According to the Gauss-Markov theorem, $\boldsymbol{\hat \beta}$ is the "best" linear unbiased estimator ([[BLUE]]) of $\beta$. The [[least squares estimator]] has the lowest variance among all unbiased estimators of
$\beta$, given that the five Gauss-Markov assumptions are met:
1. Linearity
2. Random: data are a random sample from the population
3. Non-collinearity
4. Exogeneity: the regressors aren't correlated with the error term
5. Homoscedasticity
> [!Tip]- Additional Resources
> [Statistics How To - Gauss Markov Theorem & Assumptions](https://www.statisticshowto.com/gauss-markov-theorem-assumptions/)