data/inspiration -> Medium article: "How to Overcome Biases with Data"
In this notebook, we will investigate a simple problem: is the smoking rate higher in the metro area of my hometown (Peoria, Il) or in Boston. The purpose is to see if I can combat my cognitive biases with data and explore the data to determine why I made a incorrect conclusion.
- Boston number of adults = 472582: https://www.bumc.bu.edu/inspir/files/HTML/Boston%20Population%20and%20Demographics.htm
- Peoria number of adults = 83925: https://www.infoplease.com/us/illinois/demographic-statistics-61
- Boston smoking rate = 0.164 (from first source in image)
- Peoria smoking rate = 0.199(from first source in image)
z-score for 95% CI: http://www.stat.yale.edu/Courses/1997-98/101/confint.htm
Difference between two proportions formula: https://onlinecourses.science.psu.edu/stat100/node/57/
This project requires the following Python libraries:
- NumPy
- Matplotlib
You can install these dependencies using pip:
pip install numpy matplotlib
project/
│
├── data/
│ └── dataset.csv
│
├── src/
│ ├── data_loader.py
│ ├── data_processor.py
│ └── visualizer.py
│
├── notebooks/
│ └── analysis.ipynb
│
├── results/
│ └── figures/
│
├── README.md
└── requirements.txt
-
Clone this repository:
git clone https://github.com/yourusername/data-analysis-project.git cd data-analysis-project -
Install the required dependencies:
pip install -r requirements.txt -
Run the analysis:
python src/main.pyAlternatively, you can open and run the Jupyter notebook:
jupyter notebook notebooks/analysis.ipynb
The analysis results, including generated figures, will be saved in the results/ directory.
If you'd like to contribute to this project, please fork the repository and create a pull request with your proposed changes.
This project is license free .