library(readxl)
A4Q1 <- read_excel("C:/Users/peter/Desktop/SLU/AA 5221 Applied Analytics & Methods/Week 4/A4Q1.xlsx")
observed <- table(A4Q1$flavor)
observed

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

barplot(observed,
        main = "Flavor",
        xlab = "Flavor",
        ylab = "Frequency",
        col = rainbow(length(observed)))

expected <- c(.20, .20, .20, .40)
chi_result <- chisq.test(x = observed, p = expected)
chi_result

## 
##  Chi-squared test for given probabilities
## 
## data:  observed
## X-squared = 41.6, df = 3, p-value = 4.878e-09

w <- sqrt(as.numeric(chi_result$statistic) / sum(observed))
w

## [1] 0.4079216

# A Chi-Square Goodness-of-Fit test was conducted to determine if there was a difference between the observed [categorical variable] frequencies and the expected frequencies.
# The results showed that there was a difference between the observed and expected frequencies, χ²(3) = 41.6, p = 4.9e-09.
# The difference was moderate (Cohen's W = .41).