📈 Day 5: Checking and Fixing Violations of Regression Assumptions

Описание: 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.

To try these tips on the Chisquares platform, visit www.chisquares.com