installing the Required Packages install.packages(“readxl”) install.packages(“ggplot2”) install.packages(“rcompanion”)

Opening the Installed Packages

library(readxl) 
library(ggplot2) 
library(rcompanion)

loading DatasetB2

DatasetB2 <- read_excel("C:/Users/datta/Downloads/DatasetB2.xlsx")

Creating a Contingency Table

tab <- table(DatasetB2$StudentType, DatasetB2$PetOwnership)
tab 
##                
##                 No Yes
##   Domestic      27  25
##   International 23  25

Creating a Bar Charts

p <- ggplot(DatasetB2, aes(x = StudentType, fill = PetOwnership)) +
  geom_bar(position = "dodge") +                 
  labs(
    x = "StudentType",
    y = "Frequency",
    title = "Pet Ownership by Student Type"
  ) +
  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"                 
  )
print(p)

Conducting Chi-Square Test of Independence

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

Interpret and Report the Results
The Chi-Square Test of Independence indicated there was/ was not a significant association between gender and voting behavior, χ²(1) = 0.040064, p = 0.8414.

P value 0.8414>0.05 so it is not statistically significant so there is no need to calculate Effect size