The least-squares estimate is a method of estimating the unknown parameters of a linear regression model. It minimizes the sum of squared residuals, resulting in the best fit of the data. The least-squares estimate has several desirable properties, including unbiasedness, consistency, and efficiency. Unbiasedness means that the estimated parameters are not systematically biased in any direction. Consistency means that the estimated parameters converge to the true parameters as the sample size increases. Efficiency means that the least-squares estimate has the smallest variance among all unbiased estimators.
