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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.
$\textbf{Is that my prove correct ? }$

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Yes, your proof is 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.

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