’’’{r}
install.packages(“readxl”) library(readxl) table(A4Q1\(flavor)
observed <- table(A4Q1\)flavor) observed
barplot(observed, main = “Bar Plot”, xlab = “Flavors”, ylab =
“Count”, col = rainbow(length(observed)))
expected <- c(0.25, 0.25, 0.25, 0.25)
chi_result <- chisq.test(x = observed, p = expected)
chi_result
w <- sqrt(as.numeric(chi_result$statistic) / sum(observed)) w
The observed counts for the four flavors were:
Chocolate = 87
Mango = 32
Strawberry = 57
Vanilla = 74
The expected proportion for each flavor was 25%.
A chi-square goodness-of-fit test was conducted to determine
whether the observed flavor frequencies differed from the
expected equal distribution.
Chi-square statistic = [enter value from chi_result]
Degrees of freedom = [enter value from chi_result]
p-value = [enter value from chi_result]
Since the p-value is [less than / greater than] 0.05,
we [reject / fail to reject] the null hypothesis.
Therefore, there is [sufficient / insufficient] evidence to
conclude
that the distribution of flavors differs from an equal 25%
distribution.
Effect size (Cohen’s w) = [enter value from w]
’’’