library(readxl)

A4Q1 <- read_excel("C:/Users/DeSheila Hatcher/OneDrive/Desktop/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

A Chi-Square Goodness of Fit test was conducted to determine whether there was a difference between the observed ice cream flavor purchases and the expected distribution. The results showed a statistically significant difference between the observed and expected frequencies, χ²(3) = 41.6, p < .001. The effect size was moderate (Cohen’s W = .41). Therefore, the current distribution of ice cream purchases is significantly different from the expected distribution.

Correction for Question 1:

My statistical code and calculations were correct, but I did not include

the written interpretation of the chi-square goodness-of-fit test.

I corrected this by adding an interpretation of the results below.