ANOVA in R Programming

Introduction

ANOVA helps us check if different groups have different average values. It tells us if the differences we see are real or just by chance.

You want to compare the average test scores of students from three different schools.

You want to see if different fertilizers affect plant growth differently.

You want to check if three types of diets lead to different average weight loss.

You want to compare customer satisfaction ratings across multiple stores.

Syntax

R Programming

result <- aov(response_variable ~ group_variable, data = your_data)
summary(result)

response_variable is what you measure (like height or score).

group_variable is the category you compare (like school or diet).

Examples

This checks if different diets cause different average weights.

R Programming

result <- aov(weight ~ diet, data = my_data)
summary(result)

This compares average scores between schools.

R Programming

result <- aov(score ~ school, data = test_scores)
summary(result)

Sample Program

This program creates a small dataset with weights for three diets. Then it runs ANOVA to see if the average weight differs by diet.

R Programming

# Create example data
my_data <- data.frame(
  diet = factor(c('A', 'A', 'B', 'B', 'C', 'C')),
  weight = c(5, 6, 7, 8, 5, 7)
)

# Perform ANOVA
result <- aov(weight ~ diet, data = my_data)

# Show summary
summary(result)

Important Notes

ANOVA assumes the groups have similar spread (variance).

If ANOVA shows a difference, you can do more tests to find which groups differ.

Summary

ANOVA tests if group averages are different.

Use aov() in R to run ANOVA.

Look at the summary to see if differences are significant.