A5Q2

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

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

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

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

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

# 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.

shapiro.test(A5Q2$sleep)

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

shapiro.test(A5Q2$phone)

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

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

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

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

## 
##  Spearman's rank correlation rho
## 
## data:  A5Q2$sleep and A5Q2$phone
## 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
# sleep (Mdn = 7.52) and phone use (Mdn = 3.27).
# There was a statistically significant relationship between the two
# variables, rho = -.61, p < .001.
# The relationship was negative and strong.
# As sleep increased, phone use decreased.

```