Assgn4Qn2

Import Library

library("ggpubr")

## Loading required package: ggplot2

library("readxl")

Import data A4Q2

A4Q2 <- read_excel("A4Q2.xlsx")

Scatter plot data A4Q2

ggscatter(
  A4Q2, 
  title = "Phone use (hours) vs Sleep (hours)",
  x="phone",
  y= "sleep",
  add="reg.line",
  xlab="sleep (hours)",
  ylab="phone (hours)"
)

## Comments on scatter plot The relationship is linear. The is negative relationship between variables. The relationship is strong between variables. There are significant outliers.

Descriptive statistics

mean(A4Q2$phone)

## [1] 3.804609

sd(A4Q2$phone)

## [1] 2.661866

median(A4Q2$phone)

## [1] 3.270839

mean(A4Q2$sleep)

## [1] 7.559076

sd(A4Q2$sleep)

## [1] 1.208797

median(A4Q2$sleep)

## [1] 7.524099

Phone histogram

hist(A4Q2$phone,
     main = "phone",
     breaks = 20,
     col = "blue",
     border = "white",
     cex.main = 1,
     cex.axis = 1,
     cex.lab = 1)

## Sleep histrogram

hist(A4Q2$sleep,
     main = "sleep",
     breaks = 20,
     col = "green",
     border = "white",
     cex.main = 1,
     cex.axis = 1,
     cex.lab = 1)

## Obervations Variable 1: phone The first variable looks normally distributed. The data is skewed left (positive skew). The data has a proper bell curve.

Variable 2: sleep The second variable looks normally distributed. The data is skewed right (negative skew). The data has a proper bell curve.

Shapiro test

shapiro.test(A4Q2$phone)

## 
##  Shapiro-Wilk normality test
## 
## data:  A4Q2$phone
## W = 0.89755, p-value = 9.641e-09

shapiro.test(A4Q2$sleep)

## 
##  Shapiro-Wilk normality test
## 
## data:  A4Q2$sleep
## W = 0.91407, p-value = 8.964e-08

Shapiro test observations

Variable 1: phone The first variable is abnormally distributed (p <= .005).

Variable 2: USD The second variable is abnormally distributed (p <= .005).

Correlations Test- Spearman’s

cor.test(A4Q2$phone, A4Q2$sleep, method = "spearman")

## 
##  Spearman's rank correlation rho
## 
## data:  A4Q2$phone and A4Q2$sleep
## S = 908390, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.6149873

Spearman’s Observations

A Spearmans correlation was conducted to test the relationship between a person’s phone in years (Mdn = 3.27) and sleep (Mdn = 7.525). There was a statistically significant relationship between the two variables, rho = -0.614, p = >.001. The relationship was negative and strong and strong. As phone use increased, level of sleep decreased.