📊 Statistical Inference & Modeling

Welcome to the Statistical Inference & Modeling repository! 🚀
This project is a comprehensive collection of Jupyter Notebooks dedicated to the world of data-driven decision-making, covering everything from fundamental goodness-of-fit tests to advanced predictive modeling and diagnostic techniques.

🌟 What You'll Find Here

This repository is structured as a week-by-week journey through statistical modeling:

• Weeks 1 & 2: Foundations 🧱
  • Goodness of Fit and initial data exploration.
• Week 3: Decisions & Predictions 🔮
  • Hypothesis Testing and Response Prediction.
• Week 4: The Curves of Reality 📈
  • Model Fitting and Non-Linear Regression.
• Week 5 & 6: Complex Relationships 🔗
  • Deep dives into Multiple Linear Regression.
• Week 7: Advanced Diagnostics 🔬
  • Tackling Multicollinearity, VIF, and ANOVA.

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⚡ Deep Dive: Advanced Content

If you're looking for the most sophisticated techniques in this project, check out these notebooks:

🧪 Multicollinearity & Model Diagnostics (Week 7)

Explore how to detect and handle redundant predictors using:

• VIF (Variance Inflation Factor)
• Klein’s Rule of Thumb
• ANOVA Tables for model comparison.

🌀 Non-Linear Regression & Model Fitting (Week 4)

Master the art of fitting data when straight lines aren't enough:

• Advanced diagnostic plots (Residuals vs. Fitted, QQ Plots).
• Outlier detection and handling.
• Logarithmic and polynomial transformations.

📐 Multiple Regression & ANOVA (Week 6)

Learn to model multiple predictors simultaneously and evaluate their combined significance using F-statistics and analysis of variance.

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🛠️ Requirements

• Python 3.12+
• uv installed (recommended for dependency management)

🚀 Getting Started

To set up the environment and install all dependencies:

uv sync

Once installed, you can launch the notebooks using your favorite editor (VS Code, JupyterLab, etc.) and start exploring!