Gauss-Markov conditions

The Gauss-Markov conditions are a set of assumptions that ensure the best performance of a specific type of statistical model called ordinary least squares (OLS) regression. These conditions include: the relationship between the dependent and independent variables is linear; the errors (or differences between observed and predicted values) are random and have a mean of zero; the errors are homoscedastic (having constant variance); and the errors are uncorrelated. When these conditions are met, the OLS estimates are considered the most reliable and efficient, meaning they provide the best predictions without unnecessary error.