Show $J(I-H) = 0$ This is in multiple linear regression. Given $m, n \in \mathbb{N}$ and matrices $X \in \mathbb{R}^{m \times (n+1)} (m > n + 1), H = X(X'X)^{-1}X' \in \mathbb{R}^{m\times m}$, the hat matrix, $, I = I_m$ and $J \in \mathbb{R}^{m \times m}$, a matrix of 1's, how does one show that
$J(I-H) = 0$?
I am actually trying to show
$(I-H)(H-\frac{1}{n}J) = 0$
which reduces to $J(I-H) = 0$ by noting that $HH =H$.
Apparently, this might have something to do with $J(y - X \hat{\beta}) = 0 \ \forall y \in \mathbb{R}^{1 \times (m+1)}$ where  $\hat{\beta} = (X'X)^{-1}X'y $?
Is the statement true? If so why? If not, why not, and how then to prove $J(I-H) = 0$ or $(I-H)(H-\frac{1}{n}J) = 0$? I think there's some property of MLR needed.
Is there a way to do it without statistics? Like $J(I-H) = 0$ or $(I-H)(H-\frac{1}{n}J) = 0$ from just the properties of the matrices? Must one really use some properties of MLR? What are some possible counterexamples?
 A: The "hat" matrix is a projection matrix onto the column space of $X$, $C(X)$. For any $x \in C(X)$, $Hx=x$ (see properties of projection matrix). Therefore $JH=J$ as long as you have an intercept (column of ones in your design matrix).
EDIT to address comment:
The matrix of all ones, $J$, can be written as $$J = \begin{pmatrix} \mathbf{1}^\intercal \\ \vdots \\\mathbf{1}^\intercal\end{pmatrix}$$ where $\mathbf{1}$ is a vector of ones. Then we have $$JH = \begin{pmatrix} \mathbf{1}^\intercal H \\ \vdots \\ \mathbf{1}^\intercal H \end{pmatrix} = \begin{pmatrix} \mathbf{1}^\intercal \\ \vdots \\ \mathbf{1}^\intercal\end{pmatrix} = J$$
if column of ones is in $\mathbf{X}$ then $H\mathbf{1} = \mathbf{1}$ and taking transpose of both sides we have $\mathbf{1}^\intercal H = \mathbf{1}^\intercal$ because $H$ is symmetric. 
A: This can be proved with just the properties of the matrices, since the MLR is already baked into $H$.
If $H$ is defined as $H:=X(X'X)^{-1}X'$ then clearly $HX=X$. Note that $J=11^T$, where $1$ is a column of ones. Suppose that $1$ lives in the $k$th column of $X$, which I'll write as $(X)_{\ast k}=1$.
Since the $k$th column of $HX$ equals $H$ times the $k$th column of $X$, we can show $H1=1$:
$$H1=H(X)_{\ast k}=(HX)_{\ast k}=(X)_{\ast k}=1$$
and therefore $$HJ=H11^T=(H1)1^T=11^T=J.$$
A: Going for the shortest answer here: just look at the first row of $\mathbf{X}^\intercal \mathbf{H} = \mathbf{X}^\intercal$.
A: $J(H-I) = 0$ is another way to say $JH = J$.
If for a vector of only ones, let's write it as $\mathbf{1}$, you get $\mathbf{1}H = \mathbf{1}$ then it also works for $J$ as well.
This property $\mathbf{1}H = \mathbf{1}$ is actually only true when the matrix $X$ contains the vector $\mathbf{1}$ in it's column space, for instance when it contains an intercept term (but an intercept is not necessary).
Counter example. Let $X = \lbrace -1, 0, -1\rbrace$ then $$H = \begin{bmatrix} 0.5 & 0 & -0.5 \\ 0 & 0 & 0 \\ -0.5 & 0 & 0.5 \end{bmatrix}$$
and $\mathbf{1} H = \lbrace 0,0, 0 \rbrace \neq \mathbf{1}$
