library(readxl)
library(rcompanion)
library(ggplot2)

#Load Data

data2026 <- read_excel("A4Q2.xlsx")

data2026

## # A tibble: 300 × 3
##       ID status   scholarship
##    <dbl> <chr>          <dbl>
##  1     1 Domestic           1
##  2     2 Domestic           0
##  3     3 Domestic           1
##  4     4 Domestic           0
##  5     5 Domestic           0
##  6     6 Domestic           1
##  7     7 Domestic           1
##  8     8 Domestic           0
##  9     9 Domestic           1
## 10    10 Domestic           1
## # ℹ 290 more rows

#Create the Frequency Table

scolar_table <- table(data2026$status, data2026$scholarship)
scolar_table

##                
##                   0   1
##   Domestic       39 111
##   International 118  32

#Create a Bar Graph

barplot(scolar_table,
  xlab = data2026$status,
  ylab = data2026$scholarship,
  beside = TRUE,
  col = rainbow(nrow(scolar_table)),
  legend = rownames(scolar_table),
  main = "Bar Chart")

# A Chi-Square Test of Independence was conducted to determine if there was an association between gender (male or female) and voting behavior (yes or no).

# Conduct the Chi-Square Test of Independence
chi_result <- chisq.test(scolar_table)
chi_result

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  scolar_table
## X-squared = 81.297, df = 1, p-value < 2.2e-16

# The results showed that there was an association between the two variables, data:  scolar_table X-squared = 81.297, df = 1, p-value < 2.2e-16

# Calculate the Cohen's W (Effect Size)
cramer_v <- rcompanion::cramerV(scolar_table)
cramer_v

## Cramer V 
##   0.5272

# The association was moderate (Cramer's V = 0.5272 ).