The article introduces a new method called least squares of depth trimmed (LST) residuals regression, which is a robust alternative to traditional least squares regression and competes well with least trimmed squares regression. The LST method is shown to be as robust as least trimmed squares but more efficient, especially in scenarios with specific error characteristics. It can be computed quickly using a new algorithm, making it a practical choice for statisticians in real-world applications.