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The least-squares fit $A\vec{x}^*$ is identified with the orthogonal projection of $\vec{b}$ onto $C(A)$ because the goal of least squares is to find the vector in the subspace $C(A)$ that is closest to $\vec{b}$. A fundamental geometric property of subspaces states that the unique closest point in a subspace to an external vector is its orthogonal projection.

Conditions: $C(A)$ is a subspace of $\mathbb{R}^n$; $\vec{b} \in \mathbb{R}^n$; Distance is measured by the Euclidean norm

The least-squares fit $A\vec{x}^*$ is identified with the orthogonal projection of $\vec{b}$ onto $C(A)$ because the goal of least squares is to find the vector in the subspace $C(A)$ that is closest to $\vec{b}$. A fundamental geometric property of subspaces states that the unique closest point in a subspace to an external vector is its orthogonal projection.

Conditions: $C(A)$ is a subspace of $\mathbb{R}^n$; $\vec{b} \in \mathbb{R}^n$; Distance is measured by the Euclidean norm

The orthogonal projection of $y$ onto the line spanned by a nonzero vector $v$ is given by the formula $\frac{y \cdot v}{v \cdot v} v$. This formula scales the vector $v$ by the ratio of the dot product of $y$ and $v$ to the dot product of $v$ with itself.

Conditions: The vector $v$ must be nonzero.; The projection is onto the line spanned by $v$.

The orthogonal projection of $y$ onto the line spanned by a nonzero vector $v$ is given by the formula $\frac{y \cdot v}{v \cdot v} v$. This formula scales the vector $v$ by the ratio of the dot product of $y$ and $v$ to the dot product of $v$ with itself.

Conditions: The vector $v$ must be nonzero.; The projection is onto the line spanned by $v$.