Скачать или смотреть 📈 Day 5: Checking and Fixing Violations of Regression Assumptions

Welcome back to the series! In today’s session, we dive into one of the most important—and often overlooked—parts of regression analysis: validating model assumptions and correcting issues when those assumptions break down.

🔎 What we cover in this video:

The four core regression assumptions

How to detect violations using diagnostics

Common issues: non-linearity, heteroscedasticity, multicollinearity, and non-normal residuals

Practical tools for checking assumptions in R/Python

Strategies to fix problems

Real-world examples to help you understand what “bad” looks like

🛠️ Why this matters:
Even the best-looking regression results can mislead you if the underlying assumptions aren’t met. Today’s lesson will help you build models that are robust, trustworthy, and ready for real-world data.

📚 Part of the series:
This is Day 5 of our ongoing journey into regression modeling. If you missed earlier videos, check out the playlist to get fully up to speed!

👍 Don’t forget to like, comment, and subscribe if you’re finding this series helpful. And feel free to drop your questions below—we’re learning together.

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