library(readxl)
library(rcompanion)
library(ggplot2)
Data <- read_excel("C:/Users/SHRUTI/Downloads/A4Q2.xlsx")
Data

## # 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 frequency table with TWO variables
contingency_table <- table(Data$status, Data$scholarship)
contingency_table

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

barplot(contingency_table,
        beside = TRUE,
        col = rainbow(nrow(contingency_table)),
        legend = rownames(contingency_table),
        main = "Scholarship Status by Student Status",
        xlab = "Scholarship",
        ylab = "Count")

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

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

# Calculate Cramer's V
cramers_v <- rcompanion::cramerV(contingency_table)
cramers_v

## Cramer V 
##   0.5272

# A Chi-Square Test of Independence was conducted to determine if there was an association between student status (Domestic or International) and scholarship status (Yes or No).
# The results showed that there was an association between the two variables, χ²(1) = 81.30, p < .001.
# The association was large (Cramer's V = .53).