Import Datasets

library(readxl)
DatasetB <- read_excel("D:/SLU/AdvAppliedAnalytics/DatasetB.xlsx")

Calculate mean and standard deviation

  1. Screen Time - Independent Variable
mean(DatasetB$ScreenTime)
## [1] 5.063296
sd(DatasetB$ScreenTime)
## [1] 2.056833
  1. Sleeping Hours - Dependent Variable
mean(DatasetB$SleepingHours)
## [1] 6.938459
sd(DatasetB$SleepingHours)
## [1] 1.351332

Create Histograms for IV - Screen Time

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

The variable “Screen Time” appears positively skewed (most data is in the left). The data also appears to have a proper bell curve.

Create Histograms for DV - Sleeping Hours

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

The variable “sleeping hours” appears normally distributed. The data looks symmetrical (most data is in the middle). The data also appears to have a proper bell curve.

Conduct Shapiro–Wilk tests for to check the normality of each variable

  1. ScreenTime
shapiro.test(DatasetB$ScreenTime) 
## 
##  Shapiro-Wilk normality test
## 
## data:  DatasetB$ScreenTime
## W = 0.90278, p-value = 1.914e-06

The Shaprio-Wilk p-value for ScreenTime normality test is less than 0.05, so the data is not normal

  1. SleepingHours
shapiro.test(DatasetB$SleepingHours)
## 
##  Shapiro-Wilk normality test
## 
## data:  DatasetB$SleepingHours
## W = 0.98467, p-value = 0.3004

The Shaprio-Wilk p-value for SleepingHours normality test is greater than 0.05 (0.3), so the data is normal

Correlation Analysis

cor.test(DatasetB$ScreenTime, DatasetB$SleepingHours, method = "spearman")
## 
##  Spearman's rank correlation rho
## 
## data:  DatasetB$ScreenTime and DatasetB$SleepingHours
## S = 259052, p-value = 3.521e-09
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.5544674

The Spearman Correlation test was selected because one of the variables was abnormally distributed according to the histograms and the Shapiro-Wilk tests. The p-value (probability value) is 3.521e-09, which is below 0.05. This means the results are statistically significant. The alternate hypothesis is supported. The rho-value is -0.5544674 The correlation is negative, which means as ScreenTime increases the SleepingHours decreases. The correlation value is greater -0.50, which means the relationship is strong.

Scatterplots

library(ggpubr)
## Loading required package: ggplot2
ggscatter(
  DatasetB,
  x = "ScreenTime",
  y = "SleepingHours",
  add = "reg.line",
  xlab = "Independent Variable",
  ylab = "Dependent Variable"
)

A Spearman correlation analysis was conducted to examine the relationship between Screen Time and Sleeping Hours The independent variable is Screen Time had a mean of 5.06 and a standard deviation of 2.06 The dependent variable is Sleeping Hours had mean of 6.94 and a standard deviation of 1.35 Correlation coefficient rho = -0.55, p-value = 3.521e-09 which is less than 0.05 The relationship was negative and strong. As Screen Time increased the Sleeping Time decreased.

Results Report

Screen Time (M = 5.06, SD = 2.06) was correlated with Sleeping Hours (M = 6.94, SD = 1.35), ρ = -0.55, p < 0.001 The relationship was negative and strong. As the screen time increased the hours sleeping decreased.