library(readxl) 
library(ggpubr)
## Loading required package: ggplot2
DatasetA <- read_excel("C:/Users/Joyce/Downloads/DatasetA.xlsx") 
DatasetB <- read_excel("C:/Users/Joyce/Downloads/DatasetB.xlsx") 
mean(DatasetA$StudyHours) 
## [1] 6.135609
sd(DatasetA$StudyHours)
## [1] 1.369224
mean(DatasetA$ExamScore) 
## [1] 90.06906
sd(DatasetA$ExamScore)
## [1] 6.795224
mean(DatasetB$ScreenTime) 
## [1] 5.063296
sd(DatasetB$ScreenTime)
## [1] 2.056833
mean(DatasetB$SleepingHours) 
## [1] 6.938459
sd(DatasetB$SleepingHours)
## [1] 1.351332
hist(DatasetA$StudyHours,
     main = "Study Hours",
     breaks = 20,
     col = "lightblue",
     border = "white",
     cex.main = 1,
     cex.axis = 1,
     cex.lab = 1)

hist(DatasetA$ExamScore,
     main = "Exam Score",
     breaks = 20,
     col = "lightblue",
     border = "white",
     cex.main = 1,
     cex.axis = 1,
     cex.lab = 1)

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

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

shapiro.test(DatasetA$StudyHours) 
## 
##  Shapiro-Wilk normality test
## 
## data:  DatasetA$StudyHours
## W = 0.99388, p-value = 0.9349
shapiro.test(DatasetA$ExamScore)
## 
##  Shapiro-Wilk normality test
## 
## data:  DatasetA$ExamScore
## W = 0.96286, p-value = 0.006465
shapiro.test(DatasetB$ScreenTime) 
## 
##  Shapiro-Wilk normality test
## 
## data:  DatasetB$ScreenTime
## W = 0.90278, p-value = 1.914e-06
shapiro.test(DatasetB$SleepingHours)
## 
##  Shapiro-Wilk normality test
## 
## data:  DatasetB$SleepingHours
## W = 0.98467, p-value = 0.3004
cor.test(DatasetA$StudyHours, 
         DatasetA$ExamScore,
         method = "spearman")
## Warning in cor.test.default(DatasetA$StudyHours, DatasetA$ExamScore, method =
## "spearman"): Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  DatasetA$StudyHours and DatasetA$ExamScore
## S = 16518, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.9008825
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
ggscatter(
  DatasetA,
  x = "StudyHours",
  y = "ExamScore",
  add = "reg.line",
  xlab = "Study Hours",
  ylab = "Exam Score"
)

ggscatter(
  DatasetB,
  x = "ScreenTime",
  y = "SleepingHours",
  add = "reg.line",
  xlab = "Screen Time",
  ylab = "Sleeping Hours"
)

Study hours (M = 6.14, SD = 1.37) was correlated with the exam score (M = 90.07, SD = 6.80), ρ(98) = .90, p = .001. The relationship was positive and strong. As study hours increased, exam scores increased

Screen time (M = 5.06, SD = 2.06) was correlated with sleeping hours (M = 6.94, SD = 1.35), ρ(98) = -.55, p = .001. The relationship was negative and strong. As screen time increased, sleeping hours decreased.