library(readxl)

Data <- read_excel("C:/Users/SHRUTI/Downloads/A4Q1.xlsx")
Data

## # A tibble: 250 × 1
##    flavor    
##    <chr>     
##  1 Strawberry
##  2 Vanilla   
##  3 Mango     
##  4 Strawberry
##  5 Chocolate 
##  6 Mango     
##  7 Strawberry
##  8 Strawberry
##  9 Chocolate 
## 10 Vanilla   
## # ℹ 240 more rows

# Create frequency table
observed <- table(Data$flavor)
observed

## 
##  Chocolate      Mango Strawberry    Vanilla 
##         87         32         57         74

barplot(observed,
        main = "Ice Cream Flavor Preferences",
        xlab = "Flavor",
        ylab = "Count",
        col = rainbow(length(observed)))

# Expected proportions (25% for each flavor)
expected <- c(0.25, 0.25, 0.25, 0.25)

# Conduct Chi-Square test
chi_result <- chisq.test(x = observed, p = expected)
chi_result

## 
##  Chi-squared test for given probabilities
## 
## data:  observed
## X-squared = 27.088, df = 3, p-value = 5.642e-06

# Calculate Cohen's W
w <- sqrt(as.numeric(chi_result$statistic) / sum(observed))
w

## [1] 0.3291686

# A Chi-Square Goodness-of-Fit test was conducted to determine if there was a difference between the observed ice cream flavor frequencies and the expected frequencies.
# The results showed that there was a difference between the observed and expected frequencies, χ²(3) = 27.09, p < .001.
# The difference was moderate (Cohen's W = .33).