Prove that every linear regression predictor is a linear function

A function $$f : \mathbb{R}^D \rightarrow \mathbb{R}$$ is linear if both of the following conditions hold.
(1) For all $$\textbf{x}, \textbf{y} \in\mathbb{R}^D, f(\textbf{x} + \textbf{y}) = f(\textbf{x}) + f(\textbf{y})$$.
(2) For all $$\textbf{x} \in \mathbb{R}^D$$ and $$a \in \mathbb{R}$$, $$f(a\textbf{x}) = af(\textbf{x})$$.
Consider the hypothesis class of linear regression.
That is, any predictor $$y(\textbf{x}) = \textbf{w}^{\text{T}}\textbf{x}$$ with $$\textbf{w} \in \mathbb{R}^D$$
Prove that every linear regression predictor is a linear function.
May use the definition of $$\textbf{w}^{\text{T}}\textbf{x} = \sum_j w_jx_j$$ as well as any basic fact about arithmetic without proof.

$$\textbf{My Attempt:}$$
Let $$\textbf{x}$$, $$\textbf{y}, \textbf{w} \in \mathbb{R}^D$$ and $$a\in\mathbb{R}$$.
For (1): since, $$y(\textbf{x}+\textbf{y})=\textbf{w}^{\text{T}}(\textbf{x}+\textbf{y})=\sum_jw_j(x_j+y_j)=\sum_jw_jx_j+\sum_jw_jy_j=y(\textbf{x})+y(\textbf{y})$$
For (2): since, $$y(a\textbf{x})=\textbf{w}^{\text{T}}(a\textbf{x})=\sum_ja~(w_jx_j)=a~\sum_jw_jx_j=a~y(\textbf{x})$$.
So, every linear regression predictor is a linear function.
$$\textbf{Is that my prove correct ? }$$

The statement itself is so simple, that maybe the person reading your proof (this looks like some assignment) will be extra picky, but I wouldn't worry about that. In this case I would rather make sure I have some intuitive grasp on what does it mean for a function to be linear and how does $$y(x) = w^Tx$$ look like geometrically.