You are given a dataset that describes the relationship between the temperature $($in degrees Celsius$)$ and the rate of a chemical reaction $($in arbitrary units$).$ The dataset consists of the following pairs of values:

$\backslash$table$[[$Temperature $(C),0,10,20,30,40,50,60,70,80],[$Reaction Rate $($units$),0\mathrm{.}1,0\mathrm{.}5,1\mathrm{.}5,3,5,6\mathrm{.}5,7,7\mathrm{.}5,7\mathrm{.}8]]$

Fit a power model of the form $y=a*x^b$ to the data, where $y$ is the reaction rate, and $x$ is the temperature.

Fit a saturation model of the form $y=L*(1-e^{-kx})$ to the data, where $L$ is the maximum reaction rate, and $k$ a rate constant.

Calculate the sum of squared residuals for both models.

Compare the goodness$-$of$-$fit for each model by computing the coefficient of determination $r^2$ for both fits.

Based on the $S_r$ and $r^2$ values, determine which model provides a better fit for the data. Explain your reasoning.