Chi-Square Test - Statistical Approach

This is a simple Python script that demonstrates how to perform a chi-square test using a statistical approach. The script takes two input CSV files: one containing the observed frequencies and one containing the expected frequencies. It then calculates the chi-square statistic and the corresponding p-value, and outputs the results to the console.

Requirements

• Python 3.x
• NumPy
• Pandas

Usage

1. Clone this repository or download the chisquare.py file.
2. Install the required packages using pip: pip install numpy pandas
3. Prepare two CSV files with the observed and expected frequencies. The CSV files should have a single column each, and the same number of rows.
4. Run the script with the following command: python chisquare.py path/to/observed.csv path/to/expected.csv

Example: python chisquare.py data/observed.csv data/expected.csv

License

This code is released under the MIT License. See the LICENSE file for more information.

Acknowledgments

This script was inspired by the following resources:

• Chi-squared Test for Independence in Python by Will Koehrsen
• Using Chi-Square Statistic in Research by Scribbr