Exploring Interaction Effects in Linear Regression: A Simulation Study

This project explores how interaction and polynomial terms affect linear regression modeling using a simulated dataset. The study highlights how including higher-order terms influences predictor coefficients and overall model fit. The dataset is generated within the notebook, so no external data is required.

Project Structure

• ols_interaction_analysis.ipynb — Main Jupyter Notebook with code, explanations, and results.
• ols_interaction_analysis.html — Exported HTML version of the notebook.
• ols_interaction_analysis.pdf — Exported PDF version of the notebook.
• figures/ — Contains exported plots mainly for reference; all figures are already embedded in the outputs.

Key Points

• Simulated dataset with continuous predictors.
• Linear regression models with and without interaction and polynomial terms.
• Comparison of model coefficients and overall fit metrics.
• Visualization of regression surfaces and interaction effects.

Requirements

• Python (3.10.16 recommended)
• Jupyter Notebook / Jupyter Lab
• Python packages: pandas, numpy, matplotlib, statsmodels, ISLP

You can install the required packages with:

pip install pandas numpy matplotlib statsmodels ISLP

How to Use

1. Clone or download this repository.
2. Open ols_interaction_analysis.ipynb in Jupyter Notebook or Jupyter Lab.
3. Run all cells to reproduce results, figures, and exported HTML/PDF outputs.