An awesome package for confidence intervals.
Project description
# Confidence Interval Library
The Confidence Interval Library is a Python package for calculating confidence intervals for various statistical metrics. This library simplifies the process of estimating the confidence intervals around a sample mean, proportion, or other statistical estimates.
## Installation
You can install the Confidence Interval Library directly from PyPI:
```bash
pip install confidence_interval
Features
- Mean Confidence Interval: Calculate the confidence interval for the mean of a dataset.
- Proportion Confidence Interval: (If applicable) Calculate the confidence interval for the proportion of a dataset.
- Custom Confidence Levels: Allows specifying different confidence levels.
Usage
Here is how you can use the Confidence Interval Library:
Calculating Mean Confidence Interval
from confidence_interval import mean_confidence_interval
data = [1, 2, 3, 4, 5]
mean, lower_bound, upper_bound = mean_confidence_interval(data)
print("Mean:", mean)
print("Lower bound:", lower_bound)
print("Upper bound:", upper_bound)
Requirements
- Python 3.6+
- NumPy
- SciPy
Contributing
Contributions are welcome! Please fork the repository and open a pull request with your additions, or open an issue if you find any bugs or have suggestions.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Authors
- Subashanan Nair - Initial work - SubaashNair
Acknowledgments
- To my students
- Logic and Mathematics
- Life
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