I have a question and I don't really know how to solve it. I tried it and probably my solution is $\textit{wrong}$ to say the least. Here we go!
Assume we have $A_{m \times n}.$ Prove or disprove the statement: Every vector in $\mathbf{R^n}$ is either $C(A)$ or $C(A)^\perp$ or both. Where $C(A)$ is span of column space of $A$ and $C(A)^\perp$ is the orthogonal complement of $A$.
My take: Every matrix say $A$, has the property rank$(A) \leq n$.
So if rank$(A)<n$, then at least one column (vector) of $A$ is linearly dependent and rank$(A) <$ rank $(\mathbf{R^n})$ which implies that $A$ does not span $\mathbf{R^n}$ so that we have $$\text{rank } \mathbf{(R^n)} = \text{ rank} (A) + \text{ rank } (A^\perp) $$ Similarly, if rank$(A)=n$, then all columns (vectors) of $A$ are linearly independent and rank$(A) =$ rank $(\mathbf{R^n})$ which implies that $A$ spans $\mathbf{R^n}$ so that we have $$\text{rank } \mathbf{(R^n)} = \text{ rank} (A) $$