Answer
$E=0.16S+0.2$
Work Step by Step
The regression line is:
$y=mx+b$,
where $m$ and $b$ are computed as follows.
$m=\displaystyle \frac{n(\sum xy)-(\sum x)(\sum y)}{n(\sum x^{2})-(\sum x)^{2}}\qquad b=\frac{\sum y-m(\sum x)}{n},$
$n=$ number of data points.
$\left[\begin{array}{lllll}
& S & E & SE & S^{2}\\
& & & & \\
\hline & 10 & 2 & 20 & 100\\
& 15 & 2.5 & 37.5 & 225\\
& 20 & 3 & 60 & 400\\
& 25 & 4.5 & 112.5 & 625\\
\hline & & & & \\
\sum & 70 & 12 & 230 & 1350\\
& & & &
\end{array}\right]$
$m=\displaystyle \frac{4(230)-(70)(12)}{4(1350)-(70)^{2}} =0.16$
$b=\displaystyle \frac{12-0.16(70)}{4}=0.2$
$E=0.16S+0.2$
