Overview

This notebook demonstrates how to perform a Chi-Square test to determine if there is a significant association between two categorical variables. The analysis will include data loading, exploration, and visualization of results.

Table of Contents

• Installation
• Loading Data
• Exploratory Data Analysis (EDA)
• Performing the Chi-Square Test
• Results and Interpretation
• Visualizations
• Conclusion

Installation

Make sure you have the required libraries installed. You can install them using pip: pip install pandas numpy scipy matplotlib seaborn

Results and Interpretation

• If the p-value is less than the significance level (commonly 0.05), we reject the null hypothesis and conclude that there is a significant association between the two categorical variables.
• If the p-value is greater than the significance level, we fail to reject the null hypothesis.

Conclusion In this notebook, I performed a Chi-Square test to analyze the association between two categorical variables. The results indicated whether or not there is a significant relationship between the variables.

How to Use This Template

1. Create a New Jupyter Notebook Open Jupyter Notebook and create a new notebook.
2. Copy the Markdown and Code: Copy the above content into your new notebook, separating the markdown cells from the code cells.
3. Run the Cells: Execute the cells to perform the analysis.

Summary

This structured approach provides a comprehensive guide to performing a Chi-Square test in a Jupyter Notebook. You can modify the code snippets to fit your specific dataset and analysis needs. If you have any further questions or need additional assistance, feel free to ask!