Gauss-Markov Assumptions › Introduction

Linear regression is one of the most fundamental tools in data science and statistics, but its validity depends on certain assumptions about the data and the underlying relationships. The Gauss-Markov assumptions, named after mathematicians Carl Friedrich Gauss and Andrey Markov, provide the conditions under which OLS estimation produces the most efficient and unbiased results. Understanding these assumptions is essential for proper model interpretation and for knowing when to apply alternative methods when assumptions are violated.