library(readxl)
library(ggplot2)
library(rcompanion)
DatasetA2 <- read_excel("/Users/mbongenimoyo/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 = "Frequency",
    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), 
    legend.position = "none"             
  )

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

chi-square goodness-of-fit test indicated that the observed frequencies were different from the expected frequencies, χ²(3) = 3.6, df =1, p-value=0.308.

library(readxl)
library(ggplot2)
library(rcompanion)
DatasetB2 <- read_excel("/Users/mbongenimoyo/Downloads/DatasetB2.xlsx")
tab <- table(DatasetB2$StudentType, DatasetB2$PetOwnership)
ggplot(DatasetB2, aes(x =StudentType, fill = PetOwnership)) +
  geom_bar(position = "dodge") +               
  labs(
    x = "StudentType",
    y = "Frequency",
    title = "How many students own pets"
  ) +
  theme(
    text = element_text(size = 14),
    axis.title = element_text(size = 14),
    axis.text = element_text(size = 14),
    plot.title = element_text(size = 14),
    legend.position = "none"                 
  ) 

chisq.test(tab)
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  tab
## X-squared = 0.040064, df = 1, p-value = 0.8414

The Chi-Square Test of Independence indicated there was no a significant association between StudentType and PetOwnership, χ²(1) = 0.040064, df = 1, p-value = 0.8414.The p was not statistcally significant so there was no calcultaion of effect size.

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