library(readxl)
library(ggpubr)
## Loading required package: ggplot2
A5Q1 <- read_excel("C:\\Users\\Tharu\\Downloads\\A5Q1.xlsx")
observed <- table(A5Q1$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
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 ice cream flavor purchase frequencies and the expected frequencies (20% chocolate, 20% strawberry, 20% mango, and 40% vanilla). #The results showed that there was a significant difference between the observed and expected frequencies, χ²(3) = 44.90, p < .001. #The difference was moderate (Cohen’s W = .42).