最小二乘回归算法

Every invertible $n\times n$ matrix $A$ can be written as $A=QR$ where $Q$ is orthogonal ($Q^T Q=I$) and $R$ is upper triangular.
One constructs $Q$ by applying Gram-Schmidt to the columns of $A,$ and $R$ arises from the combination coefficients.

Least squares: If $X$ is $n\times p,$ we can do $X=QR$ (where $Q$ is $n\times p,$ $R$ is $p\times p$) to solve $X\beta \approx y$. Then $R\beta = Q^T y.$

Question: Give an algorithm for linear least squares regression.