$X$ is a vector $X^T=(X_1, X_2,…,X_p)$).

$\textbf{X}$ is the $N \times (p+1)$ matrix, with each row an input vector with a 1 in the first positions. It is composed by column vectors $\textbf x_0$, $\textbf x_1$, …, $\textbf x_p$.

\textbf{y} is the N-vector of the outputs.

Training data ($x_1,y_1$), …, ($x_N,y_N$), where each $x_i = (x_{i1}, x_{i2},…,x_{ip})^T$.

 $E(Y X)$ is the regression function.