library(readxl)
library(ggplot2)
DatasetA2 <- read_excel("/Users/srikarthikeya/Downloads/DatasetA2.xlsx")
table(DatasetA2$FavoriteDrink)
## 
## Coffee   Soda    Tea  Water 
##     26     29     28     17
ggplot(DatasetA2, aes(x = FavoriteDrink, fill = FavoriteDrink)) +
  geom_bar() +
  labs(
    x = "FavoriteDrink",
    y = "No. of Students",
    title = "Distribution of FavoriteDrink"
  ) +
  theme(
    text = element_text(size = 14),       
    axis.title = element_text(size = 14),  
    axis.text = element_text(size = 14),  
    plot.title = element_text(size = 14),  
                 
  )

The Observed values for the FavoriteDrink is (Coffee = 26, Soda = 29, tea = 28, water = 17 )

observed <- c(26, 29, 28, 17) 
expected <- c(0.25, 0.25, 0.25, 0.25)
chisq.test(x = observed, p = expected)
## 
##  Chi-squared test for given probabilities
## 
## data:  observed
## X-squared = 3.6, df = 3, p-value = 0.308

P- Value = 0.308 which is greater than 0.05 that means the p-value is statistically insignificant.

Since the p-value is insignificant we don’t have to calculate the Cohen’s W value.

A chi-square goodness-of-fit test indicated that the observed frequencies were different from the expected frequencies, χ²(2) = 3.6, p = 0.308.