library(readxl)
library(ggpubr)
## Loading required package: ggplot2
library(ggplot2)
A5Q1 <- read_excel("A5Q1.xlsx")
#A5Q1
#colnames(A5Q1)
observed <- table(A5Q1$flavor)
observed
## 
##  Chocolate      Mango Strawberry    Vanilla 
##         87         32         57         74
#We have only one variable "Flavor"
barplot(observed,
        main = "Flavor",
        xlab = "Flavor",
        ylab = "Count",
        col = rainbow(length(observed)))

expected <- c(0.20,0.20,0.20,0.40)

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 [flavor] frequencies and the expected frequencies.

The results showed that there was a significant difference between the observed and expected frequencies, χ²(2) = 41.6, p = .005. The difference was moderate to large, (Cohen’s W = .41).