<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Regression on Ahmed Azeez | Portfolio</title><link>https://ahmed-azeez.github.io/tags/regression/</link><description>Recent content in Regression on Ahmed Azeez | Portfolio</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sun, 19 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ahmed-azeez.github.io/tags/regression/index.xml" rel="self" type="application/rss+xml"/><item><title>Multicollinearity Explained: Why Correlated Predictors Confuse Your Model</title><link>https://ahmed-azeez.github.io/2026/07/19/multicollinearity-explained/</link><pubDate>Sun, 19 Jul 2026 00:00:00 +0000</pubDate><guid>https://ahmed-azeez.github.io/2026/07/19/multicollinearity-explained/</guid><description>Multicollinearity occurs when two or more independent variables in a regression model are highly correlated with one another. At first glance this may not seem like a problem — after all, if two variables are related, shouldn't they both help explain the outcome? The issue is that the model struggles to tell which variable is actually responsible for the observed association.
The Problem: Two Variables, One Signal Imagine trying to determine whether income or wealth has a stronger relationship with a health outcome.</description></item></channel></rss>