Welcome to the study of econometrics! If you’re here, it’s likely because you’ve learned some economic theory and are now eager to see how it holds up in the real world. One of the most powerful tools for this task is a statistical technique called Ordinary Least Squares (OLS).

Think of OLS as a high-performance engine designed to find and measure relationships in data. It can tell you how a change in one factor, like price, might affect another, like sales. But for this engine to run perfectly and give you trustworthy results, it needs a specific set of ideal conditions. These conditions are like ensuring it has the right fuel, clean oil, and a smooth, straight road.

This document serves as your guide to these ideal conditions, which are known as the six core assumptions of the Classical Linear Regression Model (CLRM). We’ll explore each one, understand why it’s so essential, and see what they collectively guarantee about the quality of our OLS results.