library(readxl)

A4Q1 <- read_excel("A4Q1.xlsx")

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(.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

observed <- table(A4Q1$flavor) observed

barplot( observed, main = “Flavor”, xlab = “Flavor”, ylab = “Count”, col = rainbow(length(observed)) )

expected <- c(.20, .20, .20, .40)

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

chi_result

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

w ```

A Chi-square Goodness-of-Fit test was conducted to determine whether there was a difference between the observed flavor frequencies and the expected frequencies.

The results showed a statistically significant difference between the observed and expected frequencies, \(\chi^2(3) = 41.60\), \(p < .001\).

The difference was moderate in magnitude, Cohen’s \(w = .41\).