Answer
$\mathrm{S}\mathrm{S}\mathrm{E}=80.04$
(a) gives the better fit
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 $=(y_{1}-\hat{y}_{1})^{2}+(y_{2}-\hat{y}_{2})^{2}+\cdots+(y_{n}-\hat{y}_{n})^{2}.$
The model with smaller SSE gives the better fit.
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(a)
Build a table,(the table below was generated in Excel)
\begin{array}{|cc|c|c|c|cc|}
\hline & x & y & y'=-0.1x+7 & (y-y') & (y-y') ^2 \\
\hline & 2 & 4 & 6.8 & -2.8 & 7.84 \\
& 6 & 8 & 6.4 & 1.6 & 2.56 \\
& 8 & 12 & 6.2 & 5.8 & 33.64 \\
& 10 & 0 & 6 & -6 & 36 \\
\hline & & & & {\bf SSE}= & {\bf 80.04} \\\hline
\end{array}
When we solve (b) we will be able to tell which model fits better.
