Скачать или смотреть Data science tutorial: Multicollinearity

Multicollinearity can distort regression models, making it harder to interpret coefficients and understand causal relationships. In this hands-on walkthrough, I show how to simulate data, create correlated variables, and evaluate multicollinearity using Python, statsmodels, and scikit-learn.

📌 What You’ll Learn:
✅ How to generate synthetic data for regression models
✅ How to create highly correlated variables (e.g., SAT Math & Grade 8 Math Scores)
✅ How multicollinearity affects regression results
✅ How to split data into train/test and evaluate out-of-sample performance

🔥 Who is this for?

Data scientists & analysts working with regression models
Those interested in causal inference and predictive modeling
Anyone preparing for data science interviews

🔗 More Data Science & Causal Inference Resources:
📚 WhatsTheImpact.com
  / jonathan.interviews  

👀 Watch till the end to see how multicollinearity can impact real-world decision-making! 🚀

#datascience #dataanlysis #datasciencetutorial #datasciencecourse #statisticalmodeling #statisticstutorials #causalinference