```{r}library(readxl) library(ggpubr) ggscatter( A5Q2, x = “phone”, y = “sleep”, add = “reg.line”, xlab = “phone”, ylab = “sleep” ) # The relationship is linear. # The relationship is negative. # There are outliers.

mean(A5Q2\(phone) sd(A5Q2\)phone) median(A5Q2$phone)

mean(A5Q2\(sleep) sd(A5Q2\)sleep) median(A5Q2$sleep)

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

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

Variable 1: phone

The variable looks normally distributed.

The data is positively skewed..

The data does not have a proper bell curve.

Variable 2: sleep

The variable looks normally distributed.

The data is negatively skewed.

The data has a proper bell curve.

shapiro.test(A5Q2\(phone) shapiro.test(A5Q2\)sleep)

Variable 1: phone

The variable is normally distributed (p = .96).

Variable 2: sleep

The variable is normally distributed (p = .89).

cor.test( A5Q2\(phone, A5Q2\)sleep, method = “pearson” )

A Pearson correlation was conducted to test the relationship between phone (M = 3.80, SD = 2.66) and sleep (M = 7.56, SD = 1.21).

There was a statistically significant relationship between the two variables, r(148) = -.70, p < .001.

The relationship was negative and strong.

As phone increased, sleep decreased.

```