Question 1 A2 Isabel Tillett

library(readxl)
library(ggpubr)

## Loading required package: ggplot2

library(rmarkdown)
#Correction: I added the rmarkdown package.
A5Q1_1 <- read_excel("~/Downloads/A5Q1-1.xlsx")
#Correction: I fixed the excel file path so that the file could be located when knitting.
#create observed table
observed <- table(A5Q1_1$flavor)
observed

## 
##  Chocolate      Mango Strawberry    Vanilla 
##         87         32         57         74

#create barchart of observed data
barplot(observed,
        main = "Ice Cream Purchases",
        xlab = "Flavor",
        ylab = "Count",
        col = rainbow(length(observed)))

#Correction: I changed the bar chart title from "Flavor" to "Ice Cream Purchases" to match the research scenario.

#create expected scores
expected <- c(.20, .20, .20, .40)
expected

## [1] 0.2 0.2 0.2 0.4

#conduct chi-square goodness of fit test
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

#calculate effect size
w <- sqrt(as.numeric(chi_result$statistic) / sum(observed))
w

## [1] 0.4079216

#Interpretation
#A Chi-Square Goodness of Fit test was conducted to determine if there was a difference between the observed ice cream flavor frequencies and the expected frequencies.
#The results showed that there was a significant difference between the observed and expected frequencies, χ²(3) = 41.6, p < .001.
#The difference was moderate, (Cohen's W = .41).
#Correction: I revised the interpretation wording and rounding.
#Correction: I added section comments to label each step of the analysis.