Beer’s Law Calibration Curve (Python Data Analysis)

This project uses Python to construct a Beer’s Law calibration curve and perform linear regression to determine the linear relationship between absorbance and dye concentration.

Tools Used

• Python
• Pandas
• NumPy
• Matplotlib
• Jupyter Notebook

Methods

Synthetic calibration data was loaded into a Pandas DataFrame and plotted using Matplotlib. A linear regression model was applied using NumPy to estimate the slope (m), intercept (b), and coefficient of determination (R²).

Results

The model produced a strong linear relationship (R² > 0.99), consistent with Beer’s Law. A high-resolution calibration curve image was generated for reporting.

Files Included

• beers_law_project.ipynb — main analysis notebook
• beers_law_calibration_curve.png — final plot
• Beers_Law_Python_Project_Stader_Powers.pdf — polished project PDF
• beers_law_data.csv (optional)

Author

Stader Powers General Engineering, University of Mississippi