library(readxl)
A4Q1 <- read_excel("C:/Assignment 4/A4Q1.xlsx")
DatasetName <-
  read_excel("C:/Assignment 4/A4Q1.xlsx")
table(A4Q1$flavor)

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

observed <- table(A4Q1$flavor)
observed

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

barplot(observed,
        main = "flavor",
        xlab = "flavor",
        ylab = "Count",
        col = rainbow(length(observed)))

expected <- c(.348, .296, .228, .128)
chi_result <- chisq.test(x = observed, p = expected)
chi_result

## 
##  Chi-squared test for given probabilities
## 
## data:  observed
## X-squared = 78.963, df = 3, p-value < 2.2e-16

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

## [1] 0.5620065

# A Chi-Square Goodness-of-Fit test was conducted to determine if there was a difference between the observed 
# flavor count and the expected flavor count.

# The results showed that there was a difference between the observed and expected frequencies, 
# χ²(3) = 78.96, p-value < 2.2e-16 which is 0.00000000000000022.

# The difference was large (Cohen's W = .56).