a proper bell curve.
shapiro.test(data_phone$phone)
## 
##  Shapiro-Wilk normality test
## 
## data:  data_phone$phone
## W = 0.89755, p-value = 9.641e-09
#Shapiro-Wilk normality test
#data:  data_phone$phone
#W = 0.89755, p-value = 9.641e-09

shapiro.test(data_phone$sleep)
## 
##  Shapiro-Wilk normality test
## 
## data:  data_phone$sleep
## W = 0.91407, p-value = 8.964e-08
#Shapiro-Wilk normality test
#data:  data_phone$sleep
#W = 0.91407, p-value = 8.964e-08
#There is evidence to reject normality for either variable.
cor.test(data_phone$phone, data_phone$sleep, method = "spearman")
## 
##  Spearman's rank correlation rho
## 
## data:  data_phone$phone and data_phone$sleep
## S = 908390, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.6149873

A Pearson correlation was conducted to test the relationship between phone use and sleep.
There was a statistically significant relationship between the two variables, r(148) = -.61, p < .001.
The relationship was negative and moderate.
As phone use increased, sleep decreased.