Opening library

library(readxl)
library(ggpubr)
## Loading required package: ggplot2

load dataset

D1 <- read_excel("A5Q1.xlsx")

Observed categories and importing as dataframe

observed <- table(D1$flavor)
observed
## 
##  Chocolate      Mango Strawberry    Vanilla 
##         87         32         57         74
df <- as.data.frame(observed)
names(df) <- c("flavor","count")
print(df)
##       flavor count
## 1  Chocolate    87
## 2      Mango    32
## 3 Strawberry    57
## 4    Vanilla    74

Barplot

ggbarplot(df, 
          x = "flavor", 
          y= "count", 
          fill = "flavor" )

Defining expected data

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

chi squared test goodness fit

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

cohen effect size

w <- sqrt(as.numeric(chi_result$statistic) / sum(observed))
w
## [1] 0.4079216

observation

A Chi-Square Goodness of Fit test was conducted to determine if there was a difference between the observed [flavour] frequencies and the expected frequencies.

The results showed that there [was] a difference between the observed and expected frequencies, χ²(2) = 41.6, p < .001.

The difference was moderate, (Cohen’s W = .408).