Answer
$2$
Work Step by Step
Residuals and Sum-of-Squares Error (SSE)
If we model a collection of data $(x_{1}, y_{1})$ , . , $(x_{n}, y_{n})$ with a linear equation $\hat{y}=mx+b$,
then the residuals are the $n$ quantities (Observed Value-Predicted Value):
$(y_{1}-\hat{y}_{1}), (y_{2}-\hat{y}_{2}), \ldots, (y_{n}-\hat{y}_{n})$ .
The sum-of-squares error (SSE) is the sum of the squares of the residuals:
SSE $=(y_{1}-\hat{y}_{1})^{2}+(y_{2}-\hat{y}_{2})^{2}+\cdots+(y_{n}-\hat{y}_{n})^{2}.$
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Build a table, column by column
\begin{array}{c|c|c|c|c|cc}
& x & y & y'=x+1 & (y-y') & (y-y') ^ 2 \\
\hline & 0 & 1 & 1 & 0 & 0 \\
& 1 & 1 & 2 & -1 & 1 \\
& 2 & 2 & 3 & -1 & 1 \\
\hline & & & & {\bf SSE}= & {\bf 2} \\\hline
\end{array}
