Assignment 4 Q2

library(readxl)
library(ggpubr)

## Loading required package: ggplot2

A4Q2 <- read_excel("A4Q2.xlsx")
ggscatter(
  A4Q2,
  x = "phone",
  y = "sleep",
  add = "reg.line",
  xlab = "phone",
  ylab = "sleep"
)

# The relationship is linear.

# The relationship is negative.

# The relationship is weak.

# There are no outliers

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

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

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

# Variable 1:phone
# The first variable looks normally distributed.
# The data is negative skew.
# The data have no  proper bell curve.


# Variable 2:sleep
# The second variable looks normally distributed.
# The data is positive skew .
# The data has proper bell curve.

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

# Variable 1:phone
# The first variable is normally distributed (p = .64).

# Variable 2:sleep
# The second variable is normally distributed (p = .96).

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

## 
##  Pearson's product-moment correlation
## 
## data:  A4Q2$phone and A4Q2$sleep
## t = -11.813, df = 148, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7708489 -0.6038001
## sample estimates:
##        cor 
## -0.6966497

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

# A Pearson correlation was conducted to test the relationship between a person's age in years (M = 3.27, SD = 2.66) and Variable 2 (M = 7.52, SD = 1.20).
# There was a statistically significant relationship between the two variables, r(148) = .69, p< .2
# The relationship was negative and strong.
# As phone increased, sleep decreased.


# A Spearman correlation was conducted to test the relationship between Variable 1 (Mdn = 35.79) and Variable 2 (Mdn = 14.02 ).
# There was a statistically significant relationship between the two variables, ρ = 908390, p < .2.
# The relationship was negative and strong.
# As phone increased, sleep decreased.