\begin{tabular}{|r|r|r|r|r|r|r|}
\hline$x$ & 1 & 2 & 3 & 4 & 5 & 6 \\
\hline$y$ & 1218 & 1863 & 2878 & 4624 & 6933 & 10597 \\
\hline
\end{tabular}
Use exponential regression to find an exponential function that best fits this data.
\[
f(x)=
\]
Use linear regression to find an linear function that best fits this data.
\[
g(x)=
\]
Of these two, which equation best fits the data?
Linear
Exponential

Step 1 ：First, we need to find the exponential function that best fits the data. To do this, we'll use the formula: \(f(x) = ab^x\) where a and b are constants.

Step 2 ：Next, we'll find the linear function that best fits the data. To do this, we'll use the formula: \(g(x) = mx + c\) where m is the slope and c is the y-intercept.

Step 3 ：Using the given data points and exponential regression, we find the exponential function: \(f(x) = 787.24 * 1.545^x\)

Step 4 ：Using the given data points and linear regression, we find the linear function: \(g(x) = 1824.31x - 1699.6\)

Step 5 ：Comparing the residuals of the two functions, we find that the exponential function has a lower residual value.

Step 6 ：\(\boxed{\text{The equation that best fits the data is the Exponential function: } f(x) = 787.24 * 1.545^x}\)