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