# Load necessary libraries
library(tidyverse)  
library(ggfortify) # For PCA visualization

# Load a sample dataset 
data(iris)


# Perform a t-test (comparing setosa and versicolor petal lengths)
t_test <- t.test(iris$Petal.Length[iris$Species == "setosa"], 
                 iris$Petal.Length[iris$Species == "versicolor"])
print(t_test)
# Interpretation of t-test results:
# - The p-value indicates whether there is a significant difference in petal lengths
#   between the two species. A p-value < 0.05 suggests a statistically significant difference.
# - The confidence interval provides a range for the true difference in means.



# Chi-squared Test
# Create a contingency table
table_data <- table(iris$Species, iris$Sepal.Length > 5.5)
chisq_test <- chisq.test(table_data)
print(chisq_test)

# Interpretation of Chi-squared results:
# - A significant p-value (< 0.05) indicates an association between 
# the variables.

# ANOVA Test
anova_result <- aov(Sepal.Length ~ Species, data = iris)
summary(anova_result)

# Interpretation of ANOVA results:
# - Tests whether the mean of Sepal.Length differs significantly across species.
# - A significant p-value (< 0.05) suggests at least one group mean is different.