Do ellipsoids cast ellipsoidal shadows? Given an n-dimensional ellipsoid in $\mathbb{R}^n$, is any orthogonal projection of it to a subspace also an ellipsoid? Here, an ellipsoid is defined as
$$\Delta_{A, c}=\{x\in \Bbb R^n\,:\, x^TAx\le c\}$$
where $A$ is a symmetric positive definite n by n matrix, and $c > 0$.
I'm just thinking about this because it gives a nice visual way to think about least-norm regression.
I note that SVD proves immediately that any linear image (not just an orthogonal projection) of an ellipsoid is also an ellipsoid, however there might be a more geometrically clever proof when the linear map is an orthogonal projection.
 A: Indeed, ellipsoids cast ellipse shape shadows on the ground.
The intersection of any conicoid and a first degree equation plane illumination terminator between two tangential points is a conic section. It can be proved by elimination to the conic second degree equation.

A: There are already a good answers presented, but I want to add the also one may think in a following way :
The orthogonal projection defines some subspace $\langle e_1, e_2 \ldots e_n \rangle$, and we perform an orthogonal transformation $R^{T}$, such that the matrix $A$ transforms into $R^{T} A R$, and in the rotated basis, the first $n-1$ components will correspond to that subspace. After the rotation, matrix $A$ preserves its positive definiteness, and the restriction to the $(n-1) \times (n-1)$ will be positive definite by the Sylvester's critertion. Therefore this block would define an ellipsoid in one dimension lower.
A: Yes they do. You can prove it by induction on the codimension of the subspace you project to. For $x\in Vect(e_1,\ldots e_{n-1})$ there exists $t \in \mathbb{R}$ such that $x+te_n$ belongs to $\Delta$ iff the discriminant of the degree $2$ equation $(x+te_n)^TA(x+te_n)\leq c$ w.r.t. the unknown $t$ is non-negative, which turns out to still be a quadratic inequality in $x$.
A: Yes. An ellipsoid is a linear transformation of a spherical ball, and orthogonal projection is also a linear transformation, so it suffices to show that any linear transformation whose image is a subspace sends a spherical ball to an ellipsoid in that space.
A linear transformation can be decomposed into orthogonal projection by its kernel followed by some invertible linear transformation. Orthogonal projection sends a spherical ball to a spherical ball in the subspace, so we are done.
