Answer
$6$
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|ccc}
x & y & y'=x-1 & (y-y') & (y-y')^2. \\
\hline 1 & 1 & 0 & 1 & 1 \\
2 & 2 & 1 & 1 & 1 \\
3 & 4 & 2 & 2 & 4 \\
\hline & & & SSE= & 6 \\ \hline
\end{array}
