Gauss-Markov model

The Gauss-Markov model is a statistical framework used to understand relationships between variables. It assumes that there’s a consistent link between inputs and an outcome, with some randomness or noise affecting the results. The model expects that this noise has an average of zero, stays constant across all data points, and isn’t related to the inputs. Under these conditions, the model ensures that the best way to estimate relationships (called linear regression) is to minimize the average squared differences between the predicted and actual values, providing the most reliable estimates given the assumptions.